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Dankovich LJ, Joyner JS, He W, Sesay A, Vaughn-Cooke M. CogWatch: An open-source platform to monitor physiological indicators for cognitive workload and stress. HARDWAREX 2024; 19:e00538. [PMID: 38962730 PMCID: PMC11220525 DOI: 10.1016/j.ohx.2024.e00538] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/04/2024] [Revised: 04/28/2024] [Accepted: 05/14/2024] [Indexed: 07/05/2024]
Abstract
Cognitive workload is a measure of the mental resources a user is dedicating to a given task. Low cognitive workload produces boredom and decreased vigilance, which can lead to an increase in response time. Under high cognitive workload the information processing burden of the user increases significantly, thereby compromising the ability to effectively monitor their environment for unexpected stimuli or respond to emergencies. In cognitive workload and stress monitoring research, sensors are used to measure applicable physiological indicators to infer the state of user. For example, electrocardiography or photoplethysmography are often used to track both the rate at which the heart beats and variability between the individual heart beats. Photoplethysmography and chest straps are also used in studies to track fluctuations in breathing rate. The Galvanic Skin Response is a change in sweat rate (especially on the palms and wrists) and is typically measured by tracking how the resistance of two probes at a fixed distance on the subject's skin changes over time. Finally, fluctuations in Skin Temperature are typically tracked with thermocouples or infrared light (IR) measuring systems in these experiments. While consumer options such a smartwatches for health tracking often have the integrated ability to perform photoplethysmography, they typically perform significant processing on the data which is not transparent to the user and often have a granularity of data that is far too low to be useful for research purposes. It is possible to purchase sensor boards that can be added to Arduino systems, however, these systems generally are very large and obtrusive. Additionally, at the high end of the spectrum there are medical tools used to track these physiological signals, but they are often very expensive and require specific software to be licensed for communication. In this paper, an open-source solution to create a physiological tracker with a wristwatch form factor is presented and validated, using conventional off-the-shelf components. The proposed tool is intended to be applied as a cost-effective solution for research and educational settings.
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Affiliation(s)
- Louis J. Dankovich
- University of Maryland at College Park, James A. Clark School of Engineering, 8228 Paint Branch Dr, College Park, MD 20742, United States
| | - Janell S. Joyner
- University of Maryland at College Park, James A. Clark School of Engineering, 8228 Paint Branch Dr, College Park, MD 20742, United States
| | - William He
- University of Maryland at College Park, James A. Clark School of Engineering, 8228 Paint Branch Dr, College Park, MD 20742, United States
| | - Ahmad Sesay
- University of Maryland at College Park, James A. Clark School of Engineering, 8228 Paint Branch Dr, College Park, MD 20742, United States
| | - Monifa Vaughn-Cooke
- Virginia Tech, VT Carilion School of Medicine, 2 Riverside Circle, Roanoke, VA 24016, United States
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2
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Burma JS, Griffiths JK, Lapointe AP, Oni IK, Soroush A, Carere J, Smirl JD, Dunn JF. Heart Rate Variability and Pulse Rate Variability: Do Anatomical Location and Sampling Rate Matter? SENSORS (BASEL, SWITZERLAND) 2024; 24:2048. [PMID: 38610260 PMCID: PMC11013825 DOI: 10.3390/s24072048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Revised: 03/16/2024] [Accepted: 03/21/2024] [Indexed: 04/14/2024]
Abstract
Wearable technology and neuroimaging equipment using photoplethysmography (PPG) have become increasingly popularized in recent years. Several investigations deriving pulse rate variability (PRV) from PPG have demonstrated that a slight bias exists compared to concurrent heart rate variability (HRV) estimates. PPG devices commonly sample at ~20-100 Hz, where the minimum sampling frequency to derive valid PRV metrics is unknown. Further, due to different autonomic innervation, it is unknown if PRV metrics are harmonious between the cerebral and peripheral vasculature. Cardiac activity via electrocardiography (ECG) and PPG were obtained concurrently in 54 participants (29 females) in an upright orthostatic position. PPG data were collected at three anatomical locations: left third phalanx, middle cerebral artery, and posterior cerebral artery using a Finapres NOVA device and transcranial Doppler ultrasound. Data were sampled for five minutes at 1000 Hz and downsampled to frequencies ranging from 20 to 500 Hz. HRV (via ECG) and PRV (via PPG) were quantified and compared at 1000 Hz using Bland-Altman plots and coefficient of variation (CoV). A sampling frequency of ~100-200 Hz was required to produce PRV metrics with a bias of less than 2%, while a sampling rate of ~40-50 Hz elicited a bias smaller than 20%. At 1000 Hz, time- and frequency-domain PRV measures were slightly elevated compared to those derived from HRV (mean bias: ~1-8%). In conjunction with previous reports, PRV and HRV were not surrogate biomarkers due to the different nature of the collected waveforms. Nevertheless, PRV estimates displayed greater validity at a lower sampling rate compared to HRV estimates.
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Affiliation(s)
- Joel S. Burma
- Cerebrovascular Concussion Laboratory, Faculty of Kinesiology, University of Calgary, Calgary, AB T2N 1N4, Canada; (J.K.G.); (J.C.); (J.D.S.)
- Libin Cardiovascular Institute of Alberta, University of Calgary, Calgary, AB T2N 1N4, Canada
- Alberta Children’s Hospital Research Institute, University of Calgary, Calgary, AB T2N 1N4, Canada; (I.K.O.); (A.S.); (J.F.D.)
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB T2N 1N4, Canada
- Integrated Concussion Research Program, University of Calgary, Calgary, AB T2N 1N4, Canada
- Sport Injury Prevention Research Centre, Faculty of Kinesiology, University of Calgary, Calgary, AB T2N 1N4, Canada
- Human Performance Laboratory, Faculty of Kinesiology, University of Calgary, Calgary, AB T2N 1N4, Canada
| | - James K. Griffiths
- Cerebrovascular Concussion Laboratory, Faculty of Kinesiology, University of Calgary, Calgary, AB T2N 1N4, Canada; (J.K.G.); (J.C.); (J.D.S.)
- Faculty of Biomedical Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada
| | | | - Ibukunoluwa K. Oni
- Alberta Children’s Hospital Research Institute, University of Calgary, Calgary, AB T2N 1N4, Canada; (I.K.O.); (A.S.); (J.F.D.)
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB T2N 1N4, Canada
- Integrated Concussion Research Program, University of Calgary, Calgary, AB T2N 1N4, Canada
- Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 1N4, Canada
| | - Ateyeh Soroush
- Alberta Children’s Hospital Research Institute, University of Calgary, Calgary, AB T2N 1N4, Canada; (I.K.O.); (A.S.); (J.F.D.)
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB T2N 1N4, Canada
- Integrated Concussion Research Program, University of Calgary, Calgary, AB T2N 1N4, Canada
- Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 1N4, Canada
| | - Joseph Carere
- Cerebrovascular Concussion Laboratory, Faculty of Kinesiology, University of Calgary, Calgary, AB T2N 1N4, Canada; (J.K.G.); (J.C.); (J.D.S.)
- Libin Cardiovascular Institute of Alberta, University of Calgary, Calgary, AB T2N 1N4, Canada
- Alberta Children’s Hospital Research Institute, University of Calgary, Calgary, AB T2N 1N4, Canada; (I.K.O.); (A.S.); (J.F.D.)
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB T2N 1N4, Canada
- Integrated Concussion Research Program, University of Calgary, Calgary, AB T2N 1N4, Canada
- Sport Injury Prevention Research Centre, Faculty of Kinesiology, University of Calgary, Calgary, AB T2N 1N4, Canada
- Human Performance Laboratory, Faculty of Kinesiology, University of Calgary, Calgary, AB T2N 1N4, Canada
| | - Jonathan D. Smirl
- Cerebrovascular Concussion Laboratory, Faculty of Kinesiology, University of Calgary, Calgary, AB T2N 1N4, Canada; (J.K.G.); (J.C.); (J.D.S.)
- Libin Cardiovascular Institute of Alberta, University of Calgary, Calgary, AB T2N 1N4, Canada
- Alberta Children’s Hospital Research Institute, University of Calgary, Calgary, AB T2N 1N4, Canada; (I.K.O.); (A.S.); (J.F.D.)
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB T2N 1N4, Canada
- Integrated Concussion Research Program, University of Calgary, Calgary, AB T2N 1N4, Canada
- Sport Injury Prevention Research Centre, Faculty of Kinesiology, University of Calgary, Calgary, AB T2N 1N4, Canada
- Human Performance Laboratory, Faculty of Kinesiology, University of Calgary, Calgary, AB T2N 1N4, Canada
| | - Jeff F. Dunn
- Alberta Children’s Hospital Research Institute, University of Calgary, Calgary, AB T2N 1N4, Canada; (I.K.O.); (A.S.); (J.F.D.)
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB T2N 1N4, Canada
- Integrated Concussion Research Program, University of Calgary, Calgary, AB T2N 1N4, Canada
- Faculty of Biomedical Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada
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3
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Montree RJH, Peri E, Haakma R, Dekker LRC, Vullings R. Increasing accuracy of pulse arrival time estimation in low frequency recordings. Physiol Meas 2024; 45:03NT01. [PMID: 38387047 DOI: 10.1088/1361-6579/ad2c12] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Accepted: 02/22/2024] [Indexed: 02/24/2024]
Abstract
Objective.Wearable devices that measure vital signals using photoplethysmography are becoming more commonplace. To reduce battery consumption, computational complexity, memory footprint or transmission bandwidth, companies of commercial wearable technologies are often looking to minimize the sampling frequency of the measured vital signals. One such vital signal of interest is the pulse arrival time (PAT), which is an indicator of blood pressure. To leverage this non-invasive and non-intrusive measurement data for use in clinical decision making, the accuracy of obtained PAT-parameters needs to increase in lower sampling frequency recordings. The aim of this paper is to develop a new strategy to estimate PAT at sampling frequencies up to 25 Hertz.Approach.The method applies template matching to leverage the random nature of sampling time and expected change in the PAT.Main results.The algorithm was tested on a publicly available dataset from 22 healthy volunteers, under sitting, walking and running conditions. The method significantly reduces both the mean and the standard deviation of the error when going to lower sampling frequencies by an average of 16.6% and 20.2%, respectively. Looking only at the sitting position, this reduction is even larger, increasing to an average of 22.2% and 48.8%, respectively.Significance.This new method shows promise in allowing more accurate estimation of PAT even in lower frequency recordings.
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Affiliation(s)
- Roel J H Montree
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Elisabetta Peri
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Reinder Haakma
- Department of Patient Care & Monitoring, Philips Research, Eindhoven, The Netherlands
| | - Lukas R C Dekker
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
- Catharina Hospital Eindhoven, Eindhoven, The Netherlands
| | - Rik Vullings
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
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Lu L, Zhu T, Morelli D, Creagh A, Liu Z, Yang J, Liu F, Zhang YT, Clifton DA. Uncertainties in the Analysis of Heart Rate Variability: A Systematic Review. IEEE Rev Biomed Eng 2024; 17:180-196. [PMID: 37186539 DOI: 10.1109/rbme.2023.3271595] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/17/2023]
Abstract
Heart rate variability (HRV) is an important metric with a variety of applications in clinical situations such as cardiovascular diseases, diabetes mellitus, and mental health. HRV data can be potentially obtained from electrocardiography and photoplethysmography signals, then computational techniques such as signal filtering and data segmentation are used to process the sampled data for calculating HRV measures. However, uncertainties arising from data acquisition, computational models, and physiological factors can lead to degraded signal quality and affect HRV analysis. Therefore, it is crucial to address these uncertainties and develop advanced models for HRV analysis. Although several reviews of HRV analysis exist, they primarily focus on clinical applications, trends in HRV methods, or specific aspects of uncertainties such as measurement noise. This paper provides a comprehensive review of uncertainties in HRV analysis, quantifies their impacts, and outlines potential solutions. To the best of our knowledge, this is the first study that presents a holistic review of uncertainties in HRV methods and quantifies their impacts on HRV measures from an engineer's perspective. This review is essential for developing robust and reliable models, and could serve as a valuable future reference in the field, particularly for dealing with uncertainties in HRV analysis.
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5
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Sato S, Hiratsuka T, Hasegawa K, Watanabe K, Obara Y, Kariya N, Shinba T, Matsui T. Screening for Major Depressive Disorder Using a Wearable Ultra-Short-Term HRV Monitor and Signal Quality Indices. SENSORS (BASEL, SWITZERLAND) 2023; 23:3867. [PMID: 37112208 PMCID: PMC10143236 DOI: 10.3390/s23083867] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 04/06/2023] [Accepted: 04/07/2023] [Indexed: 06/19/2023]
Abstract
To encourage potential major depressive disorder (MDD) patients to attend diagnostic sessions, we developed a novel MDD screening system based on sleep-induced autonomic nervous responses. The proposed method only requires a wristwatch device to be worn for 24 h. We evaluated heart rate variability (HRV) via wrist photoplethysmography (PPG). However, previous studies have indicated that HRV measurements obtained using wearable devices are susceptible to motion artifacts. We propose a novel method to improve screening accuracy by removing unreliable HRV data (identified on the basis of signal quality indices (SQIs) obtained by PPG sensors). The proposed algorithm enables real-time calculation of signal quality indices in the frequency domain (SQI-FD). A clinical study conducted at Maynds Tower Mental Clinic enrolled 40 MDD patients (mean age, 37.5 ± 8.8 years) diagnosed on the basis of the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, and 29 healthy volunteers (mean age, 31.9 ± 13.0 years). Acceleration data were used to identify sleep states, and a linear classification model was trained and tested using HRV and pulse rate data. Ten-fold cross-validation showed a sensitivity of 87.3% (80.3% without SQI-FD data) and specificity of 84.0% (73.3% without SQI-FD data). Thus, SQI-FD drastically improved sensitivity and specificity.
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Affiliation(s)
- Shohei Sato
- Department of Electrical Engineering and Computer Science, Faculty of Systems Design, Tokyo Metropolitan University, Tokyo 191-0065, Japan
| | - Takuma Hiratsuka
- Department of Electrical Engineering and Computer Science, Faculty of Systems Design, Tokyo Metropolitan University, Tokyo 191-0065, Japan
| | - Kenya Hasegawa
- Department of Electrical Engineering and Computer Science, Faculty of Systems Design, Tokyo Metropolitan University, Tokyo 191-0065, Japan
| | - Keisuke Watanabe
- Department of Electrical Engineering and Computer Science, Faculty of Systems Design, Tokyo Metropolitan University, Tokyo 191-0065, Japan
| | - Yusuke Obara
- Maynds Tower Mental Clinic, Tokyo 151-0053, Japan
| | | | - Toshikazu Shinba
- Department of Psychiatry, Shizuoka Saiseikai General Hospital, Shizuoka 422-8527, Japan
- Research Division, Saiseikai Research Institute of Health Care and Welfare, Tokyo 108-0073, Japan
| | - Takemi Matsui
- Department of Electrical Engineering and Computer Science, Graduate School of System Design, Tokyo Metropolitan University, Tokyo 191-0065, Japan
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6
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Chen Y, Zhou E, Wang Y, Wu Y, Xu G, Chen L. The past, present, and future of sleep quality assessment and monitoring. Brain Res 2023; 1810:148333. [PMID: 36931581 DOI: 10.1016/j.brainres.2023.148333] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Revised: 03/09/2023] [Accepted: 03/12/2023] [Indexed: 03/17/2023]
Abstract
Sleep quality is considered to be an individual's self-satisfaction with all aspects of the sleep experience. Good sleep not only improves a person's physical, mental and daily functional health, but also improves the quality-of-life level to some extent. In contrast, chronic sleep deprivation can increase the risk of diseases such as cardiovascular diseases, metabolic dysfunction and cognitive and emotional dysfunction, and can even lead to increased mortality. The scientific evaluation and monitoring of sleep quality is an important prerequisite for safeguarding and promoting the physiological health of the body. Therefore, we have compiled and reviewed the existing methods and emerging technologies commonly used for subjective and objective evaluation and monitoring of sleep quality, and found that subjective sleep evaluation is suitable for clinical screening and large-scale studies, while objective evaluation results are more intuitive and scientific, and in the comprehensive evaluation of sleep, if we want to get more scientific monitoring results, we should combine subjective and objective monitoring and dynamic monitoring.
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Affiliation(s)
- Yanyan Chen
- School of Physical Education, Jianghan University, Wuhan Hubei, 430056, China
| | - Enyuan Zhou
- School of Physical Education, Jianghan University, Wuhan Hubei, 430056, China
| | - Yu Wang
- School of Physical Education, Jianghan University, Wuhan Hubei, 430056, China
| | - Yuxiang Wu
- School of Physical Education, Jianghan University, Wuhan Hubei, 430056, China
| | - Guodong Xu
- School of Physical Education, Jianghan University, Wuhan Hubei, 430056, China
| | - Lin Chen
- School of Physical Education, Jianghan University, Wuhan Hubei, 430056, China.
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7
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Cerina L, Papini GB, Fonseca P, Overeem S, van Dijk JP, Vullings R. Extraction of cardiac-related signals from a suprasternal pressure sensor during sleep. Physiol Meas 2023; 44. [PMID: 36608350 DOI: 10.1088/1361-6579/acb12b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Accepted: 01/06/2023] [Indexed: 01/07/2023]
Abstract
Objective.The accurate detection of respiratory effort during polysomnography is a critical element in the diagnosis of sleep-disordered breathing conditions such as sleep apnea. Unfortunately, the sensors currently used to estimate respiratory effort are either indirect and ignore upper airway dynamics or are too obtrusive for patients. One promising alternative is the suprasternal notch pressure (SSP) sensor: a small element placed on the skin in the notch above the sternum within an airtight capsule that detects pressure swings in the trachea. Besides providing information on respiratory effort, the sensor is sensitive to small cardiac oscillations caused by pressure perturbations in the carotid arteries or the trachea. While current clinical research considers these as redundant noise, they may contain physiologically relevant information.Approach.We propose a method to separate the signal generated by cardiac activity from the one caused by breathing activity. Using only information available from the SSP sensor, we estimate the heart rate and track its variations, then use a set of tuned filters to process the original signal in the frequency domain and reconstruct the cardiac signal. We also include an overview of the technical and physiological factors that may affect the quality of heart rate estimation. The output of our method is then used as a reference to remove the cardiac signal from the original SSP pressure signal, to also optimize the assessment of respiratory activity. We provide a qualitative comparison against methods based on filters with fixed frequency cutoffs.Main results.In comparison with electrocardiography (ECG)-derived heart rate, we achieve an agreement error of 0.06 ± 5.09 bpm, with minimal bias drift across the measurement range, and only 6.36% of the estimates larger than 10 bpm.Significance.Together with qualitative improvements in the characterization of respiratory effort, this opens the development of novel portable clinical devices for the detection and assessment of sleep disordered breathing.
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Affiliation(s)
- Luca Cerina
- Electrical Engineering,Technische Universiteit Eindhoven, Eindhoven, Noord Brabant, The Netherlands
| | - Gabriele B Papini
- Electrical Engineering,Technische Universiteit Eindhoven, Eindhoven, Noord Brabant, The Netherlands.,Philips Research, Eindhoven, Noord Brabant, The Netherlands
| | - Pedro Fonseca
- Electrical Engineering,Technische Universiteit Eindhoven, Eindhoven, Noord Brabant, The Netherlands.,Philips Research, Eindhoven, Noord Brabant, The Netherlands
| | - Sebastiaan Overeem
- Electrical Engineering,Technische Universiteit Eindhoven, Eindhoven, Noord Brabant, The Netherlands.,Center for Sleep Medicine, Kempenhaeghe Foundation, Heeze, Noord Brabant, The Netherlands
| | - Johannes P van Dijk
- Electrical Engineering,Technische Universiteit Eindhoven, Eindhoven, Noord Brabant, The Netherlands.,Center for Sleep Medicine, Kempenhaeghe Foundation, Heeze, Noord Brabant, The Netherlands
| | - Rik Vullings
- Electrical Engineering,Technische Universiteit Eindhoven, Eindhoven, Noord Brabant, The Netherlands
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8
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Ajtay BE, Béres S, Hejjel L. The oscillating pulse arrival time as a physiological explanation regarding the difference between ECG- and Photoplethysmogram-derived heart rate variability parameters. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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9
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Wu MT, Liu IF, Tzeng YH, Wang L. Modified photoplethysmography signal processing and analysis procedure for obtaining reliable stiffness index reflecting arteriosclerosis severity. Physiol Meas 2022; 43. [PMID: 35927978 DOI: 10.1088/1361-6579/ac7d91] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Accepted: 06/30/2022] [Indexed: 11/12/2022]
Abstract
Objective.This study aimed to describe a modified photoplethysmography (PPG) signal processing and analysis procedure to obtain a more reliable arterial stiffness index (SI).Approach.Three parameters were used to assess the PPG signal quality without prominent diastolic waves, which are similar to a sinusoidal waveform shape. The first parameter, sinusoidal ratio (S-value), was based on frequency-domain analysis: a higher S-value indicated the presence of PPG pulse wave with unapparent diastolic peak. The second parameter was the time difference between systolic peak-to-diastolic peak and the systolic peak-to-dicrotic notch. The third parameter was the percentage of sin-like waveform in the PPG signals. The applicability of these parameters was demonstrated in 40 participants, including 11 with apparent diastolic peaks in the PPG signals and 29 with unapparent diastolic peaks.Main results.An S-value of >3.5 indicated apparent diastolic peaks in the PPG signals. In addition, a systolic peak-to-diastolic peak time difference >80% and a sin-like waveform >55% may be associated with severity of vascular aging.Significance.These parameters successfully detected low-quality PPG signals with unapparent diastolic waveform before SI calculation, thereby ensuring the accuracy of subsequent evaluation of cardiovascular-related disease and clinical risk stratification.
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Affiliation(s)
- Meng-Ting Wu
- Ph.D. Program of Electrical and Communications Engineering, Feng Chia University, Taichung, Taiwan.,Division of Neurosurgery, Department of Surgery, Cheng-Hsin General Hospital, Taipei, Taiwan
| | - I-Fan Liu
- Heart Center, Cheng Hsin General Hospital, Taipei, Taiwan
| | - Yun-Hsuan Tzeng
- Division of Advanced Medical Imaging, Health Management Center, Cheng Hsin General Hospital, Taipei, Taiwan.,School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Lei Wang
- Department of Electrical Engineering, Feng Chia University, Taichung, Taiwan
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10
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Zaunseder S, Vehkaoja A, Fleischhauer V, Hoog Antink C. Signal-to-noise ratio is more important than sampling rate in beat-to-beat interval estimation from optical sensors. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103538] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
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