Abnormal ECG signal detection based on compressed sampling in Wearable ECG sensor

Nowadays to diagnose cardiac arrhythmias Holter device is used to record 1 or 2 ECG leads during 24 or 48h. Power consumption limitations determine that the amount of data needs to be diminished without damaging the quality of information. To get a solution, we introduce a novel method based on Compressed Sensing (CS) technique to the Wearable ECG sensor (WES). The main principle underlying this framework is to sample analog signals at sub-Nyquist rate at the analog-digital converters (ADCs) and to classify directly compressed measurement into normal and abnormal state. Those compressed measurements which imply a risk of cardiac anomaly will be stored in a multimedia flash memory card or be transferred to the terminal of the network for a cardiologist to make an off-line diagnosis of cardiac arrhythmias using the reconstructed signals from the compressed measurements. In this paper we propose a scheme to directly classify compressed ECG samples into normal or abnormal states, thus avoiding reconstruction of the entire signal to perform this task. Our algorithm takes advantage of estimating parameters directly from the compressed measurements; thereby eliminating the reconstruct stage and reducing the computational complexity in WES. Direct cardiac arrhythmia detection based on CS reduces 34% energy consumption and 90% storage in WES for the reconstructed performance of 41dB.