以心電圖辨識為應用之脈衝類神經網路波形分類架構

Abstract

類神經網路(Artificial Neural Network)屬於機器學習的一種，為模仿人腦的神經系統所建構的簡化模型。由於人腦具有學習的功能，並且在對於視覺、聽覺處理方面皆有很好的表現，人們預期透過模擬人腦的行為可以帶給電腦相同的能力，使電腦得以協助處理辨認或自動化分類等工作。在類神經網路被廣泛運用在各領域之後，近年來更結合腦神經科學發展出了更加貼近生理機制的脈衝類神經網路，將訊息以脈衝序列形式傳遞，並且模擬神經元胞體膜電位的改變，一來提高網路運算和分類的能力，一來也和人腦運作方式更加接近，目前多被使用在生理機制的探討上，在工程上的應用並不是很多。 本研究提出以脈衝類神經網路為基底的波形分類架構，以分類心電圖為應用，探討編碼方式以及脈衝類神經網路中神經元的運作，並且修改現有Tempotron學習演算法而提高分類預測能力，在MIT-BIH資料庫以及實際的資料庫:台大醫院遠距照護中心資料測試上皆有不錯的表現。我們提出了脈衝類神經網路的新應用，並且證明了脈衝類神經網路的分類預測能力以及潛力。

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.