A Domestic Diagnosis System for Early Restless Legs Syndrome Based on Deep Learning.
10.3969/j.issn.1671-7104.2019.02.001
- Author:
Ping ZHOU
1
;
Luojie HUANG
1
;
Qingxian ZHAO
1
;
Wenjin XIAO
1
;
Siyu LI
1
Author Information
1. School of Biological Science & Medical Engineering, Southeast University, Nanjing, 210096.
- Publication Type:Journal Article
- Keywords:
acceleration sensors;
deep learning;
restless legs syndrome;
sleeping dynamic signal
- MeSH:
Deep Learning;
Humans;
Movement;
Polysomnography;
Restless Legs Syndrome;
diagnosis;
Sleep
- From:
Chinese Journal of Medical Instrumentation
2019;43(2):79-82
- CountryChina
- Language:Chinese
-
Abstract:
Restless legs syndrome,as a common sleep disorder,has nowadays long been diagnosed by self-rating scale and polysomnography.In this paper,a domestic diagnosis system for early restless legs syndrome based on deep learning is proposed,which is suitable for early patients with unstable symptoms in routine diagnosis.The hardware system is installed in the bed.And the non-contact sleeping dynamic signal acquisition is realized based on the acceleration sensors.The software system uses deep learning to classify and recognize the signals.A Fully Connected Feedforward Network based on Keras framework is constructed to recognize seven kinds of activities during sleeping.The accuracy of comprehensive classification is 97.83%.Based on former results,the periodic limb movement index and awakening index were evaluated to make the diagnosis of restless legs syndrome.
