Compressed Learning for Real-Time ECG Signals Classification

Electrocardiograms (ECGs) are essential for diagnosing cardiac conditions, yet high-fidelity 12-lead signals pose challenges for storage, transmission, and real-time processing. Compressed Sensing (CS) addresses these issues by exploiting signal sparsity, but its reliance on complex reconstruction algorithms limits practical deployment. Compressed Learning (CL) offers a more efficient alternative by enabling direct feature extraction from compressed measurements. This paper proposes an optimized CL framework for classifying arrhythmia (ARR), congestive heart failure (CHF), and normal sinus rhythm (NSR) from compressed ECG signals. We introduce a deterministic block-sparse binary sensing matrix with diagonal block allocation of ones and zeros elsewhere. This lightweight design eliminates floating-point multiplications, reduces memory overhead, and ensures uniform computational complexity across compression ratios. Compared with widely used Gaussian, Bernoulli, and Fourier sensing matrices, the proposed approach preserves ECG morphology more effectively and is well suited for embedded hardware. A deep learning-based Convolutional Neural Network (CNN) is developed and benchmarked against conventional machine learning classifiers, demonstrating superior classification accuracy across all tested compression ratios redachieving up to 96.56% accuracy, 96.5% F1-score, and 97.9% specificity at CR = 0.5, and outperforming Gaussian and Bernoulli matrices. To validate real-time feasibility, the proposed matrix is implemented on an STM32 NUCLEO-F401RE microcontroller. Hardware experiments confirm that the framework achieves high accuracy while significantly reducing energy consumption and computational load requiring only 9 μs per compression operation and consuming 0.641 μJ with constant-time execution across CRs. Furthermore, the proposed framework is validated on three widely used PhysioNet databases (MIT-BIH Arrhythmia, Normal Sinus Rhythm, and BIDMC Congestive Heart Failure), which together capture diverse patient populations and varying acquisition devices. This ensures robustness of the method and supporting its applicability in diverse clinical environments. Extending this framework to wearable ECG systems represents a promising direction for future research.