Deep learning and neural architecture search for cardiac arrhythmias classification

Author Identifiers

najmeh fayyazifar https://orcid.org/0000-0002-3905-3797

Date of Award


Degree Type


Degree Name

Doctor of Philosophy


School of Science

First Advisor

David Suter

Second Advisor

Selam Ahderom

Third Advisor

Andrew Maiorana


Cardiovascular disease (CVD) is the primary cause of mortality worldwide. Among people with CVD, cardiac arrhythmias (changes in the natural rhythm of the heart), are a leading cause of death. The clinical routine for arrhythmia diagnosis includes acquiring an electrocardiogram (ECG) and manually reviewing the ECG trace to identify the arrhythmias. However, due to the varying expertise level of clinicians, accurate diagnosis of arrhythmias with similar visual characteristics (that naturally exists in some different types of arrhythmias) can be challenging for some front-line clinicians. In addition, there is a shortage of trained cardiologists globally, and especially in remote areas of Australia, where patients are sometimes required to wait for weeks or months for a visiting cardiologist. This impacts the timely care of patients living in remote areas. Therefore, developing an AI-based model, that assists clinicians in accurate real-time decision-making, is an essential task. This thesis provides supporting evidence that the problem of delayed and/or inaccurate cardiac arrhythmias diagnosis can be addressed by designing accurate deep learning models through Neural Architecture Search (NAS). These models can automatically differentiate different types of arrhythmias in a timely manner.

Many different deep learning models and more specifically, Convolutional Neural Networks (CNNs) have been developed for automatic and accurate cardiac arrhythmias detection. However, these models are heavily hand-crafted which means designing an accurate model for a given task, requires significant trial and error. In this thesis, the process of designing an accurate CNN model for 1-dimensional biomedical data classification is automated by applying NAS techniques. NAS is a recent research paradigm in which the process of designing an accurate model (for a given task) is automated by employing a search algorithm over a pre-defined search space of possible operations in a deep learning model.

In this thesis, we developed a CNN model for detection of ‘Atrial Fibrillation’ (AF) among ‘normal sinus rhythm’, ‘noise’, and ‘other arrhythmias. This model is designed by employing a well-known NAS method, Efficient Neural Architecture Search (ENAS) which uses Reinforcement Learning (RL) to perform a search over common operations in a CNN structure. This CNN model outperformed state-of-the-art deep learning models for AF detection while minimizing human intervention in CNN structure design.

In order to reduce the high computation time that was required by ENAS (and typically by RL-based NAS), in this thesis, a recent NAS method called DARTS was utilized to design a CNN model for accurate diagnosis of a wider range of cardiac arrhythmias. This method employs Stochastic Gradient Descent (SGD) to perform the search procedure over a continuous and therefore differentiable search space. The search space (operations and building blocks) of DARTS was tailored to implement the search procedure over a public dataset of standard 12-lead ECG recordings containing 111 types of arrhythmias (released by the PhysioNet challenge, 2020). The performance of DARTS was further studied by utilizing it to differentiate two major sub-types of Wide QRS Complex Tachycardia (Ventricular Tachycardia- VT vs Supraventricular Tachycardia- SVT). These sub-types have similar visual characteristics, which makes differentiating between them challenging, even for experienced clinicians. This dataset is a unique collection of Wide Complex Tachycardia (WCT) recordings, collected by our medical collaborator (University of Ottawa heart institute) over the course of 11 years. The DARTS-derived model achieved 91% accuracy, outperforming cardiologists (77% accuracy) and state-of-the-art deep learning models (88% accuracy).

Lastly, the efficacy of the original DARTS algorithm for the image classification task is empirically studied. Our experiments showed that the performance of the DARTS search algorithm does not deteriorate over the search course; however, the search procedure can be terminated earlier than what was designated in the original algorithm. In addition, the accuracy of the derived model could be further improved by modifying the original search operations (excluding the zero operation), making it highly valuable in a clinical setting.

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