A novel approach for ECG signal classification using sliding Euclidean quantization and bitwise pattern encoding.

This study aims to introduce a novel, computationally lightweight feature extraction technique called Sliding Euclidean Pattern Quantization (SEPQ), which encodes local morphological patterns of ECG signals using Euclidean distance-based binary representations within sliding windows. The proposed SEPQ method was evaluated using two ECG datasets. The first dataset contained three labeled classes (CHF, ARR, and NSR), while the second included four classes (ventricular beats (VB), supraventricular beats (SVB), fusion beats (FB), and NSR). Extracted features were classified using several machine learning models, with LightGBM achieving the highest performance-over 99% accuracy on the first dataset and above 93% on the second. A convolutional neural network (CNN) model was also employed for comparative analysis, both on raw data and in a hybrid configuration with SEPQ, yielding moderate yet noteworthy performance. Experimental results confirm that SEPQ offers a robust, interpretable, and highly accurate solution for ECG signal classification.