A robust automatic mechanism for electrocardiogram interpretation in telehealthcare.

Telehealthcare has become increasingly popular in clinical practice as a means of providing ubiquitous healthcare through long-term informative interactions and health monitoring. We have delivered a synchronized telehealthcare program since 2009. We have implemented a web-based clinical decision support system with a knowledge-based electrocardiogram (ECG) recognition mechanism as an augmentation service to assist medical practitioners doing decision making in clinical practice. To evaluate the capability and usage limits of this automatic ECG interpretation, the aim of this study was to validate the stability and robustness of proposed mechanism using stress testing through six simulation scenarios. According to experimental results, both of the processing items and processing time augmented steadily by the resource of hardware. Besides, under the cross-validation using 327,058 ECG signals from our telehealthcare program, the recognition classifiers yielded 86.8% accuracy in sinus detection and 88.4% accuracy in atrial fibrillation detection. In the future, this prominent mechanism of automatic ECG interpretation could widely offer high accessibility in the field of medical service. The findings of the present study also encourage and augment further support to implementation of screening and monitoring as part of telehealthcare.