Cardiovascular diseases (CVD), defined as a spectrum of disorders primarily impacting the heart and the circulatory system, account for a substantial fraction of worldwide morbidity and mortality. Electrocardiograms (ECGs) are routinely implemented in a patient’s diagnosis, both in hospitals and outpatient settings. They serve as one of the primary diagnostic tools as patients encounter medical personnel, particularly in suspected CVD. A cardiac holter monitor is a medical diagnostic device, connected to the patient via several conductive leads placed across the chest, and "worn" on a strap across the shoulder. A holter is applied to record continuous ECG data (typically 24 hours). With recently emerging applications of Machine Learning (ML) in data analysis techniques, the need for human expertise and potential human error could be minimized, and prediction accuracy optimized considerably. Hence, the objective of this research is to develop a machine learning approach to eventually aid physicians with their decisions as a powerful guiding/assisting tool to analyse the ECG information reported by holter monitors. Furthermore, we aim to develop a computer aided diagnostic system that can assist expert cardiologists by providing intelligent, cost effective, and time-saving diagnosis. In this thesis, we implement a deep learning-based solution to analyse readings of 24-holter monitors. In our proposed solution, we train neural networks to extract high-level features from temporal signal recordings of holter monitors. We present a supervised neural network framework to predict the physician’s final interpretations based on the holter recorded signals. The outputs of the network contain the likelihood of four possible scenarios of Normal and three types of arrhythmias. The high classification performance of the proposed methodology emphasizes the capability of this framework to be used as an assisting tool alongside the physicians to interpret holter reports.
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Thesis advisor: Rawicz, Andrew
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