Inter-patient electrocardiogram heartbeat classification with 2-D convolutional neural network
| dc.contributor.author | Ye, Kun | |
| dc.contributor.supervisor | Dong, Xiaodai | |
| dc.date.accessioned | 2021-01-26T01:03:54Z | |
| dc.date.available | 2021-01-26T01:03:54Z | |
| dc.date.copyright | 2021 | en_US |
| dc.date.issued | 2021-01-25 | |
| dc.degree.department | Department of Electrical and Computer Engineering | |
| dc.degree.level | Master of Applied Science M.A.Sc. | en_US |
| dc.description.abstract | Advanced computer technologies can transform the traditional electrocardiogram (ECG) monitoring system for better efficiency and accuracy. ECG records a heart's electrical activity using electrodes placed on the skin, and it has become an essential tool for arrhythmia detection. The complexity comes from the variety of patients' heartbeats and massive amounts of information for humans to process correctly. The first part of the thesis presents an image based two-dimensional convolution neural network (CNN) to classify the arrhythmia heartbeats with inter-patient paradigm. It includes a new data pre-processing method. The inter-patient paradigm simulates the practical use case of an ECG heartbeat classifier. Compared to the reported work in the literature, the proposed solution achieves superior experiment results. The rest of the thesis introduces the remote ECG monitoring system. The RESTful API design concepts of the system are described. The proposed API supports an efficient and secure way of interaction between each module in this remote monitoring system. | en_US |
| dc.description.scholarlevel | Graduate | en_US |
| dc.identifier.uri | http://hdl.handle.net/1828/12586 | |
| dc.language | English | eng |
| dc.language.iso | en | en_US |
| dc.rights | Available to the World Wide Web | en_US |
| dc.subject | ECG | en_US |
| dc.subject | arrhythmia | en_US |
| dc.subject | inter-patient paradigm | en_US |
| dc.subject | RESTful API design | en_US |
| dc.title | Inter-patient electrocardiogram heartbeat classification with 2-D convolutional neural network | en_US |
| dc.type | Thesis | en_US |