Thesis
Run time Analysis of LSTM models for Real Time Arrhythmia Classification
Washington State University
Master of Science (MS), Washington State University
01/2021
DOI:
https://doi.org/10.7273/000001875
Handle:
https://hdl.handle.net/2376/121894
Abstract
Arrhythmia is the irregular rhythm of a problematic heartbeat that is caused by an issue with the electrical impulses that control the heart. These issues can lead to harmful cardiovascular deceases if not diagnosed and treated. In order to detect such irregularities in the heart, the resulting electric impulses from the contraction and relaxation of the heart muscles can be detected with the use of an Electrocardiograph which is examined by a trained professional. These heartbeats follows a general pattern with a priority in focus around the QRS complex. This pattern can be leveraged to train a neural network for anomaly detection by detecting when the impulses are different from what is expected for a normal heartbeat pattern. To extract these patterns, small sections of the time-series electrocardiographs will be extracted around the QRS complex and prepared by de-noising the signal with a bandpass filter, then normalized using an L2 normalization algorithm before being passed to a neural network. This neural network utilizes a Long-Short-Term-Memory layer as its core and is capable of classifying the differences in the heartbeat as one of eighteen different heartbeats with an accuracy of 97\% within 55ms when ran on a single-board computer like a Raspberry Pi 4. This demonstrates that a neural network can be used along side a portable electrocardiogram to provide real time diagnosis on arrhythmia without the requirement of lengthy hospital stays or expensive equipment as the neural network can keep up with the heart-rate of an individual while running on cheap, portable computing systems.
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Details
- Title
- Run time Analysis of LSTM models for Real Time Arrhythmia Classification
- Creators
- Tyler Dean Petty
- Contributors
- Xinghui Zhao (Advisor)Scott Wallace (Committee Member)Paul Bonamy (Committee Member)
- Awarding Institution
- Washington State University
- Academic Unit
- Engineering and Computer Science (VANC), School of
- Theses and Dissertations
- Master of Science (MS), Washington State University
- Publisher
- Washington State University
- Number of pages
- 41
- Identifiers
- 99900606650901842
- Language
- English
- Resource Type
- Thesis