A Predictive Model for ECG Signals Collected from Specialized IoT Devices using Deep Learning

Early detection and prediction of cardiac anomalies play an important role in the diagnosis and treatment of cardiovascular diseases. In medicine, electrocardiography provides valuable information for the doctors since they can accurately determine what is happening concerning the heart activities. Nevertheless, electrocardiography classification is a non-trivial challenge due to the specialties of these data as well as the reliability of manual data collection methods. With the recent advancement of the IoT technologies, some wearable IoT devices for electrocardiography monitoring have been developed. However, the data collected from those devices, though possibly automatic, pose more challenging issues for the problem of electrocardiography classification. In this paper, we propose a novel solution for electrocardiography signal classification based on Deep Learning by combining Auto Encoder and Long-Short Term Memory models to handle data collected from intelligent IoT devices Shimmer and VitalSigns Holter.