Key result
ECG classification model using DWT and SVM achieves ~99% accuracy in detecting arrhythmias.
Why the study?
ECG abnormalities may indicate cardiac diseases requiring immediate medical attention, making abnormal ECG detection critical for patients.
Does DWT feature extraction with PCA and SVM accurately classify normal vs abnormal ECG signals compared to other feature extraction methods?
Does DWT feature extraction with PCA and SVM accurately classify normal vs abnormal ECG signals compared to other feature extraction methods?
A machine learning model using Discrete Wavelet Transform for feature extraction, PCA, and SVM classification achieved 98.7% accuracy in detecting cardiac arrhythmias from ECG signals.
High accuracy supports automated ECG screening potential; leaves open prospective clinical validation and generalizability.
Electrocardiogram (ECG) is a graphical representation of the electrical activity of the heart obtained by placing various electrodes on specific areas of the subject's body surface. Abnormalities in a patient's ECG signal may indicate cardiac diseases that require immediate medical attention. As a result, detecting an abnormal ECG is critical for the patient's benefit. This work develops a method for classifying ECG signals as normal or abnormal. In this paper, we propose a method for detecting cardiac arrhythmias in electrocardiograms (ECG). In the first stage, the proposal focuses on various feature extractor methods. The Fourier Transform (DFT), Discrete Wavelet Transform (DWT), and Improved complete ensemble empirical mode decomposition with adaptive noise were the feature extraction techniques evaluated (ICEEMDAN). The PCA method is then used to reduce the number of features. Finally, for classification, the Support Vector Machine (SVM) was used, which was trained using the features extracted in the first stage. The proposed models are tested using datasets from MIT-BIH arrhythmia and PTB Diagnostics. The experimental results show that using 3-PCs with the DWT method produces better results than the other methods, which achieve 98.7% in terms of accuracy.
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Eman Ahmad Khorsheed (2023) studied Cardiac arrhythmias. Discrete Wavelet Transform (DWT) with 3-PCs and SVM vs. Fourier Transform (DFT) and ICEEMDAN was evaluated on Classification accuracy. An ECG classification model using Discrete Wavelet Transform (DWT) with 3 principal components and a Support Vector Machine achieved 98.7% accuracy in detecting cardiac arrhythmias.
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