Domain Adaptation of ECG Signals Using a Fuzzy Energy–Frequency Spectrogram Network

Deep learning has shown strong performance in ECG domain adaptation; however, its decision-making process remains opaque, particularly when operating on input spectrograms. Traditional fuzzy inference offers interpretability but is structurally limited to tabular or multi-channel data, making it difficult to apply directly to single-channel two-dimensional spectrograms. To address this limitation, we propose the Fuzzy Energy–Frequency Spectrogram Network (FEFSN), a new fuzzy–deep learning hybrid framework that enables direct fuzzy rule generation in the spectrogram domain. In FEFSN, the Fuzzy Rule Image Generation Module (FRIGM) decomposes an STFT-transformed ECG spectrogram into multiple energy-based channels using an Energy–density Membership Function (EMF), and then applies a Frequency Membership Function (FMF) to produce AND and OR fuzzy rule images for each energy–frequency combination. The generated rule images are subsequently normalized, activated, and combined through learned weights to form a rule-based domain-adapted spectrogram, which is then processed by a CNN. To evaluate the proposed approach, we used the PhysioNet ECG-ID dataset and compared the performance of a standard CNN with and without the FRIGM under identical training conditions. The results show that FEFSN maintains or slightly improves adaptation performance compared to the baseline CNN, despite introducing only a small number of additional parameters. More importantly, FEFSN provides ante hoc interpretability, allowing direct visualization of which energy–frequency regions were emphasized or suppressed during adaptation—an ability that conventional post hoc methods such as Grad-CAM cannot offer. Overall, FEFSN demonstrates that fuzzy logic can be effectively integrated with deep learning to achieve both reliable performance and transparent, rule-based interpretability in ECG spectrogram domain adaptation.