Fast and Resource Efficient Atrial Fibrillation Detection Framework for Long Term Health Monitoring Devices

This letter presents a novel single feature-based atrial fibrillation (AF) detection framework for addressing the critical challenge of resource constraints of affordable wearable health monitoring devices equipped with sensors. The proposed method consists of simple R peak detection for extracting R–R interval, calculation of Shannon entropy of word sequence of symbolic dynamics of heart rate sequence followed by AF/non-AF classification using six classifiers. On four standard databases, with 10 and 30 s electrocardiogram (ECG) segments, the sensitivity (SE) and specificity (SP) of the support vector machine is 100% and 99.92%–100%, respectively. For decision tree, random forest, multilayer perception, naive Bayes, and light gradient boosting algorithms, the SE is 100%, and SP ranges between 99.96% and 100% for 10 and 30 s ECG segments. For further analysis, the datasets with best performance are also tested with approximate entropy and k-nearest neighbor. The best model is decision tree with the lowest model size of 1.30–1.33 kB and processing time (PT) of 2.16 and 0.97 µs for 10 and 30 s segments, respectively. The realtime implementation on the Raspberry Pi computing platform demonstrates that all methods have small model size with memory space of 1.30–194 KB and PT of 4.82–56.7 µs, outperforming computationally expensive deep learning-based AF detection methods. The significance and importance of the framework lie in its ability to provide accurate AF detection with low PT and memory space using a single feature, making it suitable for resource-constrained long-term health monitoring devices.