An ensemble model for mobile device based arrhythmia detection

Recent advances in smart mobile device technology have resulted in global availability of portable computing devices capable of performing many complex functions. With the ultimate intent of promoting human's well-being, mobile device based arrhythmia detection (MAD) has attracted lots of attention recently. Without any guidance or supervision from experts, the performance of arrhythmia detection is usually unsatisfactory. Supervised learning can learn from labeled cardiac cycles to detect arrhythmias for each mobile device user if enough training data is provided. However, it is time-consuming, costly and sometimes impossible to let experts annotate enough training data for each user. To tackle this problem, we take advantage of publicly available and well annotated data to infer knowledge which can be treated as experts for MAD. To reduce the space usage of the framework, we extract from each source of labeled data an expert model, which consists of a task-independent individual characteristic vector and a task-related preference vector. Multiple experts are then integrated into an ensemble model for arrhythmia detection. Both space and time complexities of this proposed approach are theoretically analyzed and experimentally examined. To evaluate the performance of the method, we implement it on the MIT-BIH Arrhythmia Dataset and compare it with seven state-of-the-art methods in the area. Extensive experimental results show that the proposed algorithm outperforms all the baseline methods, which validates the effectiveness of the proposed algorithm in MAD.