Authors:
F. Wadehn
;
L. Bruderer
;
D. Waltisberg
;
T. Keresztfalvi
and
Hans -A. Loeliger
Affiliation:
ETH Zurich, Switzerland
Keyword(s):
Ballistocardiography, Heart Rate Estimation, Hypothesis Test, Factor Graphs, System identification, Statespace Models, Maximum likelihood, Maximum a posteriori.
Related
Ontology
Subjects/Areas/Topics:
Applications and Services
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Cardiovascular Signals
;
Computer Vision, Visualization and Computer Graphics
;
Detection and Identification
;
Medical Image Detection, Acquisition, Analysis and Processing
;
Monitoring and Telemetry
;
Physiological Processes and Bio-Signal Modeling, Non-Linear Dynamics
Abstract:
Sparse-input learning, especially of inputs with some form of periodicity, is of major importance in bio-signal processing, including electrocardiography and ballistocardiography. Ballistocardiography (BCG), the measurement of forces on the body, exerted by heart contraction and subsequent blood ejection, allows non-invasive 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%.