Discrete Wavelet Transforms-Based Analysis of Accelerometer Signals for Continuous Human Cardiac Monitoring

Measuring cardiac activity from the chest using an accelerometer is commonly referred to as seismocardiography. Unfortunately, it cannot provide clinically valid data because easily corrupted by motion artefacts. This paper proposes two methods improve peak detection noisy seismocardiography data. They rely on discrete wavelet transform analysis either biorthogonal 3.9 or reverse 3.9. The first method involves slicing vibrations for each activity, and then detecting location, whereas other aims at directly without segmentation. Performance evaluations were conducted signals recorded small children adults based missing additional peaks. Both algorithms showed a low error rate (15.4% 2.1% children/infants adults, respectively) obtained in resting state. average sitting breathing tasks (adults only) was 14.4%. In summary, algorithm proved more promising further exploration.