A novel single-arm-worn 24 h heart disease monitor empowered by machine intelligence

A novel single-arm-worn ECG-based heart disease monitor is proposed in this paper. It is of a potential to provide continuous monitoring of different ECG metrics, and in this study, we focus on the duration of the QRS complex which is the central of an ECG heartbeat. Firstly, to avoid the low wearability induced by traditional chest-ECG or two-wrist ECG, we apply a highly wearable non-standard single-arm-ECG configuration. Afterwards, to estimate the QRS duration from noisy and weak non-standard single-arm-ECG, we propose a new three-stage machine learning framework. It firstly identifies heartbeat locations (R peaks) by a support vector machine classifier, then uses a dynamic time warping approach to locate QRS patterns that are similar to a template learned by a K-medoids clustering method, and finally learns to use the arm-ECG-based QRS duration estimates to predict a standard chest-ECG-based QRS duration trend. Experimental results demonstrate the effectiveness of this novel system, based on data collected from five subjects using our customized hardware prototype and the non-standard signal-arm-ECG configuration. To the best of our knowledge, this is the first study on the a single-arm-worn ECG-based daily heart disease monitor, using advanced signal sensing and machine learning techniques.