Efficient Compressive Sensing of ECG Segments Based on Machine Learning for QRS-Based Arrhythmia Detection

A novel method for efficient telemonitoring of arrhythmia based on using QRS complexes is proposed. Two features, namely, sum of absolute differences (SAD) and maximum of absolute differences (MAD) are efficiently computed for each ECG segment in the bio-sensor. The computed features can be transmitted from the bio-sensor using wireless channel, and they can be used in the receiver for determining the absence of QRS complex in the segment. By avoiding computationally expensive signal reconstruction for the ECG segments without QRS complex, it is shown, using simulation results, that computation time can be reduced by approximately 7.4 % for long-term telemonitoring of QRS-based arrhythmia. Detection of the absence of QRS complex can be carried out in around 7 milliseconds in a standard laptop computer with 2.2 GHz processor and 8 GB RAM.