المؤلفون: Jesse Phipps, Bryant Passage, Kaan Sel, Jonathan Martinez, Milad Saadat, Teddy Koker, Natalie Damaso, Shakti Davis, Jeffrey Palmer, Kajal Claypool, Christopher Kiley, Roderic I. Pettigrew, Roozbeh Jafari

المصدر: npj Digital Medicine, Vol 7, Iss 1, Pp 1-13 (2024)

مصطلحات موضوعية: Computer applications to medicine. Medical informatics, R858-859.7

الوصف: Abstract Data from commercial off-the-shelf (COTS) wearables leveraged with machine learning algorithms provide an unprecedented potential for the early detection of adverse physiological events. However, several challenges inhibit this potential, including (1) heterogeneity among and within participants that make scaling detection algorithms to a general population less precise, (2) confounders that lead to incorrect assumptions regarding a participant’s healthy state, (3) noise in the data at the sensor level that limits the sensitivity of detection algorithms, and (4) imprecision in self-reported labels that misrepresent the true data values associated with a given physiological event. The goal of this study was two-fold: (1) to characterize the performance of such algorithms in the presence of these challenges and provide insights to researchers on limitations and opportunities, and (2) to subsequently devise algorithms to address each challenge and offer insights on future opportunities for advancement. Our proposed algorithms include techniques that build on determining suitable baselines for each participant to capture important physiological changes and label correction techniques as it pertains to participant-reported identifiers. Our work is validated on potentially one of the largest datasets available, obtained with 8000+ participants and 1.3+ million hours of wearable data captured from Oura smart rings. Leveraging this extensive dataset, we achieve pre-symptomatic detection of COVID-19 with a performance receiver operator characteristic (ROC) area under the curve (AUC) of 0.725 without correction techniques, 0.739 with baseline correction, 0.740 with baseline correction and label correction on the training set, and 0.777 with baseline correction and label correction on both the training and the test set. Using the same respective paradigms, we achieve ROC AUCs of 0.919, 0.938, 0.943 and 0.994 for the detection of self-reported fever, and 0.574, 0.611, 0.601, and 0.635 for detection of self-reported shortness of breath. These techniques offer improvements across almost all metrics and events, including PR AUC, sensitivity at 75% specificity, and precision at 75% recall. The ring allows continuous monitoring for detection of event onset, and we further demonstrate an improvement in the early detection of COVID-19 from an average of 3.5 days to an average of 4.1 days before a reported positive test result.

وصف الملف: electronic resource

العلاقة: https://doaj.org/toc/2398-6352Test

الوصول الحر: https://doaj.org/article/85d5c684c49d4f8bbbd581cf64b1a736Test

المؤلفون: Kaan Sel, Amirmohammad Mohammadi, Roderic I. Pettigrew, Roozbeh Jafari

المصدر: npj Digital Medicine, Vol 6, Iss 1, Pp 1-15 (2023)

مصطلحات موضوعية: Computer applications to medicine. Medical informatics, R858-859.7

الوصف: Abstract The bold vision of AI-driven pervasive physiological monitoring, through the proliferation of off-the-shelf wearables that began a decade ago, has created immense opportunities to extract actionable information for precision medicine. These AI algorithms model input-output relationships of a system that, in many cases, exhibits complex nature and personalization requirements. A particular example is cuffless blood pressure estimation using wearable bioimpedance. However, these algorithms need training over significant amount of ground truth data. In the context of biomedical applications, collecting ground truth data, particularly at the personalized level is challenging, burdensome, and in some cases infeasible. Our objective is to establish physics-informed neural network (PINN) models for physiological time series data that would use minimal ground truth information to extract complex cardiovascular information. We achieve this by building Taylor’s approximation for gradually changing known cardiovascular relationships between input and output (e.g., sensor measurements to blood pressure) and incorporating this approximation into our proposed neural network training. The effectiveness of the framework is demonstrated through a case study: continuous cuffless BP estimation from time series bioimpedance data. We show that by using PINNs over the state-of-the-art time series models tested on the same datasets, we retain high correlations (systolic: 0.90, diastolic: 0.89) and low error (systolic: 1.3 ± 7.6 mmHg, diastolic: 0.6 ± 6.4 mmHg) while reducing the amount of ground truth training data on average by a factor of 15. This could be helpful in developing future AI algorithms to help interpret pervasive physiologic data using minimal amount of training data.

وصف الملف: electronic resource

العلاقة: https://doaj.org/toc/2398-6352Test

الوصول الحر: https://doaj.org/article/0b85354e36e446eeab9ab9c5e0f7b4bcTest

المؤلفون: Kaan Sel, Deen Osman, Noah Huerta, Arabella Edgar, Roderic I. Pettigrew, Roozbeh Jafari

المصدر: npj Digital Medicine, Vol 6, Iss 1, Pp 1-11 (2023)

مصطلحات موضوعية: Computer applications to medicine. Medical informatics, R858-859.7

الوصف: Abstract Smart rings provide unique opportunities for continuous physiological measurement. They are easy to wear, provide little burden in comparison to other smart wearables, are suitable for nocturnal settings, and can be sized to provide ideal contact between the sensors and the skin at all times. Continuous measuring of blood pressure (BP) provides essential diagnostic and prognostic value for cardiovascular health management. However, conventional ambulatory BP measurement devices operate using an inflating cuff that is bulky, intrusive, and impractical for frequent or continuous measurements. We introduce ring-shaped bioimpedance sensors leveraging the deep tissue sensing ability of bioimpedance while introducing no sensitivity to skin tones, unlike optical modalities. We integrate unique human finger finite element model with exhaustive experimental data from participants and derive optimum design parameters for electrode placement and sizes that yields highest sensitivity to arterial volumetric changes, with no discrimination against varying skin tones. BP is constructed using machine learning algorithms. The ring sensors are used to estimate arterial BP showing peak correlations of 0.81, and low error (systolic BP: 0.11 ± 5.27 mmHg, diastolic BP: 0.11 ± 3.87 mmHg) for >2000 data points and wide BP ranges (systolic: 89–213 mmHg and diastolic: 42–122 mmHg), highlighting the significant potential use of bioimpedance ring for accurate and continuous estimation of BP.

وصف الملف: electronic resource

العلاقة: https://doaj.org/toc/2398-6352Test

الوصول الحر: https://doaj.org/article/2c0e155f6f334e2294e78f8ccd9819e2Test

المؤلفون: Hongwoo Jang, Kaan Sel, Eunbin Kim, Sangjun Kim, Xiangxing Yang, Seungmin Kang, Kyoung-Ho Ha, Rebecca Wang, Yifan Rao, Roozbeh Jafari, Nanshu Lu

المصدر: Nature Communications, Vol 13, Iss 1, Pp 1-13 (2022)

مصطلحات موضوعية: Science

الوصف: Designing efficient sensing devices for ambulatory use remains a challenge. Here, the authors demonstrate heterogeneous serpentine ribbons enable a stretchable and robust interface between sub-micron thin graphene e-tattoos and thick and rigid printed circuit boards, which allows ambulatory electrodermal activity monitoring on the palm.

وصف الملف: electronic resource

العلاقة: https://doaj.org/toc/2041-1723Test

الوصول الحر: https://doaj.org/article/516b32b4cb0c44fe8ad193d41c5d215fTest

المؤلفون: Jonathan Martinez, Kaan Sel, Bobak J. Mortazavi, Roozbeh Jafari

المصدر: IEEE Open Journal of Engineering in Medicine and Biology, Vol 3, Pp 78-85 (2022)

مصطلحات موضوعية: Dynamic time warping, fiducial point, photoplethysmography, interbeat intervals, wearable sensors, Computer applications to medicine. Medical informatics, R858-859.7, Medical technology, R855-855.5

الوصف: Goal: To achieve high-quality comprehensive feature extraction from physiological signals that enables precise physiological parameter estimation despite evolving waveform morphologies. Methods: We propose Boosted-SpringDTW, a probabilistic framework that leverages dynamic time warping (DTW) and minimal domain-specific heuristics to simultaneously segment physiological signals and identify fiducial points that represent cardiac events. An automated dynamic template adapts to evolving waveform morphologies. We validate Boosted-SpringDTW performance with a benchmark PPG dataset whose morphologies include subject- and respiratory-induced variation. Results: Boosted-SpringDTW achieves precision, recall, and F1-scores over 0.96 for identifying fiducial points and mean absolute error values less than 11.41 milliseconds when estimating IBI. Conclusion: Boosted-SpringDTW improves F1-Scores compared to two baseline feature extraction algorithms by 35% on average for fiducial point identification and mean percent difference by 16% on average for IBI estimation. Significance: Precise hemodynamic parameter estimation with wearable devices enables continuous health monitoring throughout a patients’ daily life.

وصف الملف: electronic resource

العلاقة: https://ieeexplore.ieee.org/document/9774024Test/; https://doaj.org/toc/2644-1276Test

الوصول الحر: https://doaj.org/article/f3af6b65df414b6b81f50cb676695da5Test

المؤلفون: Kaan Sel, Deen Osman, Roozbeh Jafari

المصدر: IEEE Open Journal of Engineering in Medicine and Biology, Vol 2, Pp 210-217 (2021)

مصطلحات موضوعية: Bioimpedance, hemodynamics, impedance cardiography, non-invasive, respiration, Computer applications to medicine. Medical informatics, R858-859.7, Medical technology, R855-855.5

الوصف: Objective: Bioimpedance sensing is a powerful technique that measures the tissue impedance and captures important physiological parameters including blood flow, lung movements, muscle contractions, body fluid shifts, and other cardiovascular parameters. This paper presents a comprehensive analysis of the modality at different arterial (ulnar, radial, tibial, and carotid arteries) and thoracic (side-rib cage and top thoracolumbar fascia) body regions and offers insights into the effectiveness of capturing various cardiac and respiratory activities. Methods: We assess the bioimpedance performance in estimating inter-beat (IBI) and inter -breath intervals (IBrI) on six-hours of data acquired in a pilot-study from five healthy participants at rest. Results: Overall, we achieve mean errors as low as 0.003 ± 0.002 and 0.67 ± 0.28 seconds for IBI and IBrI estimations, respectively. Conclusions: The results show that bioimpedance can be effectively used to monitor cardiac and respiratory activities both at limbs and upper body and demonstrate a strong potential to be adopted by wearables that aim to provide high-fidelity physiological sensing to address precision medicine needs.

وصف الملف: electronic resource

العلاقة: https://ieeexplore.ieee.org/document/9444791Test/; https://doaj.org/toc/2644-1276Test

الوصول الحر: https://doaj.org/article/cc717dc77d4546dfa9670b4179536f2fTest

المؤلفون: Roozbeh Jafari, Kaan Sel, Amirmohammad Mohammadi, Roderic Pettigrew

الوصف: The bold vision of AI-driven pervasive physiological monitoring, through the proliferation of off-the-shelf wearables that began a decade ago, has created immense opportunities to extract actionable information for precision medicine. These AI algorithms model the input-output relationships of a system that, in many cases, exhibits complex nature and personalization requirements. A particular example is cuffless blood pressure estimation using wearable bioimpedance. However, these algorithms need to be trained with a significant amount of ground truth data. In the context of biomedical applications, collecting ground truth data, particularly at the personalized level is challenging, burdensome, and in some cases infeasible. Our objective is to establish physics-informed neural network (PINN) models for physiological time series data that would reduce reliance on ground truth information. We achieve this by building Taylor's approximation for the gradually changing known cardiovascular relationships between input and output (e.g., sensor measurements to blood pressure) and incorporating this approximation into our proposed neural network training. The effectiveness of the framework is demonstrated through a case study: continuous cuffless BP estimation from time series bioimpedance data. We show that by using PINNs over the state-of-the-art time series regression models tested on the same datasets, we retain a high correlation (systolic: 0.90, diastolic: 0.89) and low error (systolic: 1.3 ± 7.6 mmHg, diastolic: 0.6 ± 6.4 mmHg) while reducing the amount of ground truth training data on average by a factor of 15. This could be helpful in developing future AI algorithms to help interpret pervasive physiologic data using minimal amount of training data.

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_________::dd749201e533b37fc51f787e420d20caTest
https://doi.org/10.21203/rs.3.rs-2423200/v1Test

المؤلفون: Jesse F. Phipps, Kaan Sel, Roozbeh Jafari

المصدر: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference. 2022

مصطلحات موضوعية: Wrist Joint, Wrist, Electrodes, Monitoring, Physiologic

الوصف: Bioimpedance has emerged as a promising modality to continuously monitor hemodynamic and respiratory physiological parameters through a non-invasive skin-contact approach. Bioimpedance sensors placed at the radial zone of the volar wrist provide sensitive operation to the blood flow of the underlying radial artery. The translation of bioimpedance systems into medical-grade settings for continuous hemodynamic monitoring, however, presents challenges when constraining the necessary sensing components to a minimal form factor while maintaining sufficient accuracy and precision of measurements. Thus, it is important to understand the effects of electrode configuration on bioimpedance signals when reducing them to a wearable form factor. Previous work regarding electrode configurations in bioimpedance does not address wearable constraints, nor do they focus on electrodes viable for wearable applications. In this study, we present empirical evidence of the effects of dry silver electrode sizes and spacings on the specificity and sensitivity of a wrist-worn bioimpedance sensor array. We found that wrist-worn bioimpedance systems for hemodynamic monitoring would benefit from reduced injection electrode spacings (up to a 392% increase in signal amplitude with a 50% decrease in spacing), increased sensing electrode spacings, and decreased electrode surface areas. Clinical Relevance - The work directly contributes towards the development of cuffless continuous blood pressure monitors with applications in clinical and ambulatory settings.

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e7fb171b258e0aa4ab49951132b7f11eTest
https://pubmed.ncbi.nlm.nih.gov/36085964Test

المؤلفون: Deen, Osman, Matija, Jankovic, Kaan, Sel, Roderic I, Pettigrew, Roozbeh, Jafari

المصدر: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC).

مصطلحات موضوعية: Electric Impedance, Humans, Blood Pressure, Blood Pressure Determination, Pulse Wave Analysis, Photoplethysmography

الوصف: The demand for non-obtrusive, accurate, and continuous blood pressure (BP) monitoring systems is becoming more prevalent with the realization of its significance in preventable cardiovascular disease (CVD) globally. Current cuff-based standards are bulky, uncomfortable, and are limited to discrete recording periods. Wearable sensor technologies such as those using optical photoplethysmography (PPG) have been used to develop blood pressure estimation models through a variety of methods. However, this technology falls short as optical based systems have bias favoring lighter skin tones and lower body fat compositions. Bioimpedance (Bio-Z) is a capable modality of sensing arterial blood flow without implicit inadvertent bias towards individuals. In this paper we propose a ring-based bioimpedance system to capture arterial blood flow from the digital artery of the finger. The ring design provides a more compact wearable device utilizing only a single Bio-Z channel, making it a familiar fit to individuals. Post-processing the acquired Bio-Z signals, we extracted 9 frequency domain features from windowed beat cycles to train subject specific regression models. Results indicate the average mean absolute errors for systolic/diastolic BP to be 4.38/3.63mmHg, consistent with AAMI standards.

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::058a8afb16db2287dda2c375357471c5Test
https://doi.org/10.1109/embc48229.2022.9871653Test

المؤلفون: Kaan Sel, Noah Huerta, Michael S. Sacks, Roozbeh Jafari

المصدر: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_________::a6d5267188e6fdc89e97357417365033Test
https://doi.org/10.1109/icassp43922.2022.9747695Test