Why the study?
ECG monitoring is central to the early detection of cardiac abnormalities, requiring effective feature strategies for real-time screening.
Combining signal-specific and signal-independent features using deep learning models like CNNs can achieve highly accurate, real-time beat-by-beat ECG classification suitable for wearable devices.
Hybrid feature sets may aid arrhythmia detection research; leaves open clinical translation without prospective validation.
ECG monitoring is central to the early detection of cardiac abnormalities. We compared 28 manually selected signal-specific features with 159 automatically extracted signal-independent descriptors from the MIT BIH Arrhythmia Database. ANOVA reduced features to the 10 most informative attributes, which were evaluated alone and in combination with the signal-specific features using Random Forest, SVM, and deep neural networks (CNN, RNN, ANN, LSTM) under an interpatient 80/20 split. Merging the two feature groups delivered the best results: a 128-layer CNN achieved 100% accuracy. Power profiling revealed that deeper models improve accuracy at the cost of runtime, memory, and CPU load, underscoring the trade-off faced in edge deployments. The proposed hybrid feature strategy provides beat-by-beat classification with a reduction in the number of features, enabling real-time ECG screening on wearable and IoT devices.
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Tsai et al. (2025) studied this question.
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