Biomedical Implantable Low-Power Fault-Tolerant Systems

Abstract

The study provides an Independent component analysis strategy for filtering out artefacts and decoupling the Electrocardiogram (ECG) from the Ballistocardiogram (BCG) signal. The Independent Component Analysis (ICA) block is used to decompose the original signals (BCG+ECG). An initial 100 iterations are used to update the random values used to define the mixing and un-mixing matrices until convergence is reached. The mean and standard deviation are used to evaluate the filtering performance of a BCG signal. The J-J interval, I-J-K interval, RJ interval, and heart rate are identified in the BCG signals after they have been recovered from the ICA block.We have used these findings to determine if the signal originates from a healthy or unwell individual. The patient's aberrant condition is a strong argument for the use of implanted devices. In this experiment, we use three distinct sensors. The BCG information is collected by placing three sensors at various depths below the flat bed on which the subject is resting. The findings are analysed by taking into account all of the sensor data, and then compared to the outcomes produced by using either of the two sensors alone.