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68.
Feli, Mohammad; Kazemi, Kianoosh; Azimi, Iman; Wang, Yuning; Rahmani, Amir; Liljeberg, Pasi
2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM'23), IEEE, 2023.
Abstract | Links | BibTeX | Tags:
@conference{nokey,
title = {End-to-End PPG Processing Pipeline for Wearables: From Quality Assessment and Motion Artifacts Removal to HR/HRV Feature Extraction},
author = {Mohammad Feli and Kianoosh Kazemi and Iman Azimi and Yuning Wang and Amir Rahmani and Pasi Liljeberg},
url = {https://futurehealth.uci.edu/wp-content/uploads/2023/12/End-to-End-PPG-Processing-Pipeline-for-Wearables-From-Quality-Assessment-and-Motion-Artifacts-Removal-to-HRHRV-Feature-Extraction.pdf},
year = {2023},
date = {2023-12-08},
urldate = {2023-12-08},
booktitle = {2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM'23)},
publisher = {IEEE},
abstract = {The rapid development of wearable technology has enabled remote photoplethysmography (PPG)-based health monitoring in everyday settings, offering real-time and continuous monitoring of cardiovascular parameters, such as heart rate (HR) and heart rate variability (HRV). However, PPG signals collected in daily life are prone to artifacts and noise, posing challenges to HR and HRV extraction. The existing HR and HRV extraction methods cannot effectively handle noisy PPG signals and ensure accurate results. Additionally, current Python packages were primarily designed for analyzing ``clean" PPG signals, limiting their performance in handling artifacts and noise and resulting in unreliable HR and HRV measurements. In this paper, we propose a robust end-to-end PPG processing pipeline to reliably extract HR and HRV from PPG signals collected in free-living settings. The pipeline comprises three machine learning-based PPG analysis methods: signal quality assessment, reconstruction of noisy signal, and systolic peak detection. We assess the proposed PPG pipeline using a dataset including PPG and Electrocardiogram (ECG) signals recorded from 46 individuals by smartwatches. Our evaluation demonstrates the proposed pipeline's superior performance compared to two established benchmark methods in terms of correlation and mean absolute error with ECG as the reference. We also provide the Python implementation of our pipeline for the research community to facilitate integration into their solutions.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
The rapid development of wearable technology has enabled remote photoplethysmography (PPG)-based health monitoring in everyday settings, offering real-time and continuous monitoring of cardiovascular parameters, such as heart rate (HR) and heart rate variability (HRV). However, PPG signals collected in daily life are prone to artifacts and noise, posing challenges to HR and HRV extraction. The existing HR and HRV extraction methods cannot effectively handle noisy PPG signals and ensure accurate results. Additionally, current Python packages were primarily designed for analyzing ``clean" PPG signals, limiting their performance in handling artifacts and noise and resulting in unreliable HR and HRV measurements. In this paper, we propose a robust end-to-end PPG processing pipeline to reliably extract HR and HRV from PPG signals collected in free-living settings. The pipeline comprises three machine learning-based PPG analysis methods: signal quality assessment, reconstruction of noisy signal, and systolic peak detection. We assess the proposed PPG pipeline using a dataset including PPG and Electrocardiogram (ECG) signals recorded from 46 individuals by smartwatches. Our evaluation demonstrates the proposed pipeline's superior performance compared to two established benchmark methods in terms of correlation and mean absolute error with ECG as the reference. We also provide the Python implementation of our pipeline for the research community to facilitate integration into their solutions.
67.
Zargari, Amir Hosein Afandizadeh; Aqajari, Seyed Amir Hossein; Khodabandeh, Hadi; Rahmani, Amir M.; Kurdahi, Fadi J.
An Accurate Non-accelerometer-based PPG Motion Artifact Removal Technique using CycleGAN Journal Article
In: ACM Trans. Comput. Heal., 4 (1), pp. 1:1–1:14, 2023.
@article{DBLP:journals/health/ZargariAKRK23,
title = {An Accurate Non-accelerometer-based PPG Motion Artifact Removal
Technique using CycleGAN},
author = {Amir Hosein Afandizadeh Zargari and Seyed Amir Hossein Aqajari and Hadi Khodabandeh and Amir M. Rahmani and Fadi J. Kurdahi},
url = {https://doi.org/10.1145/3563949},
doi = {10.1145/3563949},
year = {2023},
date = {2023-01-01},
journal = {ACM Trans. Comput. Heal.},
volume = {4},
number = {1},
pages = {1:1--1:14},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
66.
Nagesh, Nitish; Azimi, Iman; Andriola, Tom; Rahmani, Amir M.; Jain, Ramesh C.
Towards Deep Personal Lifestyle Models Using Multimodal N-of-1 Data Inproceedings
In: Dang-Nguyen, Duc-Tien; Gurrin, Cathal; Larson, Martha A.; Smeaton, Alan F.; Rudinac, Stevan; Dao, Minh-Son; Trattner, Christoph; Chen, Phoebe (Ed.): MultiMedia Modeling - 29th International Conference, MMM 2023, Bergen, Norway, January 9-12, 2023, Proceedings, Part I, pp. 589–600, Springer, 2023.
@inproceedings{DBLP:conf/mmm/NageshAARJ23,
title = {Towards Deep Personal Lifestyle Models Using Multimodal N-of-1 Data},
author = {Nitish Nagesh and Iman Azimi and Tom Andriola and Amir M. Rahmani and Ramesh C. Jain},
editor = {Duc-Tien Dang-Nguyen and Cathal Gurrin and Martha A. Larson and Alan F. Smeaton and Stevan Rudinac and Minh-Son Dao and Christoph Trattner and Phoebe Chen},
url = {https://doi.org/10.1007/978-3-031-27077-2_46},
doi = {10.1007/978-3-031-27077-2_46},
year = {2023},
date = {2023-01-01},
booktitle = {MultiMedia Modeling - 29th International Conference, MMM 2023, Bergen,
Norway, January 9-12, 2023, Proceedings, Part I},
volume = {13833},
pages = {589--600},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
65.
Tazarv, Ali; Labbaf, Sina; Rahmani, Amir M.; Dutt, Nikil D.; Levorato, Marco
Active Reinforcement Learning for Personalized Stress Monitoring in Everyday Settings Journal Article
In: CoRR, abs/2305.00111 , 2023.
@article{DBLP:journals/corr/abs-2305-00111,
title = {Active Reinforcement Learning for Personalized Stress Monitoring in
Everyday Settings},
author = {Ali Tazarv and Sina Labbaf and Amir M. Rahmani and Nikil D. Dutt and Marco Levorato},
url = {https://doi.org/10.48550/arXiv.2305.00111},
doi = {10.48550/arXiv.2305.00111},
year = {2023},
date = {2023-01-01},
journal = {CoRR},
volume = {abs/2305.00111},
keywords = {UNITE},
pubstate = {published},
tppubtype = {article}
}
64.
Hughes, Thomas D; Subramanian, Ajan; Chakraborty, Rana; Cotton, Shannon A; Herrera, Maria Del Pilar Giraldo; Huang, Yong; Lambert, Natalie; Pinto, Melissa D; Rahmani, Amir M; Sierra, Carmen Josefa; Downs, Charles A.
The effect of SARS-CoV-2 variant on respiratory features and mortality Journal Article
In: Scientific reports, 13 (1), pp. 4503, 2023.
Abstract | Links | BibTeX | Tags:
@article{hughes2023effect,
title = {The effect of SARS-CoV-2 variant on respiratory features and mortality},
author = {Thomas D Hughes and Ajan Subramanian and Rana Chakraborty and Shannon A Cotton and Maria Del Pilar Giraldo Herrera and Yong Huang and Natalie Lambert and Melissa D Pinto and Amir M Rahmani and Carmen Josefa Sierra and Charles A. Downs},
url = {https://www.nature.com/articles/s41598-023-31761-y},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
journal = {Scientific reports},
volume = {13},
number = {1},
pages = {4503},
publisher = {Nature Publishing Group UK London},
abstract = {SARS-CoV-2 (COVID-19) has caused over 80 million infections 973,000 deaths in the United States, and mutations are linked to increased transmissibility. This study aimed to determine the effect of SARS-CoV-2 variants on respiratory features, mortality, and to determine the effect of vaccination status. A retrospective review of medical records (n = 55,406 unique patients) using the University of California Health COvid Research Data Set (UC CORDS) was performed to identify respiratory features, vaccination status, and mortality from 01/01/2020 to 04/26/2022. Variants were identified using the CDC data tracker. Increased odds of death were observed amongst unvaccinated individuals and fully vaccinated, partially vaccinated, or individuals who received any vaccination during multiple waves of the pandemic. Vaccination status was associated with survival and a decreased frequency of many respiratory features. More recent SARS-CoV-2 variants show a reduction in lower respiratory tract features with an increase in upper respiratory tract features. Being fully vaccinated results in fewer respiratory features and higher odds of survival, supporting vaccination in preventing morbidity and mortality from COVID-19.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
SARS-CoV-2 (COVID-19) has caused over 80 million infections 973,000 deaths in the United States, and mutations are linked to increased transmissibility. This study aimed to determine the effect of SARS-CoV-2 variants on respiratory features, mortality, and to determine the effect of vaccination status. A retrospective review of medical records (n = 55,406 unique patients) using the University of California Health COvid Research Data Set (UC CORDS) was performed to identify respiratory features, vaccination status, and mortality from 01/01/2020 to 04/26/2022. Variants were identified using the CDC data tracker. Increased odds of death were observed amongst unvaccinated individuals and fully vaccinated, partially vaccinated, or individuals who received any vaccination during multiple waves of the pandemic. Vaccination status was associated with survival and a decreased frequency of many respiratory features. More recent SARS-CoV-2 variants show a reduction in lower respiratory tract features with an increase in upper respiratory tract features. Being fully vaccinated results in fewer respiratory features and higher odds of survival, supporting vaccination in preventing morbidity and mortality from COVID-19.
63.
Burton, Candace W; Downs, Charles A; Hughes, Thomas; Lambert, Natalie; Abrahim, Heather L; Herrera, Maria Giraldo; Huang, Yong; Rahmani, Amir; Lee, Jung-Ah; Chakraborty, Rana; Pinto, Melissa D
A novel conceptual model of trauma-informed care for patients with post-acute sequelae of SARS-CoV-2 illness (PASC) Journal Article
In: Journal of advanced nursing, 78 (11), pp. 3618-3628, 2022.
Abstract | Links | BibTeX | Tags:
@article{nokey,
title = {A novel conceptual model of trauma-informed care for patients with post-acute sequelae of SARS-CoV-2 illness (PASC)},
author = {Candace W Burton and Charles A Downs and Thomas Hughes and Natalie Lambert and Heather L Abrahim and Maria Giraldo Herrera and Yong Huang and Amir Rahmani and Jung-Ah Lee and Rana Chakraborty and Melissa D Pinto},
url = {https://pubmed.ncbi.nlm.nih.gov/36036199/},
doi = {10.1111/jan.15426},
year = {2022},
date = {2022-11-01},
journal = {Journal of advanced nursing},
volume = {78},
number = {11},
pages = {3618-3628},
abstract = {Aim: This paper proposes a novel, trauma-informed, conceptual model of care for Post-Acute Sequelae of COVID-19 illness (PASC).
Design: This paper describes essential elements, linkages and dimensions of the model that affect PASC patient experiences and the potential impact of trauma-informed care on outcomes.
Data sources: PASC is a consequence of the global pandemic, and a new disease of which little is known. Our model was derived from the limited available studies, expert clinical experience specific to PASC survivors and publicly available social media narratives authored by PASC survivors.
Implications for nursing: The model provides a critical and novel framework for the understanding and care of persons affected by PASC. This model is aimed at the provision of nursing care, with the intention of reducing the traumatic impacts of the uncertain course of this disease, a lack of defined treatment options and difficulties in seeking care. The use of a trauma-informed care approach to PASC patients can enhance nurses' ability to remediate and ameliorate both the traumatic burden of and the symptoms and experience of the illness.
Conclusion: Applying a trauma-informed perspective to care of PASC patients can help to reduce the overall burden of this complex condition. Owing to the fundamentally holistic perspective of the nursing profession, nurses are best positioned to implement care that addresses multiple facets of the PASC experience.
Impact: The proposed model specifically addresses the myriad ways in which PASC may affect physical as well as mental and psychosocial dimensions of health. The model particularly seeks to suggest means of supporting patients who have already experienced a life-threatening illness and are now coping with its long-term impact. Since the scope of this impact is not yet defined, trauma-informed care for PASC patients is likely to reduce the overall health and systems burdens of this complex condition.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Aim: This paper proposes a novel, trauma-informed, conceptual model of care for Post-Acute Sequelae of COVID-19 illness (PASC).
Design: This paper describes essential elements, linkages and dimensions of the model that affect PASC patient experiences and the potential impact of trauma-informed care on outcomes.
Data sources: PASC is a consequence of the global pandemic, and a new disease of which little is known. Our model was derived from the limited available studies, expert clinical experience specific to PASC survivors and publicly available social media narratives authored by PASC survivors.
Implications for nursing: The model provides a critical and novel framework for the understanding and care of persons affected by PASC. This model is aimed at the provision of nursing care, with the intention of reducing the traumatic impacts of the uncertain course of this disease, a lack of defined treatment options and difficulties in seeking care. The use of a trauma-informed care approach to PASC patients can enhance nurses' ability to remediate and ameliorate both the traumatic burden of and the symptoms and experience of the illness.
Conclusion: Applying a trauma-informed perspective to care of PASC patients can help to reduce the overall burden of this complex condition. Owing to the fundamentally holistic perspective of the nursing profession, nurses are best positioned to implement care that addresses multiple facets of the PASC experience.
Impact: The proposed model specifically addresses the myriad ways in which PASC may affect physical as well as mental and psychosocial dimensions of health. The model particularly seeks to suggest means of supporting patients who have already experienced a life-threatening illness and are now coping with its long-term impact. Since the scope of this impact is not yet defined, trauma-informed care for PASC patients is likely to reduce the overall health and systems burdens of this complex condition.
Design: This paper describes essential elements, linkages and dimensions of the model that affect PASC patient experiences and the potential impact of trauma-informed care on outcomes.
Data sources: PASC is a consequence of the global pandemic, and a new disease of which little is known. Our model was derived from the limited available studies, expert clinical experience specific to PASC survivors and publicly available social media narratives authored by PASC survivors.
Implications for nursing: The model provides a critical and novel framework for the understanding and care of persons affected by PASC. This model is aimed at the provision of nursing care, with the intention of reducing the traumatic impacts of the uncertain course of this disease, a lack of defined treatment options and difficulties in seeking care. The use of a trauma-informed care approach to PASC patients can enhance nurses' ability to remediate and ameliorate both the traumatic burden of and the symptoms and experience of the illness.
Conclusion: Applying a trauma-informed perspective to care of PASC patients can help to reduce the overall burden of this complex condition. Owing to the fundamentally holistic perspective of the nursing profession, nurses are best positioned to implement care that addresses multiple facets of the PASC experience.
Impact: The proposed model specifically addresses the myriad ways in which PASC may affect physical as well as mental and psychosocial dimensions of health. The model particularly seeks to suggest means of supporting patients who have already experienced a life-threatening illness and are now coping with its long-term impact. Since the scope of this impact is not yet defined, trauma-informed care for PASC patients is likely to reduce the overall health and systems burdens of this complex condition.
62.
Shahhosseini, Sina; Seo, DongJoo; Kanduri, Anil; Hu, Tianyi; Lim, Sung-Soo; Donyanavard, Bryan; Rahmani, Amir M.; and Nikil Dutt,
Online Learning for Orchestration of Inference in Multi-user End-edge-cloud Networks Journal Article
In: ACM Transactions on Embedded Computing Systems, 21 (73), pp. 1–25, 2022.
Abstract | Links | BibTeX | Tags:
@article{Shahhosseini2022Online,
title = {Online Learning for Orchestration of Inference in Multi-user End-edge-cloud Networks},
author = {Sina Shahhosseini and DongJoo Seo and Anil Kanduri and Tianyi Hu and Sung-Soo Lim and Bryan Donyanavard and Amir M. Rahmani and and Nikil Dutt},
url = {https://dl.acm.org/doi/full/10.1145/3520129},
doi = {10.1145/3520129},
year = {2022},
date = {2022-11-01},
urldate = {2022-11-01},
journal = {ACM Transactions on Embedded Computing Systems},
volume = {21},
number = {73},
pages = {1–25},
abstract = {Deep-learning-based intelligent services have become prevalent in cyber-physical applications, including smart cities and health-care. Deploying deep-learning-based intelligence near the end-user enhances privacy protection, responsiveness, and reliability. Resource-constrained end-devices must be carefully managed to meet the latency and energy requirements of computationally intensive deep learning services. Collaborative end-edge-cloud computing for deep learning provides a range of performance and efficiency that can address application requirements through computation offloading. The decision to offload computation is a communication-computation co-optimization problem that varies with both system parameters (e.g., network condition) and workload characteristics (e.g., inputs). However, deep learning model optimization provides another source of tradeoff between latency and model accuracy. An end-to-end decision-making solution that considers such computation-communication problem is required to synergistically find the optimal offloading policy and model for deep learning services. To this end, we propose a reinforcement-learning-based computation offloading solution that learns optimal offloading policy considering deep learning model selection techniques to minimize response time while providing sufficient accuracy. We demonstrate the effectiveness of our solution for edge devices in an end-edge-cloud system and evaluate with a real-setup implementation using multiple AWS and ARM core configurations. Our solution provides 35% speedup in the average response time compared to the state-of-the-art with less than 0.9% accuracy reduction, demonstrating the promise of our online learning framework for orchestrating DL inference in end-edge-cloud systems.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Deep-learning-based intelligent services have become prevalent in cyber-physical applications, including smart cities and health-care. Deploying deep-learning-based intelligence near the end-user enhances privacy protection, responsiveness, and reliability. Resource-constrained end-devices must be carefully managed to meet the latency and energy requirements of computationally intensive deep learning services. Collaborative end-edge-cloud computing for deep learning provides a range of performance and efficiency that can address application requirements through computation offloading. The decision to offload computation is a communication-computation co-optimization problem that varies with both system parameters (e.g., network condition) and workload characteristics (e.g., inputs). However, deep learning model optimization provides another source of tradeoff between latency and model accuracy. An end-to-end decision-making solution that considers such computation-communication problem is required to synergistically find the optimal offloading policy and model for deep learning services. To this end, we propose a reinforcement-learning-based computation offloading solution that learns optimal offloading policy considering deep learning model selection techniques to minimize response time while providing sufficient accuracy. We demonstrate the effectiveness of our solution for edge devices in an end-edge-cloud system and evaluate with a real-setup implementation using multiple AWS and ARM core configurations. Our solution provides 35% speedup in the average response time compared to the state-of-the-art with less than 0.9% accuracy reduction, demonstrating the promise of our online learning framework for orchestrating DL inference in end-edge-cloud systems.
61.
Rostami, Ali; Nagesh, Nitish; Rahmani, Amir; Jain, Ramesh
World Food Atlas for Food Navigation Conference
MADiMa '22: Proceedings of the 7th International Workshop on Multimedia Assisted Dietary Management, 2022.
Abstract | Links | BibTeX | Tags: Food Knowledge Graph, Food Recommendation, Food Schema, Health Effect Queries, Health Navigation, Personal Food Model, Recipe Dataset
@conference{nokey,
title = {World Food Atlas for Food Navigation},
author = {Ali Rostami and Nitish Nagesh and Amir Rahmani and Ramesh Jain},
url = {https://dl.acm.org/doi/10.1145/3552484.3555748},
doi = {10.1145/3552484.3555748},
year = {2022},
date = {2022-10-01},
urldate = {2022-10-01},
booktitle = {MADiMa '22: Proceedings of the 7th International Workshop on Multimedia Assisted Dietary Management},
pages = {39–47},
abstract = {Food plays a central role in agriculture, public wellness, public health, culinary art, and culture. Food-related data is available in varying formats and with different access levels ranging from private datasets to publicly downloadable data. Every food-related query, in principle, is a food recommendation problem. We analyze the components of a food recommendation and its requirements. We demonstrate the effectiveness of having access to worldwide food data from divergent aspects for answering food- and health-related queries that would otherwise be expensive and require specialized domain expertise. We present the World Food Atlas (WFA): An open-source platform for different stakeholders in the food ecosystem to share their data on a global data hub with a singular point of access. The world food atlas contains the availability and interconnectivity of food and its effects in various forms. We gather real-world questions by partnering with nutritionists, dietitians, and doctors. We categorize the practical food queries to construct requirement tables and develop a novel schema to satisfy the requirement table to model the world food atlas. Finally, we demonstrate how food and lifestyle navigation systems can use the world food atlas to enable personalized and context-driven solutions to person-entity-context queries.},
keywords = {Food Knowledge Graph, Food Recommendation, Food Schema, Health Effect Queries, Health Navigation, Personal Food Model, Recipe Dataset},
pubstate = {published},
tppubtype = {conference}
}
Food plays a central role in agriculture, public wellness, public health, culinary art, and culture. Food-related data is available in varying formats and with different access levels ranging from private datasets to publicly downloadable data. Every food-related query, in principle, is a food recommendation problem. We analyze the components of a food recommendation and its requirements. We demonstrate the effectiveness of having access to worldwide food data from divergent aspects for answering food- and health-related queries that would otherwise be expensive and require specialized domain expertise. We present the World Food Atlas (WFA): An open-source platform for different stakeholders in the food ecosystem to share their data on a global data hub with a singular point of access. The world food atlas contains the availability and interconnectivity of food and its effects in various forms. We gather real-world questions by partnering with nutritionists, dietitians, and doctors. We categorize the practical food queries to construct requirement tables and develop a novel schema to satisfy the requirement table to model the world food atlas. Finally, we demonstrate how food and lifestyle navigation systems can use the world food atlas to enable personalized and context-driven solutions to person-entity-context queries.
60.
Huang, Yong; Pinto, Melissa D; Borelli, Jessica L; Mehrabadi, Milad Asgari; Abrihim, Heather; Dutt, Nikil; Lambert, Natalie; Nurmi, Erika L; Chakraborty, Rana; Rahmani, Amir M; Downs, Charles A.
COVID symptoms, symptom clusters, and predictors for becoming a long-hauler: looking for clarity in the haze of the pandemic Journal Article
In: Clinical Nursing Research, 31 (8), 2022.
Abstract | Links | BibTeX | Tags:
@article{huang2021covid,
title = {COVID symptoms, symptom clusters, and predictors for becoming a long-hauler: looking for clarity in the haze of the pandemic},
author = {Yong Huang and Melissa D Pinto and Jessica L Borelli and Milad Asgari Mehrabadi and Heather Abrihim and Nikil Dutt and Natalie Lambert and Erika L Nurmi and Rana Chakraborty and Amir M Rahmani and Charles A. Downs},
url = {https://journals.sagepub.com/doi/full/10.1177/10547738221125632},
doi = {10.1177/10547738221125632},
year = {2022},
date = {2022-09-01},
urldate = {2021-01-01},
journal = {Clinical Nursing Research},
volume = {31},
number = {8},
publisher = {Cold Spring Harbor Laboratory Press},
abstract = {Post-acute sequelae of SARS-CoV-2 (PASC) is defined as persistent symptoms after apparent recovery from acute COVID-19 infection, also known as COVID-19 long-haul. We performed a retrospective review of electronic health records (EHR) from the University of California COvid Research Data Set (UC CORDS), a de-identified EHR of PCR-confirmed SARS-CoV-2-positive patients in California. The purposes were to (1) describe the prevalence of PASC, (2) describe COVID-19 symptoms and symptom clusters, and (3) identify risk factors for PASC. Data were subjected to non-negative matrix factorization to identify symptom clusters, and a predictive model of PASC was developed. PASC prevalence was 11% (277/2,153), and of these patients, 66% (183/277) were considered asymptomatic at days 0–30. Five PASC symptom clusters emerged and specific symptoms at days 0–30 were associated with PASC. Women were more likely than men to develop PASC, with all age groups and ethnicities represented. PASC is a public health priority.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Post-acute sequelae of SARS-CoV-2 (PASC) is defined as persistent symptoms after apparent recovery from acute COVID-19 infection, also known as COVID-19 long-haul. We performed a retrospective review of electronic health records (EHR) from the University of California COvid Research Data Set (UC CORDS), a de-identified EHR of PCR-confirmed SARS-CoV-2-positive patients in California. The purposes were to (1) describe the prevalence of PASC, (2) describe COVID-19 symptoms and symptom clusters, and (3) identify risk factors for PASC. Data were subjected to non-negative matrix factorization to identify symptom clusters, and a predictive model of PASC was developed. PASC prevalence was 11% (277/2,153), and of these patients, 66% (183/277) were considered asymptomatic at days 0–30. Five PASC symptom clusters emerged and specific symptoms at days 0–30 were associated with PASC. Women were more likely than men to develop PASC, with all age groups and ethnicities represented. PASC is a public health priority.
59.
Lambert, Natalie; Corps, Survivor; El-Azab, Sarah A; Ramrakhiani, Nathan S; Barisano, Anthony; Yu, Lu; Taylor, Kaitlyn; Esperanca, Alvaro; Downs, Charles A; Abrahim, Heather L; Hughes, Thomas; Rahmani, Amir M.; Borelli, Jessica L; Chakraborty, Rana; Pinto, Melissa D.
The other COVID-19 survivors: Timing, duration, and health impact of post-acute sequelae of SARS-CoV-2 infection Journal Article
In: Journal of Clinical Nursing, 2022.
Abstract | Links | BibTeX | Tags:
@article{lambert2021covid,
title = {The other COVID-19 survivors: Timing, duration, and health impact of post-acute sequelae of SARS-CoV-2 infection},
author = {Natalie Lambert and Survivor Corps and Sarah A El-Azab and Nathan S Ramrakhiani and Anthony Barisano and Lu Yu and Kaitlyn Taylor and Alvaro Esperanca and Charles A Downs and Heather L Abrahim and Thomas Hughes and Amir M. Rahmani and Jessica L Borelli and Rana Chakraborty and Melissa D. Pinto},
url = {https://pubmed.ncbi.nlm.nih.gov/36181315/},
doi = {10.1111/jocn.16541},
year = {2022},
date = {2022-09-01},
urldate = {2021-01-01},
journal = {Journal of Clinical Nursing},
publisher = {Cold Spring Harbor Laboratory Press},
abstract = {Aims and objectives: To determine the frequency, timing, and duration of post-acute sequelae of SARS-CoV-2 infection (PASC) and their impact on health and function.
Background: Post-acute sequelae of SARS-CoV-2 infection is an emerging major public health problem that is poorly understood and has no current treatment or cure. PASC is a new syndrome that has yet to be fully clinically characterised.
Design: Descriptive cross-sectional survey (n = 5163) was conducted from online COVID-19 survivor support groups who reported symptoms for more than 21 days following SARS-CoV-2 infection.
Methods: Participants reported background demographics and the date and method of their covid diagnosis, as well as all symptoms experienced since onset of covid in terms of the symptom start date, duration, and Likert scales measuring three symptom-specific health impacts: pain and discomfort, work impairment, and social impairment. Descriptive statistics and measures of central tendencies were computed for participant demographics and symptom data.
Results: Participants reported experiencing a mean of 21 symptoms (range 1-93); fatigue (79.0%), headache (55.3%), shortness of breath (55.3%) and difficulty concentrating (53.6%) were the most common. Symptoms often remitted and relapsed for extended periods of time (duration M = 112 days), longest lasting symptoms included the inability to exercise (M = 106.5 days), fatigue (M = 101.7 days) and difficulty concentrating, associated with memory impairment (M = 101.1 days). Participants reported extreme pressure at the base of the head, syncope, sharp or sudden chest pain, and "brain pressure" among the most distressing and impacting daily life.
Conclusions: Post-acute sequelae of SARS-CoV-2 infection can be characterised by a wide range of symptoms, many of which cause moderate-to-severe distress and can hinder survivors' overall well-being.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Aims and objectives: To determine the frequency, timing, and duration of post-acute sequelae of SARS-CoV-2 infection (PASC) and their impact on health and function.
Background: Post-acute sequelae of SARS-CoV-2 infection is an emerging major public health problem that is poorly understood and has no current treatment or cure. PASC is a new syndrome that has yet to be fully clinically characterised.
Design: Descriptive cross-sectional survey (n = 5163) was conducted from online COVID-19 survivor support groups who reported symptoms for more than 21 days following SARS-CoV-2 infection.
Methods: Participants reported background demographics and the date and method of their covid diagnosis, as well as all symptoms experienced since onset of covid in terms of the symptom start date, duration, and Likert scales measuring three symptom-specific health impacts: pain and discomfort, work impairment, and social impairment. Descriptive statistics and measures of central tendencies were computed for participant demographics and symptom data.
Results: Participants reported experiencing a mean of 21 symptoms (range 1-93); fatigue (79.0%), headache (55.3%), shortness of breath (55.3%) and difficulty concentrating (53.6%) were the most common. Symptoms often remitted and relapsed for extended periods of time (duration M = 112 days), longest lasting symptoms included the inability to exercise (M = 106.5 days), fatigue (M = 101.7 days) and difficulty concentrating, associated with memory impairment (M = 101.1 days). Participants reported extreme pressure at the base of the head, syncope, sharp or sudden chest pain, and "brain pressure" among the most distressing and impacting daily life.
Conclusions: Post-acute sequelae of SARS-CoV-2 infection can be characterised by a wide range of symptoms, many of which cause moderate-to-severe distress and can hinder survivors' overall well-being.
Background: Post-acute sequelae of SARS-CoV-2 infection is an emerging major public health problem that is poorly understood and has no current treatment or cure. PASC is a new syndrome that has yet to be fully clinically characterised.
Design: Descriptive cross-sectional survey (n = 5163) was conducted from online COVID-19 survivor support groups who reported symptoms for more than 21 days following SARS-CoV-2 infection.
Methods: Participants reported background demographics and the date and method of their covid diagnosis, as well as all symptoms experienced since onset of covid in terms of the symptom start date, duration, and Likert scales measuring three symptom-specific health impacts: pain and discomfort, work impairment, and social impairment. Descriptive statistics and measures of central tendencies were computed for participant demographics and symptom data.
Results: Participants reported experiencing a mean of 21 symptoms (range 1-93); fatigue (79.0%), headache (55.3%), shortness of breath (55.3%) and difficulty concentrating (53.6%) were the most common. Symptoms often remitted and relapsed for extended periods of time (duration M = 112 days), longest lasting symptoms included the inability to exercise (M = 106.5 days), fatigue (M = 101.7 days) and difficulty concentrating, associated with memory impairment (M = 101.1 days). Participants reported extreme pressure at the base of the head, syncope, sharp or sudden chest pain, and "brain pressure" among the most distressing and impacting daily life.
Conclusions: Post-acute sequelae of SARS-CoV-2 infection can be characterised by a wide range of symptoms, many of which cause moderate-to-severe distress and can hinder survivors' overall well-being.
58.
Mousavi, Zahra; Simon, Katharine; Rivera, Alex; Yunusova, Asal; Hu, Sirui; Labbaf, Sina; Jafarlou, Salar; Dutt, Nikil; Jain, Ramesh; Rahmani, Amir M.; Borelli, Jessica
Sleep Patterns and Affect Dynamics Among College Students During the COVID-19 Pandemic: Intensive Longitudinal Study Journal Article
In: JMIR Formative Research, 6 (8), pp. e33964, 2022, ISSN: 2561-326X.
Abstract | Links | BibTeX | Tags: Affect Dynamics, Affect Variability, COVID-19, MHN, Objective Sleep Outcomes, Sleep
@article{Mousavi2022,
title = {Sleep Patterns and Affect Dynamics Among College Students During the COVID-19 Pandemic: Intensive Longitudinal Study},
author = {Zahra Mousavi and Katharine Simon and Alex Rivera and Asal Yunusova and Sirui Hu and Sina Labbaf and Salar Jafarlou and Nikil Dutt and Ramesh Jain and Amir M. Rahmani and Jessica Borelli},
doi = {10.2196/33964},
issn = {2561-326X},
year = {2022},
date = {2022-08-05},
urldate = {2022-08-05},
journal = {JMIR Formative Research},
volume = {6},
number = {8},
pages = {e33964},
abstract = {Background:
Sleep disturbance is a transdiagnostic risk factor that is so prevalent among young adults that it is considered a public health epidemic, which has been exacerbated by the COVID-19 pandemic. Sleep may contribute to mental health via affect dynamics. Prior literature on the contribution of sleep to affect is largely based on correlational studies or experiments that do not generalize to the daily lives of young adults. Furthermore, the literature examining the associations between sleep variability and affect dynamics remains scant.
Objective:
In an ecologically valid context, using an intensive longitudinal design, we aimed to assess the daily and long-term associations between sleep patterns and affect dynamics among young adults during the COVID-19 pandemic.
Methods:
College student participants (N=20; female: 13/20, 65%) wore an Oura ring (Ōura Health Ltd) continuously for 3 months to measure sleep patterns, such as average and variability in total sleep time (TST), wake after sleep onset (WASO), sleep efficiency, and sleep onset latency (SOL), resulting in 1173 unique observations. We administered a daily ecological momentary assessment by using a mobile health app to evaluate positive affect (PA), negative affect (NA), and COVID-19 worry once per day.
Results:
Participants with a higher sleep onset latency (b=−1.09, SE 0.36; P=.006) and TST (b=−0.15, SE 0.05; P=.008) on the prior day had lower PA on the next day. Further, higher average TST across the 3-month period predicted lower average PA (b=−0.36, SE 0.12; P=.009). TST variability predicted higher affect variability across all affect domains. Specifically, higher variability in TST was associated higher PA variability (b=0.09, SE 0.03; P=.007), higher negative affect variability (b=0.12, SE 0.05; P=.03), and higher COVID-19 worry variability (b=0.16, SE 0.07; P=.04).
Conclusions:
Fluctuating sleep patterns are associated with affect dynamics at the daily and long-term scales. Low PA and affect variability may be potential pathways through which sleep has implications for mental health.},
keywords = {Affect Dynamics, Affect Variability, COVID-19, MHN, Objective Sleep Outcomes, Sleep},
pubstate = {published},
tppubtype = {article}
}
Background:
Sleep disturbance is a transdiagnostic risk factor that is so prevalent among young adults that it is considered a public health epidemic, which has been exacerbated by the COVID-19 pandemic. Sleep may contribute to mental health via affect dynamics. Prior literature on the contribution of sleep to affect is largely based on correlational studies or experiments that do not generalize to the daily lives of young adults. Furthermore, the literature examining the associations between sleep variability and affect dynamics remains scant.
Objective:
In an ecologically valid context, using an intensive longitudinal design, we aimed to assess the daily and long-term associations between sleep patterns and affect dynamics among young adults during the COVID-19 pandemic.
Methods:
College student participants (N=20; female: 13/20, 65%) wore an Oura ring (Ōura Health Ltd) continuously for 3 months to measure sleep patterns, such as average and variability in total sleep time (TST), wake after sleep onset (WASO), sleep efficiency, and sleep onset latency (SOL), resulting in 1173 unique observations. We administered a daily ecological momentary assessment by using a mobile health app to evaluate positive affect (PA), negative affect (NA), and COVID-19 worry once per day.
Results:
Participants with a higher sleep onset latency (b=−1.09, SE 0.36; P=.006) and TST (b=−0.15, SE 0.05; P=.008) on the prior day had lower PA on the next day. Further, higher average TST across the 3-month period predicted lower average PA (b=−0.36, SE 0.12; P=.009). TST variability predicted higher affect variability across all affect domains. Specifically, higher variability in TST was associated higher PA variability (b=0.09, SE 0.03; P=.007), higher negative affect variability (b=0.12, SE 0.05; P=.03), and higher COVID-19 worry variability (b=0.16, SE 0.07; P=.04).
Conclusions:
Fluctuating sleep patterns are associated with affect dynamics at the daily and long-term scales. Low PA and affect variability may be potential pathways through which sleep has implications for mental health.
Sleep disturbance is a transdiagnostic risk factor that is so prevalent among young adults that it is considered a public health epidemic, which has been exacerbated by the COVID-19 pandemic. Sleep may contribute to mental health via affect dynamics. Prior literature on the contribution of sleep to affect is largely based on correlational studies or experiments that do not generalize to the daily lives of young adults. Furthermore, the literature examining the associations between sleep variability and affect dynamics remains scant.
Objective:
In an ecologically valid context, using an intensive longitudinal design, we aimed to assess the daily and long-term associations between sleep patterns and affect dynamics among young adults during the COVID-19 pandemic.
Methods:
College student participants (N=20; female: 13/20, 65%) wore an Oura ring (Ōura Health Ltd) continuously for 3 months to measure sleep patterns, such as average and variability in total sleep time (TST), wake after sleep onset (WASO), sleep efficiency, and sleep onset latency (SOL), resulting in 1173 unique observations. We administered a daily ecological momentary assessment by using a mobile health app to evaluate positive affect (PA), negative affect (NA), and COVID-19 worry once per day.
Results:
Participants with a higher sleep onset latency (b=−1.09, SE 0.36; P=.006) and TST (b=−0.15, SE 0.05; P=.008) on the prior day had lower PA on the next day. Further, higher average TST across the 3-month period predicted lower average PA (b=−0.36, SE 0.12; P=.009). TST variability predicted higher affect variability across all affect domains. Specifically, higher variability in TST was associated higher PA variability (b=0.09, SE 0.03; P=.007), higher negative affect variability (b=0.12, SE 0.05; P=.03), and higher COVID-19 worry variability (b=0.16, SE 0.07; P=.04).
Conclusions:
Fluctuating sleep patterns are associated with affect dynamics at the daily and long-term scales. Low PA and affect variability may be potential pathways through which sleep has implications for mental health.
57.
Jafarlou, Salar; Rahmani, Amir M.; Dutt, Nikil; Mousavi, Sanaz Rahimi
ECG Biosignal Deidentification Using Conditional Generative Adversarial Networks Conference
44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), IEEE, 2022.
Abstract | Links | BibTeX | Tags:
@conference{Jafarlou2022ECG,
title = {ECG Biosignal Deidentification Using Conditional Generative Adversarial Networks},
author = {Salar Jafarlou and Amir M. Rahmani and Nikil Dutt and Sanaz Rahimi Mousavi},
url = {https://ieeexplore.ieee.org/abstract/document/9872015},
doi = {10.1109/EMBC48229.2022.9872015},
year = {2022},
date = {2022-07-01},
booktitle = {44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)},
publisher = {IEEE},
abstract = {Electrocardiogram (ECG) signals provide rich information on individuals' potential cardiovascular conditions and disease, ranging from coronary artery disease to the risk of a heart attack. While health providers store and share these information for medical and research purposes, such data is highly vulnerable to privacy concerns, similar to many other types of healthcare data. Recent works have shown the feasibility of identifying and authenticating individuals by using ECG as a biometric due to the highly individualized nature of ECG signals. However, to the best of our knowledge, there does not exist a method in the literature attempting to de-identify ECG signals. In this paper, to address this privacy protection gap, we propose a Generative Adversarial Network (GAN)-based framework for de-identification of ECG signals. We leverage a combination of a standard GAN loss, an Ordinary Differential Equations (ODE)-based, and identity-based loss values to train a generator that de-identifies a ECG signal while preserving structure the ECG signal and information regarding the target cardio vascular condition. We evaluate our framework in terms of both qualitative and quantitative metrics considering different weightings over the above-mentioned losses. Our experiments demonstrate the efficiency of our framework in terms of privacy protection and ECG signal structural preservation.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Electrocardiogram (ECG) signals provide rich information on individuals' potential cardiovascular conditions and disease, ranging from coronary artery disease to the risk of a heart attack. While health providers store and share these information for medical and research purposes, such data is highly vulnerable to privacy concerns, similar to many other types of healthcare data. Recent works have shown the feasibility of identifying and authenticating individuals by using ECG as a biometric due to the highly individualized nature of ECG signals. However, to the best of our knowledge, there does not exist a method in the literature attempting to de-identify ECG signals. In this paper, to address this privacy protection gap, we propose a Generative Adversarial Network (GAN)-based framework for de-identification of ECG signals. We leverage a combination of a standard GAN loss, an Ordinary Differential Equations (ODE)-based, and identity-based loss values to train a generator that de-identifies a ECG signal while preserving structure the ECG signal and information regarding the target cardio vascular condition. We evaluate our framework in terms of both qualitative and quantitative metrics considering different weightings over the above-mentioned losses. Our experiments demonstrate the efficiency of our framework in terms of privacy protection and ECG signal structural preservation.
56.
Sarhaddi, Fatemeh; Azimi, Iman; Axelin, Anna; Niela-Vilen, Hannakaisa; Liljeberg, Pasi; Rahmani, Amir M
In: JMIR mHealth and uHealth, 10 (6), pp. e33458, 2022.
Abstract | Links | BibTeX | Tags:
@article{Sarhaddi2022Trends,
title = {Trends in Heart Rate and Heart Rate Variability During Pregnancy and the 3-Month Postpartum Period: Continuous Monitoring in a Free-living Context},
author = {Fatemeh Sarhaddi and Iman Azimi and Anna Axelin and Hannakaisa Niela-Vilen and Pasi Liljeberg and Amir M Rahmani},
url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9206203/},
doi = {10.2196/33458},
year = {2022},
date = {2022-06-01},
urldate = {2022-06-01},
journal = {JMIR mHealth and uHealth},
volume = {10},
number = {6},
pages = {e33458},
abstract = {Background: Heart rate variability (HRV) is a noninvasive method that reflects the regulation of the autonomic nervous system. Altered HRV is associated with adverse mental or physical health complications. The autonomic nervous system also has a central role in physiological adaption during pregnancy, causing normal changes in HRV.
Objective: The aim of this study was to assess trends in heart rate (HR) and HRV parameters as a noninvasive method for remote maternal health monitoring during pregnancy and 3-month postpartum period.
Methods: A total of 58 pregnant women were monitored using an Internet of Things–based remote monitoring system during pregnancy and 3-month postpartum period. Pregnant women were asked to continuously wear Gear Sport smartwatch to monitor their HR and HRV extracted from photoplethysmogram (PPG) signals. In addition, a cross-platform mobile app was used to collect background and delivery-related information. We analyzed PPG signals collected during the night and discarded unreliable signals by applying a PPG quality assessment method to the collected signals. HR, HRV, and normalized HRV parameters were extracted from reliable signals. The normalization removed the effect of HR changes on HRV trends. Finally, we used hierarchical linear mixed models to analyze the trends of HR, HRV, and normalized HRV parameters.
Results: HR increased significantly during the second trimester (P<.001) and decreased significantly during the third trimester (P=.006). Time-domain HRV parameters, average normal interbeat intervals (IBIs; average normal IBIs [AVNN]), SD of normal IBIs (SDNN), root mean square of the successive difference of normal IBIs (RMSSD), normalized SDNN, and normalized RMSSD decreased significantly during the second trimester (P<.001). Then, AVNN, SDNN, RMSSD, and normalized SDNN increased significantly during the third trimester (with P=.002, P<.001, P<.001, and P<.001, respectively). Some of the frequency-domain parameters, low-frequency power (LF), high-frequency power (HF), and normalized HF, decreased significantly during the second trimester (with P<.001, P<.001, and P=.003, respectively), and HF increased significantly during the third trimester (P=.007). In the postpartum period, normalized RMSSD decreased (P=.01), and the LF to HF ratio (LF/HF) increased significantly (P=.004).
Conclusions: Our study indicates the physiological changes during pregnancy and the postpartum period. We showed that HR increased and HRV parameters decreased as pregnancy proceeded, and the values returned to normal after delivery. Moreover, our results show that HR started to decrease, whereas time-domain HRV parameters and HF started to increase during the third trimester. The results also indicated that age was significantly associated with HRV parameters during pregnancy and postpartum period, whereas education level was associated with HRV parameters during the third trimester. In addition, our results demonstrate the possibility of continuous HRV monitoring in everyday life settings.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Background: Heart rate variability (HRV) is a noninvasive method that reflects the regulation of the autonomic nervous system. Altered HRV is associated with adverse mental or physical health complications. The autonomic nervous system also has a central role in physiological adaption during pregnancy, causing normal changes in HRV.
Objective: The aim of this study was to assess trends in heart rate (HR) and HRV parameters as a noninvasive method for remote maternal health monitoring during pregnancy and 3-month postpartum period.
Methods: A total of 58 pregnant women were monitored using an Internet of Things–based remote monitoring system during pregnancy and 3-month postpartum period. Pregnant women were asked to continuously wear Gear Sport smartwatch to monitor their HR and HRV extracted from photoplethysmogram (PPG) signals. In addition, a cross-platform mobile app was used to collect background and delivery-related information. We analyzed PPG signals collected during the night and discarded unreliable signals by applying a PPG quality assessment method to the collected signals. HR, HRV, and normalized HRV parameters were extracted from reliable signals. The normalization removed the effect of HR changes on HRV trends. Finally, we used hierarchical linear mixed models to analyze the trends of HR, HRV, and normalized HRV parameters.
Results: HR increased significantly during the second trimester (P<.001) and decreased significantly during the third trimester (P=.006). Time-domain HRV parameters, average normal interbeat intervals (IBIs; average normal IBIs [AVNN]), SD of normal IBIs (SDNN), root mean square of the successive difference of normal IBIs (RMSSD), normalized SDNN, and normalized RMSSD decreased significantly during the second trimester (P<.001). Then, AVNN, SDNN, RMSSD, and normalized SDNN increased significantly during the third trimester (with P=.002, P<.001, P<.001, and P<.001, respectively). Some of the frequency-domain parameters, low-frequency power (LF), high-frequency power (HF), and normalized HF, decreased significantly during the second trimester (with P<.001, P<.001, and P=.003, respectively), and HF increased significantly during the third trimester (P=.007). In the postpartum period, normalized RMSSD decreased (P=.01), and the LF to HF ratio (LF/HF) increased significantly (P=.004).
Conclusions: Our study indicates the physiological changes during pregnancy and the postpartum period. We showed that HR increased and HRV parameters decreased as pregnancy proceeded, and the values returned to normal after delivery. Moreover, our results show that HR started to decrease, whereas time-domain HRV parameters and HF started to increase during the third trimester. The results also indicated that age was significantly associated with HRV parameters during pregnancy and postpartum period, whereas education level was associated with HRV parameters during the third trimester. In addition, our results demonstrate the possibility of continuous HRV monitoring in everyday life settings.
Objective: The aim of this study was to assess trends in heart rate (HR) and HRV parameters as a noninvasive method for remote maternal health monitoring during pregnancy and 3-month postpartum period.
Methods: A total of 58 pregnant women were monitored using an Internet of Things–based remote monitoring system during pregnancy and 3-month postpartum period. Pregnant women were asked to continuously wear Gear Sport smartwatch to monitor their HR and HRV extracted from photoplethysmogram (PPG) signals. In addition, a cross-platform mobile app was used to collect background and delivery-related information. We analyzed PPG signals collected during the night and discarded unreliable signals by applying a PPG quality assessment method to the collected signals. HR, HRV, and normalized HRV parameters were extracted from reliable signals. The normalization removed the effect of HR changes on HRV trends. Finally, we used hierarchical linear mixed models to analyze the trends of HR, HRV, and normalized HRV parameters.
Results: HR increased significantly during the second trimester (P<.001) and decreased significantly during the third trimester (P=.006). Time-domain HRV parameters, average normal interbeat intervals (IBIs; average normal IBIs [AVNN]), SD of normal IBIs (SDNN), root mean square of the successive difference of normal IBIs (RMSSD), normalized SDNN, and normalized RMSSD decreased significantly during the second trimester (P<.001). Then, AVNN, SDNN, RMSSD, and normalized SDNN increased significantly during the third trimester (with P=.002, P<.001, P<.001, and P<.001, respectively). Some of the frequency-domain parameters, low-frequency power (LF), high-frequency power (HF), and normalized HF, decreased significantly during the second trimester (with P<.001, P<.001, and P=.003, respectively), and HF increased significantly during the third trimester (P=.007). In the postpartum period, normalized RMSSD decreased (P=.01), and the LF to HF ratio (LF/HF) increased significantly (P=.004).
Conclusions: Our study indicates the physiological changes during pregnancy and the postpartum period. We showed that HR increased and HRV parameters decreased as pregnancy proceeded, and the values returned to normal after delivery. Moreover, our results show that HR started to decrease, whereas time-domain HRV parameters and HF started to increase during the third trimester. The results also indicated that age was significantly associated with HRV parameters during pregnancy and postpartum period, whereas education level was associated with HRV parameters during the third trimester. In addition, our results demonstrate the possibility of continuous HRV monitoring in everyday life settings.
55.
Shahhosseini, Sina; Ni, Yang; Naeini, Emad Kasaeyan; Imani, Mohsen; Rahmani, Amir M.; Dutt, Nikil
Flexible and Personalized Learning for Wearable Health Applications using HyperDimensional Computing Conference
ACM GLSVLSI '22: Proceedings of the Great Lakes Symposium on VLSI, ACM, 2022.
Abstract | Links | BibTeX | Tags:
@conference{Shahhosseini2022Flexible,
title = {Flexible and Personalized Learning for Wearable Health Applications using HyperDimensional Computing},
author = {Sina Shahhosseini and Yang Ni and Emad Kasaeyan Naeini and Mohsen Imani and Amir M. Rahmani and Nikil Dutt},
url = {https://dl.acm.org/doi/10.1145/3526241.3530373},
doi = {10.1145/3526241.3530373},
year = {2022},
date = {2022-06-01},
booktitle = {ACM GLSVLSI '22: Proceedings of the Great Lakes Symposium on VLSI},
pages = {357–360},
publisher = {ACM},
abstract = {Health and wellness applications increasingly rely on machine learning techniques to learn end-user physiological and behavioral patterns in everyday settings, posing two key challenges: inability to perform on-device online learning for resource-constrained wearables, and learning algorithms that support privacy-preserving personalization. We exploit a Hyperdimensional computing (HDC) solution for wearable devices that offers flexibility, high efficiency, and performance while enabling on-device personalization and privacy protection. We evaluate the efficacy of our approach using three case studies and show that our system improves performance of training by up to 35.8x compared with the state-of-the-art while offering a comparable accuracy.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Health and wellness applications increasingly rely on machine learning techniques to learn end-user physiological and behavioral patterns in everyday settings, posing two key challenges: inability to perform on-device online learning for resource-constrained wearables, and learning algorithms that support privacy-preserving personalization. We exploit a Hyperdimensional computing (HDC) solution for wearable devices that offers flexibility, high efficiency, and performance while enabling on-device personalization and privacy protection. We evaluate the efficacy of our approach using three case studies and show that our system improves performance of training by up to 35.8x compared with the state-of-the-art while offering a comparable accuracy.
54.
Vo, Khuong; Vishwanath, Manoj; Srinivasan, Ramesh; Dutt, Nikil; Cao, Hung
Composing Graphical Models with Generative Adversarial Networks for EEG Signal Modeling Conference
ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, 2022.
Abstract | Links | BibTeX | Tags:
@conference{Vo2022Composing,
title = {Composing Graphical Models with Generative Adversarial Networks for EEG Signal Modeling},
author = {Khuong Vo and Manoj Vishwanath and Ramesh Srinivasan and Nikil Dutt and Hung Cao},
url = {https://ieeexplore.ieee.org/abstract/document/9747783},
doi = {10.1109/ICASSP43922.2022.9747783},
year = {2022},
date = {2022-05-01},
urldate = {2022-05-01},
booktitle = {ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
publisher = {IEEE},
abstract = {Neural oscillations in the form of electroencephalogram (EEG) can reveal underlying brain functions, such as cognition, memory, perception, and consciousness. A comprehensive EEG computational model provides not only a stochastic procedure that directly generates data but also insights to further understand the neurological mechanisms. Here, we propose a generative and inference approach that combines the complementary benefits of probabilistic graphical models and generative adversarial networks (GANs) for EEG signal modeling. We investigate the method’s ability to jointly learn coherent generation and inverse inference models on the CHI-MIT epilepsy multi-channel EEG dataset. We further study the efficacy of the learned representations in epilepsy seizure detection formulated as an unsupervised learning problem. Quantitative and qualitative experimental results demonstrate the effectiveness and efficiency of our approach.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Neural oscillations in the form of electroencephalogram (EEG) can reveal underlying brain functions, such as cognition, memory, perception, and consciousness. A comprehensive EEG computational model provides not only a stochastic procedure that directly generates data but also insights to further understand the neurological mechanisms. Here, we propose a generative and inference approach that combines the complementary benefits of probabilistic graphical models and generative adversarial networks (GANs) for EEG signal modeling. We investigate the method’s ability to jointly learn coherent generation and inverse inference models on the CHI-MIT epilepsy multi-channel EEG dataset. We further study the efficacy of the learned representations in epilepsy seizure detection formulated as an unsupervised learning problem. Quantitative and qualitative experimental results demonstrate the effectiveness and efficiency of our approach.
53.
Jimah, Tamara; Kehoe, Priscilla; Borg, Holly; Pimentel, Pamela; Rahmani, Amir M.; Dutt, Nikil; Guo, Yuqing
A Micro-Level Analysis of Physiological Responses to COVID-19: Continuous Monitoring of Pregnant Women in California Journal Article
In: Frontiers in Public Health, 10 , pp. 808763, 2022.
Abstract | Links | BibTeX | Tags:
@article{Jimah2022Micro,
title = {A Micro-Level Analysis of Physiological Responses to COVID-19: Continuous Monitoring of Pregnant Women in California},
author = {Tamara Jimah and Priscilla Kehoe and Holly Borg and Pamela Pimentel and Amir M. Rahmani and Nikil Dutt and Yuqing Guo},
url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9021503/},
doi = {10.3389/fpubh.2022.808763},
year = {2022},
date = {2022-04-01},
urldate = {2022-04-01},
journal = {Frontiers in Public Health},
volume = {10},
pages = {808763},
abstract = {Continuous monitoring of perinatal women in a descriptive case study allowed us the opportunity to examine the time during which the COVID-19 infection led to physiological changes in two low-income pregnant women. An important component of this study was the use of a wearable sensor device, the Oura ring, to monitor and record vital physiological parameters during sleep. Two women in their second and third trimesters, respectively, were selected based on a positive COVID-19 diagnosis. Both women were tested using the polymerase chain reaction method to confirm the presence of the virus during which time we were able to collect these physiological data. In both cases, we observed 3–6 days of peak physiological changes in resting heart rate (HR), heart rate variability (HRV), and respiratory rate (RR), as well as sleep surrounding the onset of COVID-19 symptoms. The pregnant woman in her third trimester showed a significant increase in resting HR (p = 0.006) and RR (p = 0.048), and a significant decrease in HRV (p = 0.027) and deep sleep duration (p = 0.029). She reported experiencing moderate COVID-19 symptoms and did not require hospitalization. At 38 weeks of gestation, she had a normal delivery and gave birth to a healthy infant. The participant in her second trimester showed similar physiological changes during the 3-day peak period. Importantly, these changes appeared to return to the pre-peak levels. Common symptoms reported by both cases included loss of smell and nasal congestion, with one losing her sense of taste. Results suggest the potential to use the changes in cardiorespiratory responses and sleep for real-time monitoring of health and well-being during pregnancy.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Continuous monitoring of perinatal women in a descriptive case study allowed us the opportunity to examine the time during which the COVID-19 infection led to physiological changes in two low-income pregnant women. An important component of this study was the use of a wearable sensor device, the Oura ring, to monitor and record vital physiological parameters during sleep. Two women in their second and third trimesters, respectively, were selected based on a positive COVID-19 diagnosis. Both women were tested using the polymerase chain reaction method to confirm the presence of the virus during which time we were able to collect these physiological data. In both cases, we observed 3–6 days of peak physiological changes in resting heart rate (HR), heart rate variability (HRV), and respiratory rate (RR), as well as sleep surrounding the onset of COVID-19 symptoms. The pregnant woman in her third trimester showed a significant increase in resting HR (p = 0.006) and RR (p = 0.048), and a significant decrease in HRV (p = 0.027) and deep sleep duration (p = 0.029). She reported experiencing moderate COVID-19 symptoms and did not require hospitalization. At 38 weeks of gestation, she had a normal delivery and gave birth to a healthy infant. The participant in her second trimester showed similar physiological changes during the 3-day peak period. Importantly, these changes appeared to return to the pre-peak levels. Common symptoms reported by both cases included loss of smell and nasal congestion, with one losing her sense of taste. Results suggest the potential to use the changes in cardiorespiratory responses and sleep for real-time monitoring of health and well-being during pregnancy.
52.
Le, Tai; Zhang, Jimmy; H.Nguyen, Anh; Torres, Ramses Seferino Trigo; Vo, Khuong; Dutt, Nikil; Lee, Juhyun; Ding, Yonghe; Xu, Xiaolei; Lau, Michael P. H.; HungCao,
A novel wireless ECG system for prolonged monitoring of multiple zebrafish for heart disease and drug screening studies Journal Article
In: Biosensors and Bioelectronics, 197 , 2022.
Abstract | Links | BibTeX | Tags:
@article{nokey,
title = {A novel wireless ECG system for prolonged monitoring of multiple zebrafish for heart disease and drug screening studies},
author = {Tai Le and Jimmy Zhang and Anh H.Nguyen and Ramses Seferino Trigo Torres and Khuong Vo and Nikil Dutt and Juhyun Lee and Yonghe Ding and Xiaolei Xu and Michael P.H. Lau and HungCao},
url = {https://www.sciencedirect.com/science/article/pii/S0956566321008459},
doi = {10.1016/j.bios.2021.113808},
year = {2022},
date = {2022-02-01},
journal = {Biosensors and Bioelectronics},
volume = {197},
abstract = {Zebrafish and their mutant lines have been extensively used in cardiovascular studies. In the current study, the novel system, Zebra II, is presented for prolonged electrocardiogram (ECG) acquisition and analysis for multiple zebrafish within controllable working environments. The Zebra II is composed of a perfusion system, apparatuses, sensors, and an in-house electronic system. First, the Zebra II is validated in comparison with a benchmark system, namely iWORX, through various experiments. The validation displayed comparable results in terms of data quality and ECG changes in response to drug treatment. The effects of anesthetic drugs and temperature variation on zebrafish ECG were subsequently investigated in experiments that need real-time data assessment. The Zebra II's capability of continuous anesthetic administration enabled prolonged ECG acquisition up to 1 h compared to that of 5 min in existing systems. The novel, cloud-based, automated analysis with data obtained from four fish further provided a useful solution for combinatorial experiments and helped save significant time and effort. The system showed robust ECG acquisition and analytics for various applications including arrhythmia in sodium induced sinus arrest, temperature-induced heart rate variation, and drug-induced arrhythmia in Tg(SCN5A-D1275N) mutant and wildtype fish. The multiple channel acquisition also enabled the implementation of randomized controlled trials on zebrafish models. The developed ECG system holds promise and solves current drawbacks in order to greatly accelerate drug screening applications and other cardiovascular studies using zebrafish.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Zebrafish and their mutant lines have been extensively used in cardiovascular studies. In the current study, the novel system, Zebra II, is presented for prolonged electrocardiogram (ECG) acquisition and analysis for multiple zebrafish within controllable working environments. The Zebra II is composed of a perfusion system, apparatuses, sensors, and an in-house electronic system. First, the Zebra II is validated in comparison with a benchmark system, namely iWORX, through various experiments. The validation displayed comparable results in terms of data quality and ECG changes in response to drug treatment. The effects of anesthetic drugs and temperature variation on zebrafish ECG were subsequently investigated in experiments that need real-time data assessment. The Zebra II's capability of continuous anesthetic administration enabled prolonged ECG acquisition up to 1 h compared to that of 5 min in existing systems. The novel, cloud-based, automated analysis with data obtained from four fish further provided a useful solution for combinatorial experiments and helped save significant time and effort. The system showed robust ECG acquisition and analytics for various applications including arrhythmia in sodium induced sinus arrest, temperature-induced heart rate variation, and drug-induced arrhythmia in Tg(SCN5A-D1275N) mutant and wildtype fish. The multiple channel acquisition also enabled the implementation of randomized controlled trials on zebrafish models. The developed ECG system holds promise and solves current drawbacks in order to greatly accelerate drug screening applications and other cardiovascular studies using zebrafish.
51.
Rahmani, Amir M; Lai, Jocelyn; Jafarlou, Salar; Azimi, Iman; Yunusova, Asal; Rivera, Alex; Labbaf, Sina; Anzanpour, Arman; Dutt, Nikil; Jain, Ramesh; Borelli, Jessica
Personal mental health navigator: Harnessing the power of data, personal models, and health cybernetics to promote psychological well-being Journal Article
In: Frontiers in Digital Health, 4 , 2022.
Abstract | Links | BibTeX | Tags: MHN
@article{,
title = {Personal mental health navigator: Harnessing the power of data, personal models, and health cybernetics to promote psychological well-being},
author = {Amir M Rahmani and Jocelyn Lai and Salar Jafarlou and Iman Azimi and Asal Yunusova and Alex Rivera and Sina Labbaf and Arman Anzanpour and Nikil Dutt and Ramesh Jain and Jessica Borelli},
url = {https://www.frontiersin.org/articles/10.3389/fdgth.2022.933587/full},
doi = {10.3389/fdgth.2022.933587},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
journal = {Frontiers in Digital Health},
volume = {4},
abstract = {Current digital mental healthcare solutions conventionally take on a reactive approach, requiring individuals to self-monitor and document existing symptoms. These solutions are unable to provide comprehensive, wrap-around, customized treatments that capture an individual’s holistic mental health model as it unfolds over time. Recognizing that each individual requires personally tailored mental health treatment, we introduce the notion of Personalized Mental Health Navigation (MHN): a cybernetic goal-based system that deploys a continuous loop of monitoring, estimation, and guidance to steer the individual towards mental flourishing. We present the core components of MHN that are premised on the importance of addressing an individual’s personal mental health state. Moreover, we provide an overview of the existing physical health navigation systems and highlight the requirements and challenges of deploying the navigational approach to the mental health domain.},
keywords = {MHN},
pubstate = {published},
tppubtype = {article}
}
Current digital mental healthcare solutions conventionally take on a reactive approach, requiring individuals to self-monitor and document existing symptoms. These solutions are unable to provide comprehensive, wrap-around, customized treatments that capture an individual’s holistic mental health model as it unfolds over time. Recognizing that each individual requires personally tailored mental health treatment, we introduce the notion of Personalized Mental Health Navigation (MHN): a cybernetic goal-based system that deploys a continuous loop of monitoring, estimation, and guidance to steer the individual towards mental flourishing. We present the core components of MHN that are premised on the importance of addressing an individual’s personal mental health state. Moreover, we provide an overview of the existing physical health navigation systems and highlight the requirements and challenges of deploying the navigational approach to the mental health domain.
50.
Rahmani, Amir M.; Lai, Jocelyn; Jafarlou, Salar; Azimi, Iman; Yunusova, Asal; Rivera, Alex. P.; Labbaf, Sina; Anzanpour, Arman; Dutt, Nikil D.; Jain, Ramesh C.; Borelli, Jessica L.
Personal mental health navigator: Harnessing the power of data, personal models, and health cybernetics to promote psychological well-being Journal Article
In: Frontiers Digit. Health, 4 , 2022.
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title = {Personal mental health navigator: Harnessing the power of data, personal
models, and health cybernetics to promote psychological well-being},
author = {Amir M. Rahmani and Jocelyn Lai and Salar Jafarlou and Iman Azimi and Asal Yunusova and Alex. P. Rivera and Sina Labbaf and Arman Anzanpour and Nikil D. Dutt and Ramesh C. Jain and Jessica L. Borelli},
url = {https://doi.org/10.3389/fdgth.2022.933587},
doi = {10.3389/fdgth.2022.933587},
year = {2022},
date = {2022-01-01},
journal = {Frontiers Digit. Health},
volume = {4},
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49.
Shahhosseini, Sina; Anzanpour, Arman; Azimi, Iman; Labbaf, Sina; Seo, Dongjoo; Lim, Sung-Soo; Liljeberg, Pasi; Dutt, Nikil D.; Rahmani, Amir M.
Exploring computation offloading in IoT systems Journal Article
In: Inf. Syst., 107 , pp. 101860, 2022.
@article{DBLP:journals/is/ShahhosseiniAAL22,
title = {Exploring computation offloading in IoT systems},
author = {Sina Shahhosseini and Arman Anzanpour and Iman Azimi and Sina Labbaf and Dongjoo Seo and Sung-Soo Lim and Pasi Liljeberg and Nikil D. Dutt and Amir M. Rahmani},
url = {https://doi.org/10.1016/j.is.2021.101860},
doi = {10.1016/j.is.2021.101860},
year = {2022},
date = {2022-01-01},
journal = {Inf. Syst.},
volume = {107},
pages = {101860},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
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