Key result
Novel hybrid feature-based LSTM framework proposed to advance multiclass ECG arrhythmia classification.
Why the study?
Many existing automated ECG analysis studies have concentrated on a small number of arrhythmia classes, making classification harder as the number of classes grows. This review surveys ECG signal preprocessing, feature extraction, and classification techniques and proposes a hybrid feature-based LSTM approach for multiclass arrhythmia classification.
This review surveys automated ECG preprocessing, feature extraction, and arrhythmia classification techniques, proposing a hybrid feature-based LSTM framework to improve multiclass arrhythmia detection.
Automated ECG classification may streamline arrhythmia detection; leaves open optimal preprocessing standards for clinical integration.
An Electrocardiogram (ECG) is a key detection method employed to examine the electrical activity of heart and to detect cardiac abnormalities and arrhythmias. As the number of patients with cardiac disorders continues to rise, automated ECG analysis and classification methods have been designed to help medical professionals analyze ECG recordings. ECG analysis typically involves several phases, such as signal preprocessing, extraction of relevant features, and classification. Preprocessing is a crucial step, since ECG recordings may include various kinds of noise that can influence the analysis that follows. A range of denoising techniques, such as frequency-domain filters, adaptive filters, and Wavelet Transform (WT) based methods have been studied to enhance the ECG signal’s quality. Various feature types, including morphological, statistical, and temporal features, as well as Fourier Transform and wavelet-based features, have likewise been examined for ECG classification. Previous studies have investigatedconventional deep learning and machine learning approaches for arrhythmia classification, while hybrid feature-based methods have been examined to provide complementary information from ECG signals. Nevertheless, many existing studies have concentrated on a small number of arrhythmia classes, which makes classification harder as the number of classes grows. Thus, this review surveys existing techniques for ECG signal preprocessing, extraction of relevant features, and classification, and points out areas that warrant further investigation. Drawing on the reviewed literature, a hybrid feature-based approach that uses an LSTM-based classification approach is proposed for multiclass ECG arrhythmia classification. The proposed approach will examine how fiducial, temporal, morphological, statistical, and Wavelet Transform (WT)-based features can be combined, and their potential to improve classification performance and support the classification of a larger number of arrhythmia classes.
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Deepanshu et al. (2026) conducted a review in Cardiac arrhythmias. Hybrid feature-based LSTM classification approach was evaluated on Multiclass ECG arrhythmia classification performance. A hybrid feature-based LSTM approach is proposed for multiclass ECG arrhythmia classification after surveying existing preprocessing and feature extraction methods (no quantitative results reported).
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