Development of fabric electrode for bio-potential signal acquisition in wearable health monitoring and effect of perspiration on signal acquisition

2021 ◽  
pp. 152808372110608
Author(s):  
M. S. Yogendra ◽  
M.V. Mallikarjuna Reddy ◽  
S.N. Kartik ◽  
K. Mohanvelu ◽  
F.V. Varghese ◽  
...  
Keyword(s):  
Health Monitoring ◽  
Signal Acquisition ◽  
Textile Materials ◽  
Ecg Signals ◽  
Conductive Fabric ◽  
Potential Electrode ◽  
Monitoring Applications ◽  
Resistivity Meter ◽  
Electrocardiogram Ecg

Development of a gel-free bio-potential electrode for the wearable health monitoring applications is a challenging goal. A conductive fabric electrode can replace the traditional conductive gel electrode. This paper describes the development of a conductive fabric electrode with regard to its potential use for electrocardiogram (ECG) acquisition. Since direct contact between the conductive fabric and human skin will be involved, an investigation on the effect of perspiration on the electrical conductivity of fabric is critical. Hence, the developed electrode was treated with alkaline (pH=8.0) and acidic (pH=4.3) perspiration for 3, 8 and 40 h to study the effect of perspiration on the conductivity and surface morphology. The acquired ECG signals were analysed with respect to morphology and frequency distribution. Conductivity tests were carried out on the perspiration-treated test electrodes by two probe method and surface resistivity meter. The ECG signals of volunteers were also recorded. The results showed a slight decrease in conductivity but without affecting the morphology and the quality of ECG signal. Leached silver content in the acid perspiration-treated solution was found to be 0.117 ppm as determined by Atomic absorption spectroscopy. The result shows that soft conducting textile materials can indeed be used as an electrode for ECG acquisition. This is a novel type of gel-free fabric electrode for long term wearable health monitoring applications including space application.

IMPLEMENTATION OF MULTIPLE-CHANNEL CAPACITIVE ECG MEASUREMENT BASED ON CONDUCTIVE FABRIC

2013 ◽  
Vol 25 (06) ◽  
pp. 1350052 ◽  
Author(s):  
Yue-Der Lin ◽  
Ya-Hsuech Chien ◽  
Shih-Fan Wang ◽  
Cheng-Lun Tsai ◽  
Hen-Hong Chang ◽  
...  
Keyword(s):  
Circuit Diagram ◽  
Test Results ◽  
Theoretic Analysis ◽  
Multiple Channel ◽  
Conductive Fabric ◽  
The Subject ◽  
The Common ◽  
Power Line Interference ◽  
Electrocardiogram Ecg

Capacitive electrocardiogram (cECG) measurement is an attractive approach for long-term health monitoring. However, there is little literature available for the implementation of multiple-channel cECG system in standard limb leads. The circuit diagram for such a system is also rarely available in literature. This paper presents a multiple-channel limb-lead cECG system that utilized conductive fabrics as the capacitive sensors. The design criteria and the corresponding circuit diagram are described in detail. The proposed system also incorporates the capacitive driven-body (CDB) circuit to reduce the common-mode power-line interference (PLI). The presented system is verified to be stable by theoretic analysis and long-term experiments. The signals acquired by the presented system are competitive with those by commercially available electrocardiogram (ECG) machines. The feasible size and distance to the subject for the sensor made by conductive fabric have also been evaluated by a series of tests. From the test results, the sensor is suggested to be of greater than 60 cm2 in area and not more than 3 mm in distance for cECG measurement.

scholarly journals Capturing Electrocardiogram Signals from Chairs by Multiple Capacitively Coupled Unipolar Electrodes

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2835 ◽  
Author(s):  
Zhongjie Hou ◽  
Jinxi Xiang ◽  
Yonggui Dong ◽  
Xiaohui Xue ◽  
Hao Xiong ◽  
...  
Keyword(s):  
Printed Circuit Board ◽  
Experimental Tests ◽  
Circuit Board ◽  
Signal Acquisition ◽  
Ecg Signal ◽  
Capacitively Coupled ◽  
Ecg Signals ◽  
Printed Circuit ◽  
Electrocardiogram Signals ◽  
Electrocardiogram Ecg

A prototype of an electrocardiogram (ECG) signal acquisition system with multiple unipolar capacitively coupled electrodes is designed and experimentally tested. Capacitively coupled electrodes made of a standard printed circuit board (PCB) are used as the sensing electrodes. Different from the conventional measurement schematics, where one single lead ECG signal is acquired from a pair of sensing electrodes, the sensing electrodes in our approaches operate in a unipolar mode, i.e., the biopotential signals picked up by each sensing electrodes are amplified and sampled separately. Four unipolar electrodes are mounted on the backrest of a regular chair and therefore four channel of signals containing ECG information are sampled and processed. It is found that the qualities of ECG signal contained in the four channel are different from each other. In order to pick up the ECG signal, an index for quality evaluation, as well as for aggregation of multiple signals, is proposed based on phase space reconstruction. Experimental tests are carried out while subjects sitting on the chair and clothed. The results indicate that the ECG signals can be reliably obtained in such a unipolar way.

scholarly journals A Wearable Electrocardiogram Telemonitoring System for Atrial Fibrillation Detection

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 606 ◽  
Author(s):  
Minggang Shao ◽  
Zhuhuang Zhou ◽  
Guangyu Bin ◽  
Yanping Bai ◽  
Shuicai Wu
Keyword(s):  
Atrial Fibrillation ◽  
Ecg Monitoring ◽  
Web Browser ◽  
Ecg Signals ◽  
Cloud Server ◽  
Android App ◽  
Electrocardiogram Ecg ◽  
The Web ◽  
Ecg Data

In this paper we proposed a wearable electrocardiogram (ECG) telemonitoring system for atrial fibrillation (AF) detection based on a smartphone and cloud computing. A wearable ECG patch was designed to collect ECG signals and send the signals to an Android smartphone via Bluetooth. An Android APP was developed to display the ECG waveforms in real time and transmit every 30 s ECG data to a remote cloud server. A machine learning (CatBoost)-based ECG classification method was proposed to detect AF in the cloud server. In case of detected AF, the cloud server pushed the ECG data and classification results to the web browser of a doctor. Finally, the Android APP displayed the doctor’s diagnosis for the ECG signals. Experimental results showed the proposed CatBoost classifier trained with 17 selected features achieved an overall F1 score of 0.92 on the test set (n = 7270). The proposed wearable ECG monitoring system may potentially be useful for long-term ECG telemonitoring for AF detection.

scholarly journals Smartphone based ECG Acquisition and Analysis

2021 ◽  
Vol 9 (VI) ◽  
pp. 539-543
Author(s):  
Renuka Vijay Kapse
Keyword(s):  
Cardiovascular Diseases ◽  
Cause Of Death ◽  
Health Monitoring ◽  
Early Warning ◽  
Heart Diseases ◽  
Ecg Signal ◽  
Android Application ◽  
Ecg Signals ◽  
Heart Damage ◽  
Electrocardiogram Ecg

Health monitoring and technologies related to health monitoring is an appealing area of research. The electrocardiogram (ECG) has constantly being mainstream estimation plan to evaluate and analyse cardiovascular diseases. Heart health is important for everyone. Heart needs to be monitored regularly and early warning can prevent the permanent heart damage. Also heart diseases are the leading cause of death worldwide. Hence the work presents a design of a mini wearable ECG system and it’s interfacing with the Android application. This framework is created to show and analyze the ECG signal got from the ECG wearable system. The ECG signals will be shipped off an android application via Bluetooth device. This system will automatically alert the user through SMS.

scholarly journals Recent Research for Unobtrusive Atrial Fibrillation Detection Methods Based on Cardiac Dynamics Signals: A Survey

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3814
Author(s):  
Fangfang Jiang ◽  
Yihan Zhou ◽  
Tianyi Ling ◽  
Yanbing Zhang ◽  
Ziyu Zhu
Keyword(s):  
Atrial Fibrillation ◽  
Detection Methods ◽  
Ecg Signals ◽  
Non Invasive ◽  
Cardiac Dynamics ◽  
Feature Extraction And Selection ◽  
Future Work ◽  
Electrocardiogram Ecg ◽  
Term Monitoring

Atrial fibrillation (AF) is the most common cardiac arrhythmia. It tends to cause multiple cardiac conditions, such as cerebral artery blockage, stroke, and heart failure. The morbidity and mortality of AF have been progressively increasing over the past few decades, which has raised widespread concern about unobtrusive AF detection in routine life. The up-to-date non-invasive AF detection methods include electrocardiogram (ECG) signals and cardiac dynamics signals, such as the ballistocardiogram (BCG) signal, the seismocardiogram (SCG) signal and the photoplethysmogram (PPG) signal. Cardiac dynamics signals can be collected by cushions, mattresses, fabrics, or even cameras, which is more suitable for long-term monitoring. Therefore, methods for AF detection by cardiac dynamics signals bring about extensive attention for recent research. This paper reviews the current unobtrusive AF detection methods based on the three cardiac dynamics signals, summarized as data acquisition and preprocessing, feature extraction and selection, classification and diagnosis. In addition, the drawbacks and limitations of the existing methods are analyzed, and the challenges in future work are discussed.

scholarly journals Field Deployable Fiber Bragg Grating Strain Patch for Long-Term Stable Health Monitoring Applications

2013 ◽  
Vol 3 (1) ◽  
pp. 39-54 ◽  
Author(s):  
Vivien Schukar ◽  
Nadine Kusche ◽  
Gerhard Kalinka ◽  
Wolfgang Habel
Keyword(s):  
Fiber Bragg Grating ◽  
Health Monitoring ◽  
Bragg Grating ◽  
Monitoring Applications

Soft Dry Electrodes for Electrocardiogram with Conductive Silver Nanowires

2014 ◽  
Vol 1685 ◽  
Author(s):  
Amanda Myers ◽  
Yong Zhu
Keyword(s):  
Health Monitoring ◽  
Silver Chloride ◽  
Silver Nanowires ◽  
Skin Irritation ◽  
Hospital Setting ◽  
Skin Impedance ◽  
Ecg Signals ◽  
Dry Electrode ◽  
Silver Silver

ABSTRACTWith increasing attention towards long-term health monitoring, there is a pressing need to create noninvasive sensors that monitor vital bioelectronic signals. Particular importance is placed on measuring electrocardiogram (ECG) signals as heart issues are widespread and can be prevented with the proper warning and care of potential problems. Currently, ECGs are taken in a hospital setting using disposable silver-silver chloride (Ag/AgCl) pre-gelled electrodes. Unfortunately, this cannot translate to a long-term monitoring setting due to the electrolytic gel of the electrodes drying and causing skin irritation. This paper presents a soft, skin-mountable dry electrode based on silver nanowires (AgNWs) for measuring ECG signals that can be used in long-term, wearable health monitoring due to the elimination of the electrolytic gel. The AgNWs are embedded in polydimethylsiloxane (PDMS), which creates a robust design that will not suffer from delamination or cracking problems that can eventually lead to loss of conductivity. The electrode is characterized by electrode-skin impedance as a function of frequency and by the surface resistance as the electrode is stretched. The performance of the dry electrode is evaluated and comparable to that of conventional Ag/AgCl electrodes. The ability of the dry electrode to conform to skin is believed to compensate for the lack of an electrolytic gel.

scholarly journals Robust Heartbeat Classification for Wearable Single-Lead ECG via Extreme Gradient Boosting

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5290
Author(s):  
Huaiyu Zhu ◽  
Yisheng Zhao ◽  
Yun Pan ◽  
Hanshuang Xie ◽  
Fan Wu ◽  
...  
Keyword(s):  
Gradient Boosting ◽  
Signal Acquisition ◽  
Ecg Monitoring ◽  
Daily Lives ◽  
Heartbeat Classification ◽  
Ecg Analysis ◽  
Ecg Signals ◽  
Extreme Gradient Boosting ◽  
Electrocardiogram Ecg ◽  
Monitoring Devices

Wearable electrocardiogram (ECG) monitoring devices have enabled everyday ECG collection in our daily lives. However, the condition of ECG signal acquisition using wearable devices varies and wearable ECG signals could be interfered with by severe noises, resulting in great challenges of computer-aided automated ECG analysis, especially for single-lead ECG signals without spare channels as references. There remains room for improvement of the beat-level single-lead ECG diagnosis regarding accuracy and efficiency. In this paper, we propose new morphological features of heartbeats for an extreme gradient boosting-based beat-level ECG analysis method to carry out the five-class heartbeat classification according to the Association for the Advancement of Medical Instrumentation standard. The MIT-BIH Arrhythmia Database (MITDB) and a self-collected wearable single-lead ECG dataset are used for performance evaluation in the static and wearable ECG monitoring conditions, respectively. The results show that our method outperforms other state-of-the-art models with an accuracy of 99.14% on the MITDB and maintains robustness with an accuracy of 98.68% in the wearable single-lead ECG analysis.

scholarly journals Learning Explainable Time-Morphology Patterns for Automatic Arrhythmia Classification from Short Single-Lead ECGs

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4331
Author(s):  
Hyeonjeong Lee ◽  
Miyoung Shin
Keyword(s):  
Health Monitoring ◽  
State Of The Art ◽  
Variable Length ◽  
Model Parameters ◽  
Visual Interpretation ◽  
Fixed Size ◽  
Ecg Signals ◽  
Cardiac Health ◽  
Learning Framework ◽  
Electrocardiogram Ecg

Automatic detection of abnormal heart rhythms, including atrial fibrillation (AF), using signals obtained from a single-lead wearable electrocardiogram (ECG) device, is useful for daily cardiac health monitoring. In this study, we propose a novel image-based deep learning framework to classify single-lead ECG recordings of short variable length into several different rhythms associated with arrhythmias. By transforming variable-length 1D ECG signals into fixed-size 2D time-morphology representations and feeding them to the beat–interval–texture convolutional neural network (BIT-CNN) model, we aimed to learn the comprehensible characteristics of beat shape and inter-beat patterns over time for arrhythmia classification. The proposed approach allows feature embedding vectors to provide interpretable time-morphology patterns focused at each step of the learning process. In addition, this method reduces the number of model parameters needed to be trained and aids visual interpretation, while maintaining similar performance to other CNN-based approaches to arrhythmia classification. For experiments, we used the PhysioNet/CinC Challenge 2017 dataset and achieved an overall F1_NAO of 81.75% and F1_NAOP of 76.87%, which are comparable to those of the state-of-the-art methods for variable-length ECGs.

scholarly journals Sensitivity and Adjustment Model of Electrocardiographic Signal Distortion Based on the Electrodes’ Location and Motion Artifacts Reduction for Wearable Monitoring Applications

2021 ◽  
Author(s):  
Fabian Castaño ◽  
Alher Mauricio Hernández
Keyword(s):  
Vital Signs ◽  
Motion Artifacts ◽  
Signal Acquisition ◽  
Ecg Signal ◽  
Signal Distortion ◽  
Ecg Signals ◽  
Vital Signs Monitoring ◽  
Significant Difference ◽  
Monitoring Applications ◽  
Artifacts Reduction

Wearable vital signs monitoring and specially the electrocardiogram have taken important role due to the information that provide about high-risk diseases, it has been evidenced by the needed to increase the health service coverage in home care as has been encouraged by WHO. Some wearables devices have been developed to monitor the ECG in which the location of the measurement electrodes is modified respect to the Einthoven model. However, mislocation of the electrodes on the torso can lead to the modification of acquired signals, diagnostic mistakes and misinterpretation of the information in the signal. This work presents a volume conductor evaluation and an ECG signal waveform comparison when the location of electrodes is changed, to find a electrodes’ location that reduces distortion of interest signals. In addition, effect of motion artifacts and electrodes’ location on the signal acquisition are evaluated. A group of volunteers was recorded to obtain ECG signals, the result was compared with a computational model of the heart behavior through the EA ECG, DTW and SNR methods to quantitatively determine the signal distortion. It was found that while the Einthoven method is followed, it is possible to acquire the ECG signal from the patient’s torso or back without a significant difference, and the electrodes position can be moved 6cm at most from the suggested location by the Einthoven triangle in Mason-Likar’s method.

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