Sparse-input Detection Algorithm with Applications in Electrocardiography and Ballistocardiography

Abstract

Sparse-input learning, especially of inputs with some form of periodicity, is of major importance in biosignal processing, including electrocardiography and ballistocardiography. Ballistocardiography (BCG), the measurement of forces on the body, exerted by heart contraction and subsequent blood ejection, allows noninvasive and non-obstructive monitoring of several key biomarkers such as the respiration rate, the heart rate and the cardiac output. In the following we present an efficient online multi-channel algorithm for estimating single heart beat positions and their approximate strength using a statistical hypothesis test. The algorithm was validated with 10 minutes long ballistocardiographic recordings of 12 healthy subjects, comparing it to synchronized surface ECG measurements. The achieved mean error rate for the heart beat detection excluding movement artifacts was 4:7%.