Imperial College London
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ProfessorArnabMajumdar

Faculty of Engineering, Department of Civil and Environmental Engineering

Professor of Transport Risk and Safety
 
 
 
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Contact

 

+44 (0)20 7594 6037a.majumdar

 
 
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Assistant

 

Ms Maya Mistry +44 (0)20 7594 6100

 
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Location

 

604Skempton BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Mastoi:2022:10.3390/life12060842,
author = {Mastoi, Q-U-A and Wah, TY and Mohammed, MA and Iqbal, U and Kadry, S and Majumdar, A and Thinnukool, O},
doi = {10.3390/life12060842},
journal = {Life},
pages = {842--842},
title = {Novel DERMA fusion technique for ECG heartbeat classification},
url = {http://dx.doi.org/10.3390/life12060842},
volume = {12},
year = {2022}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - An electrocardiogram (ECG) consists of five types of different waveforms or characteristics (P, QRS, and T) that represent electrical activity within the heart. Identification of time intervals and morphological appearance of the waves are the major measuring instruments to detect cardiac abnormality from ECG signals. The focus of this study is to classify five different types of heartbeats, including premature ventricular contraction (PVC), left bundle branch block (LBBB), right bundle branch block (RBBB), PACE, and atrial premature contraction (APC), to identify the exact condition of the heart. Prior to the classification, extensive experiments on feature extraction were performed to identify the specific events from ECG signals, such as P, QRS complex, and T waves. This study proposed the fusion technique, dual event-related moving average (DERMA) with the fractional Fourier-transform algorithm (FrlFT) to identify the abnormal and normal morphological events of the ECG signals. The purpose of the DERMA fusion technique is to analyze certain areas of interest in ECG peaks to identify the desired location, whereas FrlFT analyzes the ECG waveform using a time-frequency plane. Furthermore, detected highest and lowest components of the ECG signal such as peaks, the time interval between the peaks, and other necessary parameters were utilized to develop an automatic model. In the last stage of the experiment, two supervised learning models, namely support vector machine and K-nearest neighbor, were trained to classify the cardiac condition from ECG signals. Moreover, two types of datasets were used in this experiment, specifically MIT-BIH Arrhythmia with 48 subjects and the newly disclosed Shaoxing and Ningbo People’s Hospital (SPNH) database, which contains over 10,000 patients. The performance of the experimental setup produced overwhelming results, which show around 99.99% accuracy, 99.96% sensitivity, and 99.9% specificity.
AU  - Mastoi,Q-U-A
AU  - Wah,TY
AU  - Mohammed,MA
AU  - Iqbal,U
AU  - Kadry,S
AU  - Majumdar,A
AU  - Thinnukool,O
DO  - 10.3390/life12060842
EP  - 842
PY  - 2022///
SN  - 2075-1729
SP  - 842
TI  - Novel DERMA fusion technique for ECG heartbeat classification
T2  - Life
UR  - http://dx.doi.org/10.3390/life12060842
UR  - https://www.mdpi.com/2075-1729/12/6/842
UR  - http://hdl.handle.net/10044/1/97681
VL  - 12
ER  -
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