EVALUATION OF QRS COMPLEX BASED ON DWT COEFFICIENTS ANALYSIS USING DAUBECHIES WAVELETS FOR DETECTION OF MYOCARDIAL ISCHAEMIA

This paper presents a new approach in the field of electrocardiogram (ECG) feature extraction system based on the discrete wavelet transform (DWT) coefficients using Daubechies Wavelets. Real ECG signals recorded in lead II configuration are chosen for processing. The ECG signal was acquired by a battery operated, portable ECG data acquisition and signal processing module. In the second step the ECG signal was denoised using soft thresholding with Symlet4 wavelet. Further denoising was achieved by removing the corresponding wavelet coefficients at higher levels of decomposition. Later the ECG data files were converted to .txt files and subsequently to. mat files before being imported into the Matlab 7.4.0 environment for the computation of the decomposition coefficients. The QRS complexes were grouped as normal or myocardial ischaemic ones based on these decomposition coefficients. The algorithm developed by us was evaluated with control database comprising 120 records and validated using 60 records making up test database. By using the DWT coefficients, we have successfully achieved the myocardial ischaemia detection rates up to 97.5% with the technique developed by us for control data and up to 100% for validation test data.

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DISCRETE WAVELET TRANSFORM APPLIED ON PERSONAL IDENTITY VERIFICATION WITH ECG SIGNAL

International Journal of Wavelets Multiresolution and Information Processing ◽

10.1142/s0219691309002957 ◽

2009 ◽

Vol 07 (03) ◽

pp. 341-355 ◽

Cited By ~ 31

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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Two Electrode ECG And EOG System For Monitoring Applications

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.k1315.0981119 ◽

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).

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Capturing Electrocardiogram Signals from Chairs by Multiple Capacitively Coupled Unipolar Electrodes

Sensors ◽

10.3390/s18092835 ◽

2018 ◽

Vol 18 (9) ◽

pp. 2835 ◽

Cited By ~ 3

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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A Wearable Electrocardiogram Telemonitoring System for Atrial Fibrillation Detection

Sensors ◽

10.3390/s20030606 ◽

2020 ◽

Vol 20 (3) ◽

pp. 606 ◽

Cited By ~ 3

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.

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QRS COMPLEX DETECTION USING OPTIMAL DISCRETE WAVELET

International Journal of Computational Intelligence and Applications ◽

10.1142/s1469026809002576 ◽

2009 ◽

Vol 08 (02) ◽

pp. 97-109

Author(s):

R. SHANTHA SELVA KUMARI ◽

S. BHARATHI ◽

V. SADASIVAM

Keyword(s):

Maximum Error ◽

Perfect Reconstruction ◽

Discrete Wavelet ◽

Ecg Signal ◽

Qrs Complex ◽

Time Frequency ◽

Qrs Complex Detection ◽

Reconstructed Signal ◽

Complex Detection ◽

Electrocardiogram Ecg

Wavelet transform has emerged as a powerful tool for time frequency analysis of complex nonstationary signals such as the electrocardiogram (ECG) signal. In this paper, the design of good wavelets for cardiac signal is discussed from the perspective of orthogonal filter banks. Optimum wavelet for ECG signal is designed and evaluated based on perfect reconstruction conditions and QRS complex detection. The performance is evaluated by using the ECG records from the MIT-BIH arrhythmia database. In the first step, the filter coefficients (optimum wavelet) is designed by reparametrization of filter coefficients. In the second step, ECG signal is decomposed to three levels using the optimum wavelet and reconstructed. From the reconstructed signal, the range of error signal is calculated and it is compared with the performance of other suitable wavelets already available in the literature. The optimum wavelet gives the maximum error range as 10-14–10-11 which is better than that of other wavelets existing in the literature. In the third step, the baseline wandering is removed from the ECG signal for better detection of QRS complex. The optimum wavelet detects all R peaks of all records. That is using optimum wavelet 100% sensitivity and positive predictions are achieved. Based on the performance, it is confirmed that optimum wavelet is more suitable for ECG signal.

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Anticipating Atrial Fibrillation Signal Using Efficient Algorithm

International Journal of Online and Biomedical Engineering (iJOE) ◽

10.3991/ijoe.v17i02.19183 ◽

2021 ◽

Vol 17 (02) ◽

pp. 106

Author(s):

Mohand Lokman Ahmad Al-dabag ◽

Haider Th. Salim ALRikabi ◽

Raid Rafi Omar Al-Nima

Keyword(s):

Atrial Fibrillation ◽

Computational Cost ◽

Extraction Methods ◽

Support Vector ◽

Ecg Signal ◽

Heart Problem ◽

Ecg Signals ◽

The Right ◽

Electrocardiogram Ecg ◽

Low Computational Cost

One of the common types of arrhythmia is Atrial Fibrillation (AF), it may cause death to patients. Correct diagnosing of heart problem through examining the Electrocardiogram (ECG) signal will lead to prescribe the right treatment for a patient. This study proposes a system that distinguishes between the normal and AF ECG signals. First, this work provides a novel algorithm for segmenting the ECG signal for extracting a single heartbeat. The algorithm utilizes low computational cost techniques to segment the ECG signal. Then, useful pre-processing and feature extraction methods are suggested. Two classifiers, Support Vector Machine (SVM) and Multilayer Perceptron (MLP), are separately used to evaluate the two proposed algorithms. The performance of the last proposed method with the two classifiers (SVM and MLP) show an improvement of about (19% and 17%, respectively) after using the proposed segmentation method so it became 96.2% and 97.5%, respectively.

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TWO-LEAD JOINT DETECTION ALGORITHM OF QRS COMPLEXES IN ECG SIGNAL

Journal of Mechanics in Medicine and Biology ◽

10.1142/s0219519421400728 ◽

2021 ◽

Author(s):

WANSONG XU ◽

TIANWU CHEN ◽

FANYU DU

Keyword(s):

Detection Algorithm ◽

Detection Accuracy ◽

Ecg Signal ◽

Joint Detection ◽

Ecg Signals ◽

Detection Algorithms ◽

Threshold Adjustment ◽

Current Algorithm ◽

Electrocardiogram Ecg

Objective: The detection of QRS complexes is an important part of computer-aided analysis of electrocardiogram (ECG). However, most of the existing detection algorithms are mainly for single-lead ECG signals, which requires high quality of signal. If the signal quality decreases suddenly due to some interference, then the current algorithm is easy to cause misjudgment or missed detection. To improve the detection ability of QRS complexes under sudden interference, we study the QRS complexes information on multiple leads in-depth, and propose a two-lead joint detection algorithm of QRS complexes. Methods: Firstly, the suspected QRS complexes are screened on the main lead. For the suspected QRS complexes with low confidence and the complexes that may be missed, further accurate detection and joint judgment shall be carried out at the corresponding position of the auxiliary lead. At the same time, the adaptive threshold adjustment algorithm and backtracking mechanism are used to modify the detection results. Results: The proposed detection algorithm is validated using 48 ECG records of the MIT-BIH arrhythmia database, and achieves average detection accuracy of 99.71%, sensitivity of 99.88% and positive predictivity of 99.81%. Conclusion: The proposed algorithm has high accuracy, which can effectively deal with the sudden interference of ECG signal. Meanwhile, the algorithm requires small amount of computation, and can be embedded into hardware for real-time detection.

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Predictive ECG coding using linear time-invariant models

Archive of oncology ◽

10.2298/aoo0403152b ◽

2004 ◽

Vol 12 (3) ◽

pp. 152-158 ◽

Cited By ~ 2

Author(s):

Aleksandar Boskovic ◽

Miroslav Despotovic ◽

Dragana Bajic

Keyword(s):

Linear Time ◽

Quality Criteria ◽

Ecg Signal ◽

Medical Doctors ◽

Digital Conversion ◽

Ecg Signals ◽

Linear Time Invariant ◽

Ecg Compression ◽

Time Invariant ◽

Electrocardiogram Ecg

Electrocardiogram (ECG) signal compression suffers of lack of standards for analogue-digital conversion. Results of this study have shown that 8 bits/sample, although frequently in use, does not satisfy quality criteria for medical doctors. This paper also presents predictive technique for lossless ECG compression using linear time-invariant models. Tests on clinically measured ECG signals confirm a very good performance in terms of compression ratio.

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Smartphone based ECG Acquisition and Analysis

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.35013 ◽

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.

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Electrocardiogram (ECG) based stress recognition integrated with different classification of age and gender

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v15.i1.pp199-210 ◽

2019 ◽

Vol 15 (1) ◽

pp. 199

Author(s):

N. S. Nor Shahrudin ◽

K. A. Sidek ◽

A. Z. Jusoh

Keyword(s):

Data Acquisition ◽

Stress Level ◽

Ecg Signal ◽

Alternative Mechanism ◽

Age And Gender ◽

Stress States ◽

And Gender ◽

Electrocardiogram Ecg ◽

Ecg Data

Good mental health is important in our daily life. A person commonly finds stress as a barrier to enhance an individual’s performance. Be reminded that not all people have the same level of stress because different people have dissimilar problems in their life. In addition, different level of age and gender will affect unequal amount of stress. Electrocardiogram (ECG) signal is an electrical indicator of the heart that can detect changes of human response which relates to our emotions and reactions. Thus, this research proposed a non-intrusive detector to identify stress level for both gender and different classification of age using the ECG. A total of 30 healthy subjects were involved during the data acquisition stage. Data acquisition which initialize ECG data were divided into two conditions; which are normal and stress states. ECG data for normal state only need the participant to breath in and out normally. In other hand, the participants also need to undergo Stroop Colour word test as a stress inducer to represent ECG in stress state. Then, Sgolay filter was selected in the pre-processing stage to remove artifacts in the signal. The process was followed by feature extraction of the ECG signal and finally classified using RR Interval (RRI), different amplitudes of R peaks and Cardioid graph were used to evaluate the performance of the proposed technique. As a result, Class 5 (age range between 50-59 years old) marks the highest changes of stress level rather than other classes, while women are more affected by stress rather than men by showing tremendous percentage changes between normal and stress level over the proposed classifiers. The result proves that ECG signals can be used as an alternative mechanism to recognize stress more efficiently with the integration of gender and age variabilities.

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