Memristor-based echo state network for ECG classification: optimizing accuracy and efficiency with different architectures

Electrocardiogram (ECG) signal classification is a crucial task in automated cardiac monitoring and early disease detection. Conventional machine learning and deep learning methods often face challenges related to high computational costs, large data requirements and difficulty in handling real-time ECG analysis. In this work, we have proposed three memristor-based echo state network (MESN) models for ECG classification: (1) MESN, (2) Parallel MESNs (PMESN) with a classifier and (3) Attention-enhanced MESN (AMESN), where all three models employ artificial neural network (ANN) as a readout layer. These models leverage the property of an ESN to transform ECG signals into high-dimensional representations, thereby enhancing classification performance. The proposed models were evaluated on different benchmark ECG datasets: ECG200, ECG5000, ECGFiveDays, NonInvasiveFetalECGThorax1, NonInvasiveFetalECGThorax2, and TwoLeadECG. The results demonstrate that AMESN achieves the highest precision, recall, F1-score and overall accuracy particularly excelling in datasets with high intra-class variability. Whereas, PMESN improves robustness by leveraging multiple reservoir layers, while MESN provides a strong baseline with computational efficiency. These models are also compared with other state-of-the-art models and the analysis shows that AMESN achieves 1.92-13.39% higher accuracy, demonstrating superior performance. These findings highlight the potential of ESN as a lightweight yet powerful approach for ECG classification, making it suitable for integration into wearable and IoT-based healthcare systems.