scholarly journals A Novel Heart Rate Robust Method for Short-Term Electrocardiogram Biometric Identification

2019 ◽  
Vol 9 (1) ◽  
pp. 201 ◽  
Author(s):  
Di Wang ◽  
Yujuan Si ◽  
Weiyi Yang ◽  
Gong Zhang ◽  
Tong Liu
Keyword(s):  
Heart Rate ◽  
Principal Component ◽  
Identification Accuracy ◽  
Ecg Signal ◽  
Identification Task ◽  
Short Term ◽  
Ecg Signals ◽  
Subject Identification ◽  
Novel Method ◽  
Electrocardiogram Ecg

In the past decades, the electrocardiogram (ECG) has been investigated as a promising biometric by exploiting the subtle discrepancy of ECG signals between subjects. However, the heart rate (HR) for one subject may vary because of physical activities or strong emotions, leading to the problem of ECG signal variation. This variation will significantly decrease the performance of the identification task. Particularly for short-term ECG signal without many heartbeats, the hardly measured HR makes the identification task even more challenging. This study aims to propose a novel method suitable for short-term ECG signal identification. In particular, an improved HR-free resampling strategy is proposed to minimize the influence of HR variability during heartbeat processing. For feature extraction, the Principal Component Analysis Network (PCANet) is implemented to determine the potential difference between subjects. The proposed method is evaluated using a public ECG-ID database that contains various HR data for some subjects. Experimental results show that the proposed method is robust to HR change and can achieve high subject identification accuracy (94.4%) on ECG signals with only five heartbeats. Thus, the proposed method has the potential for application to systems that use short-term ECG signals for identification (e.g., wearable devices).

scholarly journals Multiscale Distribution Entropy Analysis of Short-Term Heart Rate Variability

Entropy ◽  
2018 ◽  
Vol 20 (12) ◽  
pp. 952 ◽  
Author(s):  
Dae-Young Lee ◽  
Young-Seok Choi
Keyword(s):  
Heart Rate ◽  
Time Series ◽  
Heart Rate Variability ◽  
Coarse Grained ◽  
Multiscale Entropy ◽  
Short Term ◽  
Ecg Signals ◽  
Improved Stability ◽  
Moving Averaging ◽  
Electrocardiogram Ecg

Electrocardiogram (ECG) signal has been commonly used to analyze the complexity of heart rate variability (HRV). For this, various entropy methods have been considerably of interest. The multiscale entropy (MSE) method, which makes use of the sample entropy (SampEn) calculation of coarse-grained time series, has attracted attention for analysis of HRV. However, the SampEn computation may fail to be defined when the length of a time series is not enough long. Recently, distribution entropy (DistEn) with improved stability for a short-term time series has been proposed. Here, we propose a novel multiscale DistEn (MDE) for analysis of the complexity of short-term HRV by utilizing a moving-averaging multiscale process and the DistEn computation of each moving-averaged time series. Thus, it provides an improved stability of entropy evaluation for short-term HRV extracted from ECG. To verify the performance of MDE, we employ the analysis of synthetic signals and confirm the superiority of MDE over MSE. Then, we evaluate the complexity of short-term HRV extracted from ECG signals of congestive heart failure (CHF) patients and healthy subjects. The experimental results exhibit that MDE is capable of quantifying the decreased complexity of HRV with aging and CHF disease with short-term HRV time series.

scholarly journals Systematic Approach to Processing and Analysis Diagnostic Indicators of Electrocardiograms Based on Labview

2020 ◽  
Vol 23 (2) ◽  
pp. 82-88
Author(s):  
M. T. Magrupova ◽  
Yo. T. Talatov
Keyword(s):  
Heart Rate ◽  
Complete Information ◽  
Primary Diagnosis ◽  
Integrated System ◽  
Ecg Signal ◽  
Programming System ◽  
Transform Method ◽  
Important Place ◽  
Ecg Signals ◽  
Electrocardiogram Ecg

Introduction. Cardiovascular disease occupies an important place throughout the world, which necessitates the development of more effective modern means of diagnosis and treatment. The primary diagnosis of heart disease is based on analysis and processing of an electrocardiogram (ECG). Despite the fact that there are many methods and algorithms for ECG analysis and processing, one of the urgent problems of cardiology remains to obtain the most complete information about heart electric potential, respectively, the behavior of the waves P, Q, R, S and T.Aim. Development of algorithms and software for processing and analysis of electrocardiograms (ECGs), as well as calculation of heart rate and detection of arrhythmias based on Labview.Materials and methods. The methods for removing noise using the wavelet transform method to eliminate baseline deviation ,to extract ECG signs ,to calculate heart rate and to detect arrhythmias based on Labview have been adopted as a mathematical apparatus for processing and analyzing ECGs.Results. Organizing of the ECG database, developing algorithms for converting the ECG file of the database into a useful format for Labview, processing of the ECG signal with removing noise from the original ECG signal, extracting signs for obtaining ECG diagnostic indicators, calculating heart rate and detecting arrhythmias.Conclusion. An analysis of the results demonstrates that systematic approaches to evaluating ECG signals allow to avoid one-way decisions and to integrate different methods into an integrated system of ideas of the state. The implementation of the proposed algorithms using Labview programming system ensures the removal of noise and artifacts, the extraction of the necessary ECG signs, the calculation of heart contractions and the detection of arrhythmias.

scholarly journals Differential effects of the blood pressure state on pulse rate variability and heart rate variability in critically ill patients

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Elisa Mejía-Mejía ◽  
James M. May ◽  
Mohamed Elgendi ◽  
Panayiotis A. Kyriacou
Keyword(s):  
Blood Pressure ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Pulse Rate ◽  
Pulse Transit Time ◽  
Pulse Rate Variability ◽  
Physiological Factors ◽  
Ecg Signals ◽  
Non Invasive ◽  
Electrocardiogram Ecg

AbstractHeart rate variability (HRV) utilizes the electrocardiogram (ECG) and has been widely studied as a non-invasive indicator of cardiac autonomic activity. Pulse rate variability (PRV) utilizes photoplethysmography (PPG) and recently has been used as a surrogate for HRV. Several studies have found that PRV is not entirely valid as an estimation of HRV and that several physiological factors, including the pulse transit time (PTT) and blood pressure (BP) changes, may affect PRV differently than HRV. This study aimed to assess the relationship between PRV and HRV under different BP states: hypotension, normotension, and hypertension. Using the MIMIC III database, 5 min segments of PPG and ECG signals were used to extract PRV and HRV, respectively. Several time-domain, frequency-domain, and nonlinear indices were obtained from these signals. Bland–Altman analysis, correlation analysis, and Friedman rank sum tests were used to compare HRV and PRV in each state, and PRV and HRV indices were compared among BP states using Kruskal–Wallis tests. The findings indicated that there were differences between PRV and HRV, especially in short-term and nonlinear indices, and although PRV and HRV were altered in a similar manner when there was a change in BP, PRV seemed to be more sensitive to these changes.

scholarly journals Two Electrode ECG And EOG System For Monitoring Applications

2019 ◽  
Vol 8 (11) ◽  
pp. 262-265
Keyword(s):  
Reference Electrode ◽  
Cost Effective ◽  
Electrode System ◽  
Ecg Signal ◽  
Biomedical Signal ◽  
Ecg Signals ◽  
Arm Position ◽  
The Stability ◽  
Electrocardiogram Ecg ◽  
Total Gain

Heart and Eye are two vital organs in the human system. By knowing the Electrocardiogram (ECG) and Electro-oculogram (EOG), one will be able to tell the stability of the heart and eye respectively. In this project, we have developed a circuit to pick the ECG and EOG signal using two wet electrodes. Here no reference electrode is used. EOG and ECG signals have been acquired from ten healthy subjects. The ECG signal is obtained from two positions, namely wrist and arm position respectively. The picked-up biomedical signal is recorded and heart rate information is extracted from ECG signal using the biomedical workbench. The result found to be promising and acquired stable EOG and ECG signal from the subjects. The total gain required for the arm position is higher than the wrist position for the ECG signal. The total gain necessary for the EOG signal is higher than the ECG signal since the ECG signal is in the range of millivolts whereas EOG signal in the range of microvolts. This two-electrode system is stable, cost-effective and portable while still maintaining high common-mode rejection ratio (CMRR).

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 Fractal Based Analysis of the Influence of Odorants on Heart Activity

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Hamidreza Namazi ◽  
Vladimir V. Kulish
Keyword(s):  
Heart Rate ◽  
Heart Diseases ◽  
Approximate Entropy ◽  
Ecg Signal ◽  
Heart Activity ◽  
Heart Research ◽  
Olfactory Stimuli ◽  
Important Challenge ◽  
External Stimuli ◽  
Electrocardiogram Ecg

Abstract An important challenge in heart research is to make the relation between the features of external stimuli and heart activity. Olfactory stimulation is an important type of stimulation that affects the heart activity, which is mapped on Electrocardiogram (ECG) signal. Yet, no one has discovered any relation between the structures of olfactory stimuli and the ECG signal. This study investigates the relation between the structures of heart rate and the olfactory stimulus (odorant). We show that the complexity of the heart rate is coupled with the molecular complexity of the odorant, where more structurally complex odorant causes less fractal heart rate. Also, odorant having higher entropy causes the heart rate having lower approximate entropy. The method discussed here can be applied and investigated in case of patients with heart diseases as the rehabilitation purpose.

A Novel Method for the Detection of Sleep Apnea Syndrome Based on Single-Lead ECG Signal

2012 ◽  
Vol 239-240 ◽  
pp. 1079-1083 ◽  
Author(s):  
Yue Wen Tu ◽  
Xiao Min Yu ◽  
Hang Chen ◽  
Shu Ming Ye
Keyword(s):  
Sleep Apnea ◽  
Sleep Apnea Syndrome ◽  
Support Vector ◽  
Ecg Signal ◽  
Respiratory Signal ◽  
Peak Points ◽  
Novel Method ◽  
Apnea Syndrome ◽  
Prevention Of Hypertension ◽  
Electrocardiogram Ecg

The diagnosis of sleep apnea syndrome (SAS) has important clinical significance for the prevention of hypertension, coronary heart disease, arrhythmias, stroke and other diseases. In this paper, a novel method for the detection of SAS based on single-lead Electrocardiogram (ECG) signal was proposed. Firstly, the R-peak points of ECG recordings were pre-detected to calculate RR interval series and ECG-derived respiratory signal (EDR). Then 40 time- and spectral-domain features were extracted and normalized. Finally, support vector machine (SVM) was employed to these features as a classifier to detect SAS events. The performance of the presented method was evaluated using the MIT-BIH Apnea-ECG database, results show that an accuracy of 95% in train sets and an accuracy of 88% in test sets are achievable.

DISCRETE WAVELET TRANSFORM APPLIED ON PERSONAL IDENTITY VERIFICATION WITH ECG SIGNAL

2009 ◽  
Vol 07 (03) ◽  
pp. 341-355 ◽  
Author(s):  
CHUANG-CHIEN CHIU ◽  
CHOU-MIN CHUANG ◽  
CHIH-YU HSU
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Personal Identity ◽  
Rejection Rate ◽  
Normal Subjects ◽  
Discrete Wavelet ◽  
Ecg Signal ◽  
Identity Verification ◽  
Ecg Signals ◽  
Electrocardiogram Ecg

The main purpose of this study is to present a novel personal authentication approach with the electrocardiogram (ECG) signal. The electrocardiogram is a recording of the electrical activity of the heart and the recorded signals can be used for individual verification because ECG signals of one person are never the same as those of others. The discrete wavelet transform was applied for extracting features that are the wavelet coefficients derived from digitized signals sampled from one-lead ECG signal. By the proposed approach applied on 35 normal subjects and 10 arrhythmia patients, the verification rate was 100% for normal subjects and 81% for arrhythmia patients. Furthermore, the performance of the ECG verification system was evaluated by the false acceptance rate (FAR) and false rejection rate (FRR). The FAR was 0.83% and FRR was 0.86% for a database containing only 35 normal subjects. When 10 arrhythmia patients were added into the database, FAR was 12.50% and FRR was 5.11%. The experimental results demonstrated that the proposed approach worked well for normal subjects. For this reason, it can be concluded that ECG used as a biometric measure for personal identity verification is feasible.

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