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Bizzego A, Esposito G. Performance Assessment of Heartbeat Detection Algorithms on Photoplethysmograph and Functional NearInfrared Spectroscopy Signals. SENSORS (BASEL, SWITZERLAND) 2023; 23:3668. [PMID: 37050728 PMCID: PMC10099230 DOI: 10.3390/s23073668] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Revised: 03/21/2023] [Accepted: 03/29/2023] [Indexed: 06/19/2023]
Abstract
With wearable sensors, the acquisition of physiological signals has become affordable and feasible in everyday life. Specifically, Photoplethysmography (PPG), being a low-cost and highly portable technology, has attracted notable interest for measuring and diagnosing cardiac activity, one of the most important physiological and autonomic indicators. In addition to the technological development, several specific signal-processing algorithms have been designed to enable reliable detection of heartbeats and cope with the lower quality of the signals. In this study, we compare three heartbeat detection algorithms: Derivative-Based Detection (DBD), Recursive Combinatorial Optimization (RCO), and Multi-Scale Peak and Trough Detection (MSPTD). In particular, we considered signals from two datasets, namely, the PPG-DALIA dataset (N = 15) and the FANTASIA dataset (N = 20) which differ in terms of signal characteristics (sampling frequency and length) and type of acquisition devices (wearable and medical-grade). The comparison is performed both in terms of heartbeat detection performance and computational workload required to execute the algorithms. Finally, we explore the applicability of these algorithms on the cardiac component obtained from functional Near InfraRed Spectroscopy signals (fNIRS).The results indicate that, while the MSPTD algorithm achieves a higher F1 score in cases that involve body movements, such as cycling (MSPTD: Mean = 74.7, SD = 14.4; DBD: Mean = 54.4, SD = 21.0; DBD + RCO: Mean = 49.5, SD = 22.9) and walking up and down the stairs (MSPTD: Mean = 62.9, SD = 12.2; DBD: Mean = 50.5, SD = 11.9; DBD + RCO: Mean = 45.0, SD = 14.0), for all other activities the three algorithms perform similarly. In terms of computational complexity, the computation time of the MSPTD algorithm appears to grow exponentially with the signal sampling frequency, thus requiring longer computation times in the case of high-sampling frequency signals, where the usage of the DBD and RCO algorithms might be preferable. All three algorithms appear to be appropriate candidates for exploring the applicability of heartbeat detection on fNIRS data.
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Edge-Enabled Heart Rate Estimation from Multisensor PPG Signals. JOURNAL OF HEALTHCARE ENGINEERING 2023; 2023:4682760. [PMID: 36875750 PMCID: PMC9977552 DOI: 10.1155/2023/4682760] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Revised: 06/14/2022] [Accepted: 11/25/2022] [Indexed: 02/24/2023]
Abstract
Heart rate (HR) estimation from multisensor PPG signals suffers from the dilemma of inconsistent computation results, due to the prevalence of bio-artifacts (BAs). Furthermore, advancements in edge computing have shown promising results from capturing and processing diversified types of sensing signals using the devices of Internet of Medical Things (IoMT). In this paper, an edge-enabled method is proposed to estimate HRs accurately and with low latency from multisensor PPG signals captured by bilateral IoMT devices. First, we design a real-world edge network with several resource-constrained devices, divided into collection edge nodes and computing edge nodes. Second, a self-iteration RR interval calculation method, at the collection edge nodes, is proposed leveraging the inherent frequency spectrum feature of PPG signals and preliminarily eliminating the influence of BAs on HR estimation. Meanwhile, this part also reduces the volume of sent data from IoMT devices to compute edge nodes. Afterward, at the computing edge nodes, a heart rate pool with an unsupervised abnormal detection method is proposed to estimate the average HR. Experimental results show that the proposed method outperforms traditional approaches which rely on a single PPG signal, attaining better results in terms of the consistency and accuracy for HR estimation. Furthermore, at the designed edge network, our proposed method processes a 30 s PPG signal to obtain an HR, consuming only 4.24 s of computation time. Hence, the proposed method is of significant value for the low-latency applications in the field of IoMT healthcare and fitness management.
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3
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Maity AK, Veeraraghavan A, Sabharwal A. PPGMotion: Model-based detection of motion artifacts in photoplethysmography signals. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103632] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
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4
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Kang X, Zhang J, Shao Z, Wang G, Geng X, Zhang Y, Zhang H. A Wearable and Real-Time Pulse Wave Monitoring System Based on a Flexible Compound Sensor. BIOSENSORS 2022; 12:bios12020133. [PMID: 35200393 PMCID: PMC8870208 DOI: 10.3390/bios12020133] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Revised: 02/09/2022] [Accepted: 02/18/2022] [Indexed: 12/30/2022]
Abstract
Continuous monitoring of pulse waves plays a significant role in reflecting physical conditions and disease diagnosis. However, the current collection equipment cannot simultaneously achieve wearable and continuous monitoring under varying pressure and provide personalized pulse wave monitoring targeted different human bodies. To solve the above problems, this paper proposed a novel wearable and real-time pulse wave monitoring system based on a novel flexible compound sensor. Firstly, a custom-packaged pressure sensor, a signal stabilization structure, and a micro pressurization system make up the flexible compound sensor to complete the stable acquisition of pulse wave signals under continuously varying pressure. Secondly, a real-time algorithm completes the analysis of the trend of the pulse wave peak, which can quickly and accurately locate the best pulse wave for different individuals. Finally, the experimental results show that the wearable system can both realize continuous monitoring and reflecting trend differences and quickly locate the best pulse wave for different individuals with the 95% accuracy. The weight of the whole system is only 52.775 g, the working current is 46 mA, and the power consumption is 160 mW. Its small size and low power consumption meet wearable and portable scenarios, which has significant research value and commercialization prospects.
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Affiliation(s)
- Xiaoxiao Kang
- Institute of Microelectronics of Chinese Academy of Sciences, Beijing 100029, China; (X.K.); (J.Z.); (Z.S.); (G.W.); (X.G.); (Y.Z.)
- University of Chinese Academy of Sciences, Beijing 100049, China
- Beijing Key Laboratory for Next Generation RF Communication Chip Technology, Beijing 100029, China
| | - Jun Zhang
- Institute of Microelectronics of Chinese Academy of Sciences, Beijing 100029, China; (X.K.); (J.Z.); (Z.S.); (G.W.); (X.G.); (Y.Z.)
- University of Chinese Academy of Sciences, Beijing 100049, China
- Beijing Key Laboratory for Next Generation RF Communication Chip Technology, Beijing 100029, China
| | - Zheming Shao
- Institute of Microelectronics of Chinese Academy of Sciences, Beijing 100029, China; (X.K.); (J.Z.); (Z.S.); (G.W.); (X.G.); (Y.Z.)
- University of Chinese Academy of Sciences, Beijing 100049, China
- Beijing Key Laboratory for Next Generation RF Communication Chip Technology, Beijing 100029, China
| | - Guotai Wang
- Institute of Microelectronics of Chinese Academy of Sciences, Beijing 100029, China; (X.K.); (J.Z.); (Z.S.); (G.W.); (X.G.); (Y.Z.)
- University of Chinese Academy of Sciences, Beijing 100049, China
- Beijing Key Laboratory for Next Generation RF Communication Chip Technology, Beijing 100029, China
| | - Xingguang Geng
- Institute of Microelectronics of Chinese Academy of Sciences, Beijing 100029, China; (X.K.); (J.Z.); (Z.S.); (G.W.); (X.G.); (Y.Z.)
- Beijing Key Laboratory for Next Generation RF Communication Chip Technology, Beijing 100029, China
| | - Yitao Zhang
- Institute of Microelectronics of Chinese Academy of Sciences, Beijing 100029, China; (X.K.); (J.Z.); (Z.S.); (G.W.); (X.G.); (Y.Z.)
- Beijing Key Laboratory for Next Generation RF Communication Chip Technology, Beijing 100029, China
| | - Haiying Zhang
- Institute of Microelectronics of Chinese Academy of Sciences, Beijing 100029, China; (X.K.); (J.Z.); (Z.S.); (G.W.); (X.G.); (Y.Z.)
- University of Chinese Academy of Sciences, Beijing 100049, China
- Beijing Key Laboratory for Next Generation RF Communication Chip Technology, Beijing 100029, China
- Correspondence:
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5
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Bizzego A, Gabrieli G, Furlanello C, Esposito G. Comparison of Wearable and Clinical Devices for Acquisition of Peripheral Nervous System Signals. SENSORS (BASEL, SWITZERLAND) 2020; 20:E6778. [PMID: 33260880 PMCID: PMC7730565 DOI: 10.3390/s20236778] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Revised: 11/20/2020] [Accepted: 11/25/2020] [Indexed: 12/29/2022]
Abstract
A key access point to the functioning of the autonomic nervous system is the investigation of peripheral signals. Wearable devices (WDs) enable the acquisition and quantification of peripheral signals in a wide range of contexts, from personal uses to scientific research. WDs have lower costs and higher portability than medical-grade devices. However, the achievable data quality can be lower, and data are subject to artifacts due to body movements and data losses. It is therefore crucial to evaluate the reliability and validity of WDs before their use in research. In this study, we introduce a data analysis procedure for the assessment of WDs for multivariate physiological signals. The quality of cardiac and electrodermal activity signals is validated with a standard set of signal quality indicators. The pipeline is available as a collection of open source Python scripts based on the pyphysio package. We apply the indicators for the analysis of signal quality on data simultaneously recorded from a clinical-grade device and two WDs. The dataset provides signals of six different physiological measures collected from 18 subjects with WDs. This study indicates the need to validate the use of WDs in experimental settings for research and the importance of both technological and signal processing aspects to obtain reliable signals and reproducible results.
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Affiliation(s)
- Andrea Bizzego
- Department of Psychology and Cognitive Science, University of Trento, 38122 Trento, Italy;
| | - Giulio Gabrieli
- Psychology Program, School of Social Sciences, Nanyang Technological University, Singapore 639798, Singapore;
| | | | - Gianluca Esposito
- Department of Psychology and Cognitive Science, University of Trento, 38122 Trento, Italy;
- Psychology Program, School of Social Sciences, Nanyang Technological University, Singapore 639798, Singapore;
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore 639798, Singapore
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6
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Zhang R, Hua Z, Chen C, Liu G, Wen W. Analysis of autonomic nervous pattern in hypertension based on short-term heart rate variability. BIOMED ENG-BIOMED TE 2020; 66:/j/bmte.ahead-of-print/bmt-2019-0184/bmt-2019-0184.xml. [PMID: 32769220 DOI: 10.1515/bmt-2019-0184] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2019] [Accepted: 06/30/2020] [Indexed: 11/15/2022]
Abstract
Physiological studies have found that the autonomic nervous system plays an important role in controlling blood pressure values. This paper, based on machine learning approaches, analysed short-term heart rate variability to determine differences in autonomic nervous function between hypertensive patients and normal population. The electrocardiogram (ECG) of hypertensive patients are 137 ECG recordings provided by Smart Health for Assessing the Risk of Events via ECG (SHAREE database). The RR intervals of healthy subjects include the data of 18 subjects from the MIT-BIH Normal Sinus Rhythm Database (nsrdb) and 54 subjects from the Normal Sinus Rhythm RR Interval Database (nsr2db). In this paper, each RR segment includes continuous 500 beats. Seventeen features were extracted to distinguish the hypertensive heart beat rhythms from the normal ones, and Kolmogorov-Smirnov test and sequential backward selection (SBS) were applied to get the best feature combinations. In addition, support vector machine (SVM), k-nearest neighbor (KNN) and random forest (RF) were applied as classifiers in the study. The performance of each classifier was evaluated independently using the leave-one-subject-out validation method. The best predictive model was based on RF and enabled to identify hypertensive patients by five features with an accuracy of 86.44%. The best five HRV features are sample entropy (SampEn), very low frequency spectral powers (VLF), root mean square of successful differences (RMSSD), ratio of low frequency spectral powers and high frequency spectral powers (LF/HF) and vector angle index (VAI). The results of the study show sympathetic overactivity and decreased parasympathetic tone in hypertensive patients.
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Affiliation(s)
- Ruiqi Zhang
- School of Electronic and Information Engineering, Southwest University, Chongqing, China
- and Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, Chongqing, China
| | - Zhengchun Hua
- School of Electronic and Information Engineering, Southwest University, Chongqing, China
- and Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, Chongqing, China
| | - Chen Chen
- School of Electronic and Information Engineering, Southwest University, Chongqing, China
- and Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, Chongqing, China
| | - Guangyuan Liu
- School of Electronic and Information Engineering, Southwest University, Chongqing, China
- and Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, Chongqing, China
| | - Wanhui Wen
- School of Electronic and Information Engineering, Southwest University, Chongqing, China
- and Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, Chongqing, China
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7
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Motion Artifact Reduction in Wearable Photoplethysmography Based on Multi-Channel Sensors with Multiple Wavelengths. SENSORS 2020; 20:s20051493. [PMID: 32182772 PMCID: PMC7085621 DOI: 10.3390/s20051493] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/12/2020] [Revised: 03/02/2020] [Accepted: 03/07/2020] [Indexed: 11/17/2022]
Abstract
Photoplethysmography (PPG) is an easy and convenient method by which to measure heart rate (HR). However, PPG signals that optically measure volumetric changes in blood are not robust to motion artifacts. In this paper, we develop a PPG measuring system based on multi-channel sensors with multiple wavelengths and propose a motion artifact reduction algorithm using independent component analysis (ICA). We also propose a truncated singular value decomposition for 12-channel PPG signals, which contain direction and depth information measured using the developed multi-channel PPG measurement system. The performance of the proposed method is evaluated against the R-peaks of an electrocardiogram in terms of sensitivity (Se), positive predictive value (PPV), and failed detection rate (FDR). The experimental results show that Se, PPV, and FDR were 99%, 99.55%, and 0.45% for walking, 96.28%, 99.24%, and 0.77% for fast walking, and 82.49%, 99.83%, and 0.17% for running, respectively. The evaluation shows that the proposed method is effective in reducing errors in HR estimation from PPG signals with motion artifacts in intensive motion situations such as fast walking and running.
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8
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Wireless, Portable Fiber Bragg Grating Interrogation System Employing Optical Edge Filter. SENSORS 2019; 19:s19143222. [PMID: 31336657 PMCID: PMC6679589 DOI: 10.3390/s19143222] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/10/2019] [Revised: 07/11/2019] [Accepted: 07/18/2019] [Indexed: 02/05/2023]
Abstract
A small-size, high-precision fiber Bragg grating interrogator was developed for continuous plethysmograph monitoring. The interrogator employs optical edge filters, which were integrated with a broad-band light source and photodetector to demodulate the Bragg wavelength shift. An amplifier circuit was designed to effectively amplify the plethysmograph signal, obtained as a small vibration of optical power on the large offset. The standard deviation of the measured Bragg wavelength was about 0.1 pm. The developed edge filter module and amplifier circuit were encased with a single-board computer and communicated with a laptop computer via Wi-Fi. As a result, the plethysmograph was clearly obtained remotely, indicating the possibility of continuous vital sign measurement.
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Yoon J, Joo Y, Oh E, Lee B, Kim D, Lee S, Kim T, Byun J, Hong Y. Soft Modular Electronic Blocks (SMEBs): A Strategy for Tailored Wearable Health-Monitoring Systems. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2019; 6:1801682. [PMID: 30886798 PMCID: PMC6402283 DOI: 10.1002/advs.201801682] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/27/2018] [Revised: 11/15/2018] [Indexed: 05/09/2023]
Abstract
Precise monitoring of human body signals can be achieved by soft, conformal contact and precise arrangement of wearable devices to the desired body positions. So far, no design and fabrication methodology in soft wearable devices is able to address the variations in the form factor of the human body such as the various sizes and shapes of individual body parts, which can significantly cause misalignments and the corresponding inaccurate monitoring. Here, a concept of soft modular electronic blocks (SMEBs) enabling the assembly of soft wearable systems onto human skin with functions and layouts tailored to the form factors of individuals' bodies is presented. Three types of SMEBs are developed as fundamental building blocks for functional modularization. The physical design of SMEBs is optimized for a mechanically stable island-bridge configuration. The prepared SMEBs can be integrated onto a target body part through rapid, room-temperature (RT) assembly (<5 s) using an oxygen plasma-induced siloxane bonding method. A soft metacarpophalangeal (MP) joints flexion monitoring system that is tailored to allow for accurate monitoring for multiple individuals with unique joint and hand sizes is demonstrated.
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Affiliation(s)
- Jaeyoung Yoon
- Department of Electrical and Computer EngineeringInter University Semiconductor Research Center (ISRC)Seoul National UniversitySeoul08826Republic of Korea
| | - Yunsik Joo
- Department of Electrical and Computer EngineeringInter University Semiconductor Research Center (ISRC)Seoul National UniversitySeoul08826Republic of Korea
| | - Eunho Oh
- Department of Electrical and Computer EngineeringInter University Semiconductor Research Center (ISRC)Seoul National UniversitySeoul08826Republic of Korea
| | - Byeongmoon Lee
- Department of Electrical and Computer EngineeringInter University Semiconductor Research Center (ISRC)Seoul National UniversitySeoul08826Republic of Korea
| | - Daesik Kim
- Department of Electrical and Computer EngineeringInter University Semiconductor Research Center (ISRC)Seoul National UniversitySeoul08826Republic of Korea
| | - Seunghwan Lee
- Department of Electrical and Computer EngineeringInter University Semiconductor Research Center (ISRC)Seoul National UniversitySeoul08826Republic of Korea
| | - Taehoon Kim
- Department of Electrical and Computer EngineeringInter University Semiconductor Research Center (ISRC)Seoul National UniversitySeoul08826Republic of Korea
| | - Junghwan Byun
- Department of Mechanical and Aerospace EngineeringInstitute of Advanced Machines and Design (IAMD)Soft Robotics Research Center (SRRC)Seoul National UniversitySeoul08826Republic of Korea
| | - Yongtaek Hong
- Department of Electrical and Computer EngineeringInter University Semiconductor Research Center (ISRC)Seoul National UniversitySeoul08826Republic of Korea
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10
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Tamura T. Current progress of photoplethysmography and SPO 2 for health monitoring. Biomed Eng Lett 2019; 9:21-36. [PMID: 30956878 PMCID: PMC6431353 DOI: 10.1007/s13534-019-00097-w] [Citation(s) in RCA: 84] [Impact Index Per Article: 16.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2018] [Revised: 01/05/2019] [Accepted: 01/15/2019] [Indexed: 11/28/2022] Open
Abstract
A photoplethysmograph (PPG) is a simple medical device for monitoring blood flow and transportation of substances in the blood. It consists of a light source and a photodetector for measuring transmitted and reflected light signals. Clinically, PPGs are used to monitor the pulse rate, oxygen saturation, blood pressure, and blood vessel stiffness. Wearable unobtrusive PPG monitors are commercially available. Here, we review the principle issues and clinical applications of PPG for monitoring oxygen saturation.
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Affiliation(s)
- Toshiyo Tamura
- Future Robotics Institute, Wadeda University, Tokyo, Japan
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11
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Jin C, Xia C, Zhang S, Wang L, Wang Y, Yan H. A Wearable Combined Wrist Pulse Measurement System Using Airbags for Pressurization. SENSORS 2019; 19:s19020386. [PMID: 30669333 PMCID: PMC6358741 DOI: 10.3390/s19020386] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/13/2018] [Revised: 01/09/2019] [Accepted: 01/15/2019] [Indexed: 11/16/2022]
Abstract
The pulse measurement instrument is based on traditional Chinese medicine (TCM) and is used to collect the pulse of patients to assist in diagnosis and treatment. In the existing pulse measurement system, desktop devices have large volumes, complex pressure adjusting operations, and unstable pressurization. Wearable devices tend to have no pressurization function or the function to pressurize three channels separately, which are not consistent with the diagnostic method in TCM. This study constructs a wearable pulse measurement system using airbags for pressurization. This system uses guide plates, guide grooves, and positioning screws to adjust the relative position of the wristband and locate Cun, Guan and Chi regions. The pulse signal measured by the sensor is collected and sent to a computer by microcontroller unit. In experiments, this system successfully obtains the best pulse-taking pressure, its pulse waveform under continuous decompression, and the pulse waveform of three regions under light, medium, and heavy pressure. Compared with the existing technology, the system has the advantages of supporting single-region and three-region pulse acquisition, independent pressure adjustment, and position adjustment. It meets the needs of home, medical, and experimental research, and it is convenient and comfortable to wear and easy to carry.
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Affiliation(s)
- Chenling Jin
- School of Mechanical and Power Engineering, East China University of Science and Technology, Shanghai 200237, China.
| | - Chunming Xia
- School of Mechanical and Power Engineering, East China University of Science and Technology, Shanghai 200237, China.
| | - Shiyu Zhang
- School of Mechanical and Power Engineering, East China University of Science and Technology, Shanghai 200237, China.
| | - Liren Wang
- School of Mechanical and Power Engineering, East China University of Science and Technology, Shanghai 200237, China.
| | - Yiqin Wang
- School of Basic Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China.
| | - Haixia Yan
- School of Basic Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China.
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12
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Kasbekar RS, Mendelson Y. Evaluation of key design parameters for mitigating motion artefact in the mobile reflectance PPG signal to improve estimation of arterial oxygenation. Physiol Meas 2018; 39:075008. [PMID: 30051881 DOI: 10.1088/1361-6579/aacfe5] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Pulse oximetry, a widely accepted method for non-invasive estimation of arterial oxygen saturation (SpO2) and pulse rate (PR), is increasingly being adapted for mobile applications. Previous work in mitigating motion artefact, which corrupts the photoplethysmogram (PPG) used in pulse oximetry, has focused on reducing noise using signal processing algorithms or through sensor design that controlled only one variable at a time. In this work, we have investigated the effect of several variables such as sensor weight, relative motion, placement, and contact force against the skin that can impact motion artefact independently or by interacting with each other. APPROACH We have identified a unique combination of these variables that is most optimal in reducing motion artefacts using a full factorial design of experiments methodology and evaluated the effect of these factors on PPG readings with and without motion. MAIN RESULTS Data collected on 10 diverse subjects showed that placement (p = 0.03), contact force (p = 0.004), and sensor-to-skin adhesion or relative motion when combined with force (p < 0.001) had the most significant effect on reducing the motion artefact signal. Sensor weight (p = 0.822) by itself had no significant effect, however when combined with sensor adhesion (p < 0.001) had a significant impact. SIGNIFICANCE This lays the foundation for future development of more robust sensors that can significantly reduce the effect of motion artefacts in reflectance-based pulse oximetry and could have great clinical value due to significant reduction of SpO2 errors and false alarms associated with motion artefact, making wearable pulse oximetry more reliable in mobile applications.
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Affiliation(s)
- Rajesh S Kasbekar
- Department of Biomedical Engineering, Worcester Polytechnic Institute, Worcester, MA 01609, United States of America
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13
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Lan KC, Raknim P, Kao WF, Huang JH. Toward Hypertension Prediction Based on PPG-Derived HRV Signals: a Feasibility Study. J Med Syst 2018; 42:103. [DOI: 10.1007/s10916-018-0942-5] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2017] [Accepted: 03/15/2018] [Indexed: 10/17/2022]
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14
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Using support vector machines on photoplethysmographic signals to discriminate between hypovolemia and euvolemia. PLoS One 2018; 13:e0195087. [PMID: 29596477 PMCID: PMC5875841 DOI: 10.1371/journal.pone.0195087] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2017] [Accepted: 03/18/2018] [Indexed: 11/19/2022] Open
Abstract
Identifying trauma patients at risk of imminent hemorrhagic shock is a challenging task in intraoperative and battlefield settings given the variability of traditional vital signs, such as heart rate and blood pressure, and their inability to detect blood loss at an early stage. To this end, we acquired N = 58 photoplethysmographic (PPG) recordings from both trauma patients with suspected hemorrhage admitted to the hospital, and healthy volunteers subjected to blood withdrawal of 0.9 L. We propose four features to characterize each recording: goodness of fit (r2), the slope of the trend line, percentage change, and the absolute change between amplitude estimates in the heart rate frequency range at the first and last time points. Also, we propose a machine learning algorithm to distinguish between blood loss and no blood loss. The optimal overall accuracy of discriminating between hypovolemia and euvolemia was 88.38%, while sensitivity and specificity were 88.86% and 87.90%, respectively. In addition, the proposed features and algorithm performed well even when moderate blood volume was withdrawn. The results suggest that the proposed features and algorithm are suitable for the automatic discrimination between hypovolemia and euvolemia, and can be beneficial and applicable in both intraoperative/emergency and combat casualty care.
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15
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Lee H, Chung H, Ko H, Jeong C, Noh SE, Kim C, Lee J. Dedicated cardiac rehabilitation wearable sensor and its clinical potential. PLoS One 2017; 12:e0187108. [PMID: 29088260 PMCID: PMC5663433 DOI: 10.1371/journal.pone.0187108] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2017] [Accepted: 10/13/2017] [Indexed: 12/22/2022] Open
Abstract
We describe a wearable sensor developed for cardiac rehabilitation (CR) exercise. To effectively guide CR exercise, the dedicated CR wearable sensor (DCRW) automatically recommends the exercise intensity to the patient by comparing heart rate (HR) measured in real time with a predefined target heart rate zone (THZ) during exercise. The CR exercise includes three periods: pre-exercise, exercise with intensity guidance, and post-exercise. In the pre-exercise period, information such as THZ, exercise type, exercise stage order, and duration of each stage are set up through a smartphone application we developed for iPhones and Android devices. The set-up information is transmitted to the DCRW via Bluetooth communication. In the period of exercise with intensity guidance, the DCRW continuously estimates HR using a reflected pulse signal in the wrist. To achieve accurate HR measurements, we used multichannel photo sensors and increased the chances of acquiring a clean signal. Subsequently, we used singular value decomposition (SVD) for de-noising. For the median and variance of RMSEs in the measured HRs, our proposed method with DCRW provided lower values than those from a single channel-based method and template-based multiple-channel method for the entire exercise stage. In the post-exercise period, the DCRW transmits all the measured HR data to the smartphone application via Bluetooth communication, and the patient can monitor his/her own exercise history.
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Affiliation(s)
- Hooseok Lee
- Department of Biomedical Engineering, Wonkwang University College of Medicine, Iksan, Republic of Korea
| | - Heewon Chung
- Department of Biomedical Engineering, Wonkwang University College of Medicine, Iksan, Republic of Korea
| | - Hoon Ko
- Department of Biomedical Engineering, Wonkwang University College of Medicine, Iksan, Republic of Korea
| | - Changwon Jeong
- Department of Biomedical Engineering, Wonkwang University College of Medicine, Iksan, Republic of Korea
| | - Se-Eung Noh
- Department of Rehabilitation Medicine, Wonkwang University Colledge of Medicine, Iksan, Republic of Korea
| | - Chul Kim
- Department of Rehabilitation Medicine, Sanggye Paik Hospital, Inje University Medical College, Seoul, Republic of Korea
| | - Jinseok Lee
- Department of Biomedical Engineering, Wonkwang University College of Medicine, Iksan, Republic of Korea
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16
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Hwang CS, Yang SP, Jang KW, Park JW, Jeong KH. Angle-selective optical filter for highly sensitive reflection photoplethysmogram. BIOMEDICAL OPTICS EXPRESS 2017; 8:4361-4368. [PMID: 29082070 PMCID: PMC5654785 DOI: 10.1364/boe.8.004361] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/30/2017] [Revised: 08/25/2017] [Accepted: 08/28/2017] [Indexed: 06/07/2023]
Abstract
We report an angle-selective optical filter (ASOF) for highly sensitive reflection photoplethysmography (PPG) sensors. The ASOF features slanted aluminum (Al) micromirror arrays embedded in transparent polymer resin, which effectively block scattered light under human tissue. The device microfabrication was done by using geometry-guided resist reflow of polymer micropatterns, polydimethylsiloxane replica molding, and oblique angle deposition of thin Al film. The angular transmittance through the ASOF is precisely controlled by the angle of micromirrors. For the mirror angle of 30 degrees, the ASOF accepts an incident light between - 90 to + 50 degrees and the maximum transmittance at - 55 degrees. The ASOF exhibits the substantial reduction of both the in-band noise of PPG signals over a factor of two and the low-frequency noise by three times. Consequently, this filter allows distinguishing the diastolic peak that allows miscellaneous parameters with diverse vascular information. This optical filter provides a new opportunity for highly sensitive PPG monitoring or miscellaneous optical tomography.
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17
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Lin ST, Chen WH, Lin YH. A Pulse Rate Detection Method for Mouse Application Based on Multi-PPG Sensors. SENSORS (BASEL, SWITZERLAND) 2017; 17:E1628. [PMID: 28708112 PMCID: PMC5539748 DOI: 10.3390/s17071628] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/07/2017] [Revised: 06/26/2017] [Accepted: 07/11/2017] [Indexed: 11/16/2022]
Abstract
Heart rate is an important physiological parameter for healthcare. Among measurement methods, photoplethysmography (PPG) is an easy and convenient method for pulse rate detection. However, as the PPG signal faces the challenge of motion artifacts and is constrained by the position chosen, the purpose of this paper is to implement a comfortable and easy-to-use multi-PPG sensor module combined with a stable and accurate real-time pulse rate detection method on a computer mouse. A weighted average method for multi-PPG sensors is used to adjust the weight of each signal channel in order to raise the accuracy and stability of the detected signal, therefore reducing the disturbance of noise under the environment of moving effectively and efficiently. According to the experiment results, the proposed method can increase the usability and probability of PPG signal detection on palms.
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Affiliation(s)
- Shu-Tyng Lin
- Department of Electronic and Computer Engineering, National Taiwan University of Science and Technology, Taipei 10607, Taiwan.
| | - Wei-Hao Chen
- Department of Electronic and Computer Engineering, National Taiwan University of Science and Technology, Taipei 10607, Taiwan.
| | - Yuan-Hsiang Lin
- Department of Electronic and Computer Engineering, National Taiwan University of Science and Technology, Taipei 10607, Taiwan.
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18
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Wijesinghe RE, Lee SY, Ravichandran NK, Han S, Jeong H, Han Y, Jung HY, Kim P, Jeon M, Kim J. Optical coherence tomography-integrated, wearable (backpack-type), compact diagnostic imaging modality for in situ leaf quality assessment. APPLIED OPTICS 2017; 56:D108-D114. [PMID: 28375377 DOI: 10.1364/ao.56.00d108] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
We developed a compact, wearable diagnostic imaging modality employing optical coherence tomography for in situ plant leaf quality assessments. This system is capable of diagnosing infected leaves at the initial disease stages. Our system is a versatile backpack-type imaging modality with a compact spectrometer, miniature computer, rechargeable power source, and handheld inspection probe. This method enhances real-time in situ specimen inspection through direct implementation of the imaging modality in a plantation. To evaluate the initial performance, field experiments were conducted in apple, pear, and persimmon plantations. Based on the obtained results, we can conclude that the developed imaging modality can be considered as a promising, efficient, convenient, and fast in situ inspection technique for various agricultural fields, which minimizes the limitations of complex tabletop inspection modalities.
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19
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Kim J, Ko H. Reconfigurable Multiparameter Biosignal Acquisition SoC for Low Power Wearable Platform. SENSORS 2016; 16:s16122002. [PMID: 27898004 PMCID: PMC5190983 DOI: 10.3390/s16122002] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/30/2016] [Revised: 11/17/2016] [Accepted: 11/24/2016] [Indexed: 12/05/2022]
Abstract
A low power and low noise reconfigurable analog front-end (AFE) system on a chip (SoC) for biosignal acquisition is presented. The presented AFE can be reconfigured for use in electropotential, bioimpedance, electrochemical, and photoelectrical modes. The advanced healthcare services based on multiparameter physiological biosignals can be easily implemented with these multimodal and highly reconfigurable features of the proposed system. The reconfigurable gain and input referred noise of the core instrumentation amplifier block are 25 dB to 52 dB, and 1 μVRMS, respectively. The power consumption of the analog blocks in one readout channel is less than 52 μW. The reconfigurable capability among various modes of applications including electrocardiogram, blood glucose concentration, respiration, and photoplethysmography are shown experimentally.
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Affiliation(s)
- Jongpal Kim
- Samsung Electronics Inc., Suwon 16678, Korea.
| | - Hyoungho Ko
- Department of Electronics Engineering, Chungnam National University, Daejeon 34134, Korea.
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