Deep Learning for Morphological Arrhythmia Classification in Encoded ECG Signal

This paper introduces a technique for encoding ECG signals before transmission from a compact, low-power wearable ECG sensor in real time to a host device. Using the encoded ECG signal received via a Bluetooth link, a deep learning neural network model is proposed for execution on the host device to detect and classify arrhythmia in real time. The resulting system, called ACES (Arrhythmia Classification using Encoded ECG Signals), can be used for critical cardiac health monitoring and advanced real-time diagnostics. ACES employs a Bidirectional Long-Short Term Memory (BiLSTM) based neural network model to detect and identify six distinct classes of arrhythmia with a high degree of accuracy. Each of these six classes corresponds to a morphologically unique arrhythmia. A separate class is used for normal ECG signals ("normal sinus rhythm"). Data from the MIT-BIH Arrhythmia dataset and human subjects were used in the evaluation of a prototype system, following appropriate IRB protocols for human subjects testing. The ECG signal encoding also saves considerable energy in transmitting data to the host device as only a small amount of encoded data is transmitted per ECG cycle instead of a full set of ECG signal samples.