Abstract

Info

Author(s):: HOU Xiaoli1; ZHAO Ya2; YAN Huishen1; CHENG Hong2; 1. Medical School, Yangzhou Polytechnic College, Yangzhou 225000, China 2. Medical School, Yangzhou University, Yangzhou 225000, China

Keywords:: Keywords: Parkinsons disease?ong short-term memory network?esidual network?arkinsons disease vowel set

Abstract:: Abstract: Parkinsons disease (PD) diagnosis method based on speech feature classification has the characteristics of non-invasive, efficient, accurate, remote and low-cost. A PD diagnosis method based on deep long short-term memory (LSTM) residual network is proposed.?he characteristics of speech signal in PD patients and deep LSTM residual model are analyzed, and the PD diagnosis model based on deep LSTM residual network is divided into 3 parts, namely speech signal pre-processing network, deep LSTM residual network for speech diagnosis and GAP-ELM network for PD classification. The proposed model can realize the extraction of deep features of PD speech signal, and obtain the time-changing state of PD speech signal through the forgetting gate and memory gate of LSTM structure. PD diagnostic test is completed using PD vowel set. The experimental results demonstrate the proposed method has a higher recognition accuracy for PD in various signal-to-noise ratio environments, and it can complete the training in fewer epochs, and achieve better stability and smaller loss value.?/html>