Design of high performance and power efficient electrocardiogram detectors for implantable cardiac pacemaker systems

Abstract

The new-age advancements in biomedical signal processing are due to circuits and systems which can process complex data, which made the healthcare facilities more compact and affordable. Among healthcare devices, cardiac pacemakers have become a recurrent biomedical device which is engrafted in the human body to monitor and detect a subject’s heart rate. Cardiovascular diseases (CVDs) or diseases related to the heart are due to abnormalities or disorders of the heart and blood vessels. Till date, limited literature is available, which focuses on a single technique that can perform ECG signal denoising, ECG signal detection, and lossless data compression. Current circuitry can be interpreted as a cardiac electrical signal compression algorithm representing the time signal information into a single event description of the cardiac activity. ECG signal detection techniques like an artificial neural network, genetic algorithm, Hilbert transform, hidden Markov model are some sophisticated algorithms which provide suitable results, but their realization using IC technologies is very complicated. Due to less complexity and high performance, wavelet transform based approaches are widely used. In this thesis, after a thorough analysis of various wavelet transforms, it is found that biorthogonal wavelet transform is best suitable to detect ECG signal. The main steps involved in the ECG detection process consist of de-noising and locating different ECG waves using adaptive slope prediction thresholding.