MACHINE LEARNING AND TELEMEDICINE FOR THE EARLY DIAGNOSIS AND TREATMENT OF CARDIOVASCULAR DISEASES

Abstract

This thesis examined the application of contemporary technology, such as artificial intelligence and telemedicine, to enhance the existing cardiac patient monitoring and digital diagnostic systems. The research specifically aimed to improve the monitoring of individuals with cardiac problems. The proposed methods enable real-time, remote monitoring of cardiac patients and enhanced arrhythmia prediction abilities. From 271 million in 1990 to 523 million in 2019, cardiovascular diseases (CVDs) have nearly doubled. CVD-related fatalities have grown as well from 12.1 million in 1990 to 20.5 million in 2021, a rise of 53%. This persistent increase in cardiovascular diseases places a substantial strain on the healthcare system, particularly in poorer nations with higher CVD rates due to a lack of financial resources. We developed a mobile application named 'Dhadkan' to minimise the number of hospital visits and expand the availability and cost of renowned doctors to the whole Indian population. The use of automated ECG analysis methods in clinical practise is hampered by a large level of uncertainty resulting from patient variables. In a multi-modal design, none of the ECG annotators that have been created to date account for patient characteristics. We analysed the UCI Arrhythmia dataset using the XGBoost model, correlating patient features to ECG morphological changes. The model recognised patient gender with 87.75% confidence using discriminative ECG characteristics. We propose a multi-modal architecture for ECG analysis and arrhythmia classification that can help defy the variability in ECG owing to patient-specific circumstances. This approach, rECGnition (robust ECG abnormality detection), combines Beat Morphology and Patient Characteristics to generate a discriminative feature map that comprehends the internal correlation of both modalities. Consequently, we developed a self-attentive canonical fusion method for ECG and patient data using DL for effective cardiac diagnostics. Furthermore, DL was also used to develop a programme that digitises 12-lead paper electrocardiograms. This was done so that we could increase both the speed with which ECGs are evaluated and the portability of the data they contain.