Towards numerical temporal-frequency system modelling of associations between ballistocardiogram and electrocardiogram

Abstract

Ballistocardiogram (BCG) is a bio signal which is measured and recorded by the mechanical activity of the heart (ballistic forces). Due to technological advancements in the recent years BCG has regained its interest and has become an active field of research. In this thesis we try to establish a statistical approach by building BCG-ECG models. Here we would like to promote the system modelling approach to BCG computing that allows to explore the underlying association between BCG and other physiological signals such as electrocardiogram (ECG). This is in contrast to most of the existing works in the related signal processing domain, which focus on detecting heart rate only.The system modelling approach may eventually improve the clinical significance of the BCG by extracting deeply embedded information. Towards this goal, here we present our preliminary study where we design a wavelet-based temporal-frequency system model for associating BCG and ECG. BCG trial data were acquired from 4 different subjects for analysis and a suitable protocol was followed during the process. The data were synchronized with the ECG for building transfer function models and a novel algorithm was proposed and adapted for increasing the efficiency of the model. The developed model is compared with the original ECG using Root Mean Square Error (RMSE) as a metric.