Efficient Machine Learning Algorithms for One- and Two-dimensional Biomedical Signals

Abstract

Data analysis plays a crucial role in healthcare when it comes to diagnosing and detecting illnesses and medical conditions. Thanks to the advancements in data computing and machine learning, healthcare professionals can leverage this technology to their advantage. There is a vast amount of biomedical data available, ranging from patient records to medical imaging and genomic sequencing, as well as clinical trial results. By analyzing this data with the help of machine learning, healthcare professionals can gain valuable insights that could lead to more accurate diagnoses, better treatment options, and improved health outcomes for patients. It’s worth noting that any discovery made through data analysis has the potential to enhance the quality of life for individuals. This dissertation presents a successful method for detecting life-threatening ventricular arrhythmias, namely ventricular tachycardia, ventricular fibrillation, and ventricular flutter, through the use of machine learning algorithms. The method leverages various statistical features and is capable of detecting these arrhythmias over different ECG signal durations. Our method can efficiently differentiate ventricular tachycardia/fibrillation/flutter (VTFL) against normal sinus rhythm (NSR) with an accuracy, recall and positive predictive value of 98.21, 95.57 and 98.61 percents respectively. The discriminatory power of the same algorithm between VTFL and non-VTFL as characterized by accuracy, recall and positive predictive value are 98.12, 93.29, and 97.2 percents respectively. This dissertation also suggests a novel way to identify ST segment depression through a single ECG lead. The proposed method involves transforming one-dimensional ECG signals into two-dimensional images to efficiently detect ST segment depression, a key indicator of myocardial ischemia. The generalized algorithm (subject independent) developed, when analyzed using a convolutional neural network with 8, 16, and 32 filters in its consecutive convolutional layers yielded for ECG segments of 40 beats, a sensitivity, specificity and an accuracy of 91.18, 98.79 and 95.51 percents respectively. A personalized algorithm (subject dependent) built on the same CNN architecture as the generalized algorithm for detecting ST segment depression yielded a sensitivity, specificity and precision of 97.04, 99.72 and 98.90 percents respectively. In addition to one-dimensional ECG signals, this dissertation explores utilizing ultrasound images in combination with Generative Adversarial Networks (GANs) for data augmentation to enhance breast cancer detection. We achieved an accuracy of 90.2 percent, which is higher that any results reported for a single model. By employing a GAN-based approach to augment data, the detection process can be considerably improved, resulting in greater performance when identifying the disease from ultrasound breast images. This advancement is valuable for timely diagnosis and treatment, and has the ability to positively impact patient outcomes.