Novel fusion-based time-frequency analysis for early prediction of sudden cardiac death from electrocardiogram signals

Abstract

Sudden cardiac death (SCD) is one of the leading causes of global mortality, often occurring without warning and driven by complex cardiac dynamics. Despite significant advances in cardiovascular diagnostics, accurately predicting SCD at an early stage remains a critical challenge. This study proposes a novel fusion-based time-frequency (T-F) deep learning framework for the early prediction of SCD by classifying associated cardiac conditions. Electrocardiogram (ECG) signals were first denoised and segmented to isolate clinically relevant patterns. These signals were then transformed into two-dimensional T-F representations using spectrograms and scalograms, capturing complementary temporal and spectral information. An average fusion technique merged these representations, enriching T-F images with enhanced discriminatory power. The fused images were used to train deep learning (DL) models, and performance was evaluated using subject-wise data splits to assess generalizability across individuals. The proposed approach achieved a classification accuracy of 94.60 %, effectively identifying cardiac conditions associated with SCD one hour before its onset. This fusion-based framework shows strong potential for integration into real-time, automated diagnostic systems, enabling early warning, personalized monitoring, and timely intervention to reduce fatal outcomes.