Sleep Disorder Classification Method based on Logistic Regression with Apnea-ECG Dataset - Details

author：

Indexed by：

Scopus

Abstract：

Adequate　sleep　is　significant　for　human　to　actively　pursue　daily　activity.　On　the　other　hand,　insomnia　is　directly　proportional　to　aging　and　health　deterioration.　Sleep　disorder　classification　is　important　for　medical　scientists　as　well　as　machine　learning　researchers.　In　the　paper,　we　have　developed　a　sleep　disorder　classification　method　for　Electrocardiogram　(ECG)　data.　The　data　set　have　two　labels;　sleep　disorder　or　not,　which　can　be　categorized　as　binary　output.　As　a　result,　logistic　regression　is　used　as　classification　technique.　For　initial　experimentation,　an　existing　data　set　is　used.　The　data　set　consists　of　every　10　second　data　up　to　6000　seconds　for　35　patients.　We　have　used　70%　data　for　training,　and　30%　data　for　testing　purpose.　Our　results　from　logistic　regression　show　that　the　logistic　regression　method　is　efficient　to　detect　sleep　disorders.　The　prediction　result　is　found　to　be　59%.　The　future　research　in　this　direction　would　be　to　take　data　from　hospitals　and　use　our　developed　algorithm　for　sleep　disorder　classification　and　prediction.　©　2019　Association　for　Computing　Machinery.

Keyword：

ECG; Logistic regression; Sleep disorder

Community：

[ 1 ] [Cai, L.]College of Physics and Information Engineering, Fuzhou University, Fuzhou, China
[ 2 ] [Cai, L.]Zhicheng College, University of South Alabama, United States
[ 3 ] [Cai, L.]Fuzhou University, Fuzhou, China
[ 4 ] [Cai, L.]University of South Alabama, United States
[ 5 ] [Chen, C.]HaiXi Institutes, Chinese Academy of Science, Fuzhou, China
[ 6 ] [Chen, C.]Zhicheng College Fuzhou University, Fuzhou, China
[ 7 ] [Chen, C.]Fuzhou University, Fuzhou, China
[ 8 ] [Wang, X.]Zhicheng College Fuzhou University, Fuzhou, China
[ 9 ] [Wang, X.]Fuzhou University, Fuzhou, China
[ 10 ] [Yang, X.]Zhicheng College Fuzhou University, Fuzhou, China
[ 11 ] [Yang, X.]Fuzhou University, Fuzhou, China
[ 12 ] [Lin, S.]Zhicheng College Fuzhou University, Fuzhou, China
[ 13 ] [Lin, S.]Fuzhou University, Fuzhou, China
[ 14 ] [Huang, J.]Department of Respiratory Medicine, First Affiliated Hospital of Fujian Medical University, Fuzhou, China
[ 15 ] [Jiang, J.]College of Electrical Engineering and Automation Fuzhou University, Fuzhou, China
[ 16 ] [Jiang, J.]Fuzhou University, Fuzhou, China
[ 17 ] [Datta, R.]School of Computing, University of South Alabama, Mobile, United States
[ 18 ] [Du, M.]College of Electrical Engineering and Automation Fuzhou University, Fuzhou, China
[ 19 ] [Du, M.]Provincial Key Laboratory of Eco-industrial Green Technology, Wuyi College, Wuyishan, China
[ 20 ] [Du, M.]Fuzhou University, Fuzhou, China
[ 21 ] [Jiang, H.]College of Electrical Engineering and Automation Fuzhou University, Fuzhou, China
[ 22 ] [Jiang, H.]Fuzhou University, Fuzhou, China
[ 23 ] [Zhu, M.]Zhicheng College, University of South Alabama, United States
[ 24 ] [Zhu, M.]College of Mathematics and Computer Science, Fuzhou University, Fuzhou, China
[ 25 ] [Zhu, M.]Fuzhou University, Fuzhou, China
[ 26 ] [Zhu, M.]University of South Alabama, United States
[ 27 ] [Huang, J.]Fuzhou University, Fuzhou, China
[ 28 ] [Huang, J.]University of South Alabama, United States

Reprint 's Address：

[Du, M.]College of Electrical Engineering and Automation Fuzhou UniversityChina

Email：

dumin@fzu.edu.cn

Show more details

Related Keywords：

Source ：

ACM International Conference Proceeding Series

Year： 2019

Language： English

Cited Count：

WoS CC Cited Count：

SCOPUS Cited Count：

ESI Highly Cited Papers on the List： 0 Unfold All

WanFang Cited Count：

Chinese Cited Count：

30 Days PV： 1

Affiliated Colleges：

Get Fulltext

DOI Library Discovery Baidu Scholar Search SCOPUS

Type
Departments

All Years Choose Year From to