Cardiac abnormality detection with a tiny diagonal state space model based on sequential liquid neural processing unit

Abstract

This manuscript introduces a novel method for cardiac abnormality detection by combining the Diagonal State Space Sequence (S4D) model with the Closed-form Continuous-time neural network (CfC), yielding a highly effective, robust, generalizable, and compact solution. Our proposed S4D-CfC model is evaluated on 12- and single-lead electrocardiogram data from over 20 000 patients. The system exhibits validation results with strong average F1 score and average area under the receiver operating characteristic curve values of 0.88% and 98%, respectively. To demonstrate the tiny machine learning of our 242 KB size model, we deployed the system on relatively resource-constrained hardware to evaluate its training performance on-the-edge. Such on-device fine-tuning can enhance personalized solutions in this context, allowing the system to learn each patient’s data features. A comparison with a structured 2D convolutional long short-term memory CfC model demonstrates the S4D-CfC model’s superior performance. The proposed model’s size can be significantly reduced to 25 KB, maintaining reasonable performance on 2.5 s data, 75% shorter than the original 10 s data, making it suitable for resource-constrained hardware and minimizing latency. In summary, the S4D-CfC model represents a groundbreaking advancement in cardiac abnormality detection, offering robustness, generalization, and practicality with the potential for efficient deployment on limited-resource platforms, revolutionizing healthcare technology.