Level-crossing processing and deep convolutional neural network for arrhythmia classification in telehealth services

Telehealthcare is an evolving area that typically employs cloud-connected wireless biomedical gadgets for diagnosis, monitoring, and prognosis of diseases. In such environment, data compression, transmission, security and processing effectiveness are key issues. This paper proposes a new method for the automated diagnosis of arrhythmia in an efficient and effective manner. The proposed technique fuses a combination of Level-Crossing Analog-Digital Converters (LCADCs), Enhanced Activity Selection Algorithm (EASA), Adaptive-Rate Filtering (ARF), and ID-CNN. The electrocardiogram (ECG) signal is sampled by using the level-crossing concept. The QRS based segmentation and ARF with lower tap filters are realized. The denoised segments, without any handcrafted features extraction, are classified with one dimensional (1-D) deep convolutional neural network (CNN). Comparison is performed with using statistically extracted features in combination with CNN, existing state-of-the-art classical methods for ECG classification, and recent advanced deep learning models. The goal is to reach an efficient method by attaining a real-time data size reduction, computationally efficient signal preconditioning and a lower latency accurate classification. Five clinically important classes of arrhythmias, collected from the MIT-BIH dataset, are used to examine its applicability. Our experimental results show a 4.2-times diminishing in the count of acquired samples, on average, compared to conventional fix-rate counterparts. Similarly, data dimension reduction results in a more than 7.2-times computational effectiveness of the post denoising stage over the conventional counterparts. Moreover, classification latency is also significantly reduced while still achieving an accuracy rate of 99%.