Early Parkinson's Multimodal Detection System Based on Temporal Convolutional Networks
Parkinson's disease is one of the most common neurodegenerative diseases,its clinical features overlap with other neu-rodegenerative diseases and lack a clear pathological mechanism,leading to difficulties in early diagnosis and high misdiagnosis rates;In order to study effective early detection methods for Parkinson's disease,deeply explore the temporal characteristics of Parkinson's disease development,and improve the accuracy of early Parkinson's disease prediction,analysis,and diagnostic decision-making,an early Parkinson's disease multimodal detection system based on temporal convolutional networks is designed,providing an auxiliary diagnostic basis on the timely detection of early Parkinson's disease;The system utilizes the speech,gait,and subject self-test data,adopts the multiple linear pooling method for multimodal fusion,and integrates the time convolutional networks and parameter sharing to improve the detection accuracy of the system and reduce overfitting risks;Experimental results show that the accuracy of the early Parkinson's disease detection system based on temporal convolutional networks reaches 96.22％,which is superior to traditional Parkinson's detection models in multiple evaluation indicators and demonstrates good early Parkinson's joint detection performance.
NETL
NSTL
万方数据
