Optical wrist-worn device for sleep monitoring

Renevey, Philippe; Delgado-Gonzalo, Ricard; Lemkaddem, Alia; Proença, Martin; Lemay, Mathieu; Solà, Josep; Tarniceriu, Adrian; Bertschi, Mattia

doi:10.1007/978-981-10-5122-7_154

Cited by 26 publications

(16 citation statements)

References 5 publications

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“…It is deployed in most of the mainstream wearable devices and smart phones. Studies confirm the accuracy of PPG-based solutions to monitor various vital signs, such as heart rate (HR) and heart rate variability (HRV) 8,9 , stress levels 10 , irregularity in heart beats 11,12 , sleep quality 13 , respiratory rate 14 , etc. For BP monitoring, PPG-based methods rely on either pulse wave velocity (PWV) techniques 15,16 or pulse wave analysis (PWA) methods 17,18 .…”

Section: Introductionmentioning

confidence: 80%

The Accuracy of Blood Pressure Monitoring Using the Senbiosys Ring: A Study on Patients Undergoing Coronary Angiography and Patients in the Intensive Care Unit

Haddad¹,

Schukraft

Boukhayma³

et al. 2021

Preprint

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The study NCT04379986 aims at evaluating the performance of the Senbiosys technology in monitoring blood pressure (BP) values for elderly patients: 25 patients undergoing coronary angiography, aged 68.92 ± 6.41 years, and 10 patients in the intensive care unit (ICU), aged 71.90 ± 8.56 years. The technology is based on pulse wave analysis (PWA) of low-power photoplethysmography (PPG) from the SBF2003 ring. The PPG recordings are processed by the PPG-based blood pressure monitoring (PPG-BPM) algorithm 1 to generate systolic BP (SBP) and diastolic BP (DBP) estimates. For the SBP, the mean difference ± standard deviation between the reference arterial line and the PPG-BPM values is 0 ± 5.06 mmHg for the patients undergoing coronary angiography and 0 ± 7.46 mmHg for the patients in the ICU. For the DBP, the mean difference ± standard deviation between the reference arterial line and the PPG-BPM values is 0 ± 1.85 mmHg for the patients undergoing coronary angiography and 0 ± 4.17 mmHg for the patients in the ICU. The results show that the accuracy of our algorithm is within the 5 ± 8 mmHg ISO/ANSI/AAMI protocol requirement.

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Section: Introductionmentioning

confidence: 80%

The Accuracy of Blood Pressure Monitoring Using the Senbiosys Ring: A Study on Patients Undergoing Coronary Angiography and Patients in the Intensive Care Unit

Haddad¹,

Schukraft

Boukhayma³

et al. 2021

Preprint

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show abstract

“…It is already available in many wearable devices and smart phones. Many studies confirm the accuracy of PPG-based solutions to monitor heart rate (HR) and heart rate variability (HRV) [6], [7], stress levels [8], [9], irregularity in heart beats [10], [11], sleep quality [12], [13], etc. For BP monitoring, PPG-based methods rely on either pulse wave velocity (PWV) techniques or pulse wave analysis (PWA) methods.…”

Section: Ppg-based Methodsmentioning

confidence: 99%

Continuous PPG-Based Blood Pressure Monitoring Using Multi-Linear Regression

Haddad,

Boukhayma,

Caizzone

2020

Preprint

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In this work, we present a photoplethysmographybased blood pressure monitoring algorithm (PPG-BPM) that solely requires a photoplethysmography (PPG) signal. The technology is based on pulse wave analysis (PWA) of PPG signals retrieved from different body locations to continuously estimate the systolic blood pressure (SBP) and the diastolic blood pressure (DBP). The proposed algorithm extracts morphological features from the PPG signal and maps them to SBP and DBP values using a multiple linear regression (MLR) model. The performance of the algorithm is evaluated on the publicly available Multiparameter Intelligent Monitoring in Intensive Care (MIMIC I) database. We utilize 28 data-sets (records) from the MIMIC I database that contain both PPG and brachial arterial blood pressure (ABP) signals. The collected PPG and ABP signals are synchronized and divided into intervals of 30 seconds, called epochs. In total, we utilize 47153 clean 30-second epochs for the performance analysis. Out of the 28 data-sets, we use only 2 data-sets (records 041 and 427 in the MIMIC I) with a total of 2677 clean 30second epochs to build the MLR model of the algorithm. For the SBP, a mean difference (± standard deviation) of 0.0 ± 8.01 mmHg and a mean absolute error (MAE) of 6.10 mmHg between the arterial line and the PPG-based values are achieved, with a Pearson correlation coefficient r = 0.90, p < .001. For the DBP, a mean difference of 0.0±6.22 mmHg and an MAE of 4.65 mmHg between the arterial line and the PPG-based values are achieved, with a Pearson correlation coefficient r = 0.85, p < .001. We also use a binary classifier for the BP values with the positives indicating SBP ≥ 130 mmHg and/or DBP ≥ 80 mmHg and the negatives indicating otherwise. The classifier results generated by the PPG-based SBP and DBP estimates achieve a sensitivity and a specificity of 79.11% and 92.37%, respectively.

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“…Sleep staging has been investigated in different complexity in home environments focusing on various problems: wake-sleep classification [17,21,36,37,27,18], REM and NREM [31,5,7], 4 Stages, i.e., wake, REM, light and deep sleep [39] or 5 stages [16, 38] [9]. Overall, methods usually deploy PSG as ground truth where a score is given ever 30-seconds.…”

Section: Related Workmentioning

confidence: 99%

Enhanced multi-source data analysis for personalized sleep-wake pattern recognition and sleep parameter extraction

Fallmann

Chen

2020

Pers Ubiquit Comput

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Sleep behavior is traditionally monitored with polysomnography, and sleep stage patterns are a key marker for sleep quality used to detect anomalies and diagnose diseases. With the growing demand for personalized healthcare and the prevalence of the Internet of Things, there is a trend to use everyday technologies for sleep behavior analysis at home, having the potential to eliminate expensive in-hospital monitoring. In this paper, we conceived a multi-source data mining approach to personalized sleep-wake pattern recognition which uses physiological data and personal information to facilitate fine-grained detection. Physiological data includes actigraphy and heart rate variability and personal data makes use of gender, health status and race information which are known influence factors. Moreover, we developed a personalized sleep parameter extraction technique fused with the sleep-wake approach, achieving personalized instead of static thresholds for decision-making. Results show that the proposed approach improves the accuracy of sleep and wake stage recognition, therefore, offers a new solution for personalized sleep-based health monitoring.

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Optical wrist-worn device for sleep monitoring

Cited by 26 publications

References 5 publications

The Accuracy of Blood Pressure Monitoring Using the Senbiosys Ring: A Study on Patients Undergoing Coronary Angiography and Patients in the Intensive Care Unit

The Accuracy of Blood Pressure Monitoring Using the Senbiosys Ring: A Study on Patients Undergoing Coronary Angiography and Patients in the Intensive Care Unit

Continuous PPG-Based Blood Pressure Monitoring Using Multi-Linear Regression

Enhanced multi-source data analysis for personalized sleep-wake pattern recognition and sleep parameter extraction

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