Spiking Neural Network Based Waveform Classification Structure with an Application on Arrhythmia Pattern Recognition
Date Issued
2013
Date
2013
Author(s)
Pai, Tsung-Hsueh
Abstract
Artificial neural network is a kind of machine learning tools; it’s a simplified model of the brain, imitating biological neural networks. The human brain is able to learn from experience, and has good performance on visual and audio signal processing. By imitating human brain neural networks, people expect to bring computers the same ability as human that can help people to solve various problems. Combined with neuroscience knowledge, a more physiological meaningful tool, spiking neural network, has been created. The spiking neural network transmits information by spike trains and imitates the membrane potential function of neurons. Hence spiking neural network has the better performance on classification and prediction, and the work function is more similar to the human brain. Now spiking neural network is used for neuroscience simulation and machine learning application. However, there are few machine learning applications of spiking neural network.
This study designed a spiking neural network based waveform classification structure with an application of arrhythmia pattern recognition. There was discussion of encoding methods and functionality of spiking neurons. Furthermore, we modified the Tempotron algorithm to improve the accuracy of prediction. At last, we got the good performance in the tests of MIT-BIH arrhythmia database and NTUH telehealth database. This study proposed a new application of spiking neural networks and proved the ability and potential of spiking neural networks.
Subjects
類神經網路
脈衝類神經網路
Tempotron學習演算法
心電圖
波形分類
機器學習
SDGs
Type
thesis
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