Automated Heart Arrhythmia Detection from Electrocardiographic Data

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He, Jinyuan (2020) Automated Heart Arrhythmia Detection from Electrocardiographic Data. PhD thesis, Victoria University.


Heart arrhythmia is a severe heart problem, which threatens people’s lives by pre- venting their hearts from pumping enough blood into vital organs. Arrhythmia has been a major worldwide health problem for years, accounting for nearly 12% of global deaths every year. The research of automated heartbeat classification is highly demanded, which provides a cost-effective screening for heart arrhythmia and allows at-risk patients to receive timely treatments. To construct an effective automated heartbeat classification model from ECG recordings for arrhythmia de- tection, several key challenges must be addressed, including data quality, heartbeat segmentation range, data imbalance problem, intra and inter-patients variations, identification of supraventricular ectopic heartbeats from normal heartbeats, and model interpretability. This thesis comprehensively discusses these challenges and proposes four practical models to gradually tackle the heartbeat classification task. Specifically, in Chapter 3, a model named D-ECG is proposed to solve the problems suffered by previous methods of applying a standalone classifier and us- ing a static feature set to classify all heartbeat types. D-ECG introduces the dynamic ensemble selection techniques in heartbeat classification for the first time and incorporates a result regulator to improve the disease heartbeats detection performance. Although the dynamic ensemble selection technique has introduced visible improvements in the heartbeat classification task, they also brought some disadvantages. The dynamic selection nature, which determines the best classifiers according to the sample to be predicted, can result in a delay of the model predic- tion, making the model less practical in online detection scenarios. In Chapter 4, the author proposes a novel pyramid-like model to tackle this problem. The model adopts a dual-channel classification strategy and customizes a binary classification algorithm that takes neighbor-related information into account to assist disease heartbeats detection. Compared to the D-ECG framework, the pyramid-like model can provide more timely response to an unknown heartbeat while maintaining a good classification performance as the D-ECG framework. It has the potential to be applied in online detection scenarios. In Chapter 5, the author examines the recent advances brought by deep neural networks and proposes a DNN-based solution named Multi-channels Convolution Neural Network (MCHCNN) to solve the problems of current deep-learning based heartbeat classification models. As an improvement, the proposed network accepts raw ECG heartbeat and heart rhythm (RR-intervals) as inputs and uses different sizes of convolution filters in parallel to capture temporal and frequency patterns from ECG signals. The experimental results have shown visible improvements brought by MCHCNN. However, there is still a long way before MCHCNN can make practical impacts because its performance of S-type heartbeats detection is still relatively low. To tackle this problem, the author investigates the potential causes to the problem and proposes an advanced two-step DNN-based classification framework in Chapter 6. Due to the observed difficulty of detecting S-type heart- beats from N -type heartbeats, the proposed framework trains a deep dual-channel convolutional neural network (DDCNN) which accepts segmented heartbeats as input in the first step to classify V-type, F-type and Q-type heartbeats. At this stage, S-type and N-type heartbeats are not the targets, so they are put into one bundle to be studied in the next step. In the second step, a central-towards LSTM supportive model (CLSM) is specially designed to distinguish S-type heart- beats from N-type ones. The RR-intervals of a heartbeat and its neighbors are arranged in sequence form, serving as the input to CLSM. In particular, CLSM learns and extracts hidden temporal dependency between heartbeats by processing the input RR-interval sequence in central-towards directions. Instead of using raw individual RR-intervals, the abstractive, mutual-connected temporal information provides stronger and more stable support for identifying the problematic S-type heartbeats. Besides, as an improvement as well as a necessary driver for activating the CLSM, a rule-based data augmentation method is also proposed to supply high-quality synthetic samples for the under-represented S-type RR-interval se- quences. Extensive experiments are conducted to provide a comprehensive evaluation for each proposed model. The results prove that the research of heartbeat classification presented in this thesis brings practical ideas and solutions to the arrhythmia detection problem.

Item type Thesis (PhD thesis)
Subjects Historical > FOR Classification > 0801 Artificial Intelligence and Image Processing
Current > Division/Research > College of Science and Engineering
Keywords artificial intelligence; heart arrhythmia; Cardiac Arrhythmia; electrocardiographic data; ECG; heartbeat classification; D-ECG
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