A Resource-Efficient Cardiac Arrhythmia Detection Using Nonlinear Dynamics in Optimized Delay State Networks.

Abstract

In this study, a novel methodology is proposed, combining Reconstructed Phase Space (RPS) analysis with an optimized Delay State Network (DSN) to enhance the detection and classification of cardiac arrhythmias. Traditional methods often fail to capture subtle temporal phase drifts indicative of arrhythmias or require extensive computational resources and handcrafted features, limiting their effectiveness for early diagnosis and real-time applicability. The proposed approach reconstructs the nonlinear dynamics of cardiac signals and leverages the entire Phase Space Structure (PSS) as direct input to the DSN. The optimized DSN employs a single nonlinear node with delayed feedback to emulate multiple virtual nodes, reducing hardware demands by over an order of magnitude compared to conventional reservoirs or LSTMs. To accurately capture ECG dynamics, the framework integrates delay and embedding optimization, while PCA and Ridge Embedding manage dimensionality within the DSN. The functionality of the DSN model is further optimized by incorporating shared memory and multiprocessing frameworks, enabling scalable and efficient handling of large datasets. The methodology was validated on three benchmark datasets, demonstrating its generalizability across diverse cardiac conditions. Experimental results achieved 99.3% accuracy, with sensitivity and specificity of 99.1% and 99.7%, respectively. Edge deployment on a Raspberry Pi 5 demonstrated inference within $1.2\!-\!4.8$ seconds for $ 10\,s\!-\!60\,s$ ECG segments, with peak memory usage of 2.57 GB observed for 60 s segments, and power consumption remaining below 2.5 W. The proposed framework provides a robust, scalable, and accurate solution for arrhythmia classification and broader time-series-based diagnostics in resource-constrained environments.