A novel method for detecting R-peaks in electrocardiogram (ECG) signal

2012 ◽  
Vol 7 (2) ◽  
pp. 118-128 ◽  
Author(s):  
M.Sabarimalai Manikandan ◽  
K.P. Soman
Keyword(s):  
Ecg Signal ◽  
Novel Method ◽  
Electrocardiogram Ecg

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.

scholarly journals Individual Identification Using Linear Projection of Heartbeat Features

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
Yogendra Narain Singh
Keyword(s):  
Healthy Subjects ◽  
Orthogonal Basis ◽  
Individual Identification ◽  
Support Vector ◽  
Ecg Signal ◽  
Linear Projection ◽  
Novel Method ◽  
Electrocardiogram Ecg ◽  
Better Than

This paper presents a novel method to use the electrocardiogram (ECG) signal as biometrics for individual identification. The ECG characterization is performed using an automated approach consisting of analytical and appearance methods. The analytical method extracts the fiducial features from heartbeats while the appearance method extracts the morphological features from the ECG trace. We linearly project the extracted features into a subspace of lower dimension using an orthogonal basis that represent the most significant features for distinguishing heartbeats among the subjects. Result demonstrates that the proposed characterization of the ECG signal and subsequently derived eigenbeat features are insensitive to signal variations and nonsignal artifacts. The proposed system utilizing ECG biometric method achieves the best identification rates of 85.7% for the subjects of MIT-BIH arrhythmia database and 92.49% for the healthy subjects of our IIT (BHU) database. These results are significantly better than the classification accuracies of 79.55% and 84.9%, reported using support vector machine on the tested subjects of MIT-BIH arrhythmia database and our IIT (BHU) database, respectively.

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).

CARDIAC ARRHYTHMIA DIAGNOSIS USING A NEURO-FUZZY APPROACH

2010 ◽  
Vol 10 (03) ◽  
pp. 417-429 ◽  
Author(s):  
R. BENALI ◽  
N. DIB ◽  
F. REGUIG BEREKSI
Keyword(s):  
Fuzzy Neural Network ◽  
Ecg Signal ◽  
Qrs Detection ◽  
Medical Database ◽  
Fuzzy Classifier ◽  
Signal Parameters ◽  
Neuro Fuzzy ◽  
Fuzzy Neural ◽  
Novel Method ◽  
Electrocardiogram Ecg

The ventricular premature contractions (VPC) are cardiac arrhythmias that are widely encountered in the cardiologic field. They can be detected using the electrocardiogram (ECG) signal parameters. A novel method for detecting VPC from the ECG signal is proposed using a new algorithm (Slope) combined with a fuzzy-neural network (FNN). To achieve this objective, an algorithm for QRS detection is first implemented, and then a neuro-fuzzy classifier is developed. Its performances are evaluated by computing the percentages of sensitivity (SE), specificity (SP), and correct classification (CC). This classifier allows extraction of rules (knowledge base) to clarify the obtained results. We use the medical database (MIT-BIH) to validate our results.

Autoencoder for ecg signal outlier processing in system of biometric authentication

2019 ◽  
Vol 24 (1-2) ◽  
pp. 108-117
Author(s):  
Khoma V.V. ◽  
◽  
Khoma Y.V. ◽  
Khoma P.P. ◽  
Sabodashko D.V. ◽  
...  
Keyword(s):  
Neural Networks ◽  
Statistical Approach ◽  
Main Idea ◽  
Biometric Identification ◽  
Ecg Signal ◽  
Biometric Authentication ◽  
Lower Accuracy ◽  
Novel Method ◽  
Waveform Distortions ◽  
The One

A novel method for ECG signal outlier processing based on autoencoder neural networks is presented in the article. Typically, heartbeats with serious waveform distortions are treated as outliers and are skipped from the authentication pipeline. The main idea of the paper is to correct these waveform distortions rather them in order to provide the system with better statistical base. During the experiments, the optimum autoencoder architecture was selected. An open Physionet ECGID database was used to verify the proposed method. The results of the studies were compared with previous studies that considered the correction of anomalies based on a statistical approach. On the one hand, the autoencoder shows slightly lower accuracy than the statistical method, but it greatly simplifies the construction of biometric identification systems, since it does not require precise tuning of hyperparameters.

WBAN Based Long Term ECG Monitoring

2013 ◽  
Vol 1 (3) ◽  
pp. 20-37 ◽  
Author(s):  
Marius Rosu ◽  
Sever Pasca
Keyword(s):  
Low Power ◽  
Sensor Nodes ◽  
Ecg Signal ◽  
Area Network ◽  
Wireless Sensor Nodes ◽  
Medication Process ◽  
Remote Healthcare ◽  
Electrocardiogram Ecg ◽  
Supply Information

Healthcare solutions using anytime, and anywhere remote healthcare surveillance devices, have become a major challenge. The patients with chronic diseases who need only therapeutic supervision are not advised to occupy a hospital bed. Using Wearable Wireless Body/Personal Area Network (WWBAN), intelligent monitoring of heart can supply information about medical conditions. Electrocardiogram (ECG) is the core reference in the diagnosis and medication process. An approach on healthcare solution WBAN based, for real-time ECG signal monitoring and long-term recording will be presented. Low-power wireless sensor nodes with local processing and encoding capabilities in order to achieve maximum mobility and flexibility are our main goal. ZigBee wireless technology will be used for transmission. Sensor device will be programmed to process locally the ECG signal and to raise an alert. Low-power and miniaturization are essential physical requirements.

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.

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