Atrial fibrillation classification using deep learning algorithm in Internet of Things–based smart healthcare system.
Detecting the electrocardiogram pattern in Internet of Things–based healthcare system and notifying this to the user is a challenging task. Using advance computing methods for classification of electrocardiogram signal is a notable research topic. In this research work, an intelligent electrocardiog...
| Published in: | Health Informatics Journal Vol. 26; no. 3; pp. 1827 - 1841 |
|---|---|
| Main Authors: | , |
| Format: | algorithm equations & formulas pictorial research tables/charts tracings Journal Article |
| Published: |
Sage Publications Inc.
Sep2020
|
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=144846456&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 144846456 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14604582 EJK jtl: Health Informatics Journal issn: 14604582 maglogo: Y pubinfo: dt: Sep2020 vid: 26 iid: 3 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 144846456 144846456 144846456 10.1177/1460458219891384 144846456 ppf: 1827 ppct: 14 formats: tig: atl: Atrial fibrillation classification using deep learning algorithm in Internet of Things–based smart healthcare system. aug: au: Rajan Jeyaraj, Pandia Nadar, Edward Rajan Samuel affil: Mepco Schlenk Engineering College, India sug: subj: Deep Learning Algorithms Internet of Things Atrial Fibrillation Classification Medical Informatics Signal Processing, Computer Assisted Electrocardiography Neural Networks (Computer) Comparative Studies Sensitivity and Specificity Computer Hardware Human ab: Detecting the electrocardiogram pattern in Internet of Things–based healthcare system and notifying this to the user is a challenging task. Using advance computing methods for classification of electrocardiogram signal is a notable research topic. In this research work, an intelligent electrocardiogram signal classification, employing deep learning algorithm, developed and tested in Internet of Things–based smart healthcare system was proposed. For classification of acquired electrocardiogram signal, a partitioned deep convolutional neural network was proposed. The electrocardiogram feature continuously in the Internet of Things–based monitoring system was learnt. To make use of learned features in the continuous time series data, it forms a higher order space in the server. We have made quantifiable comparative analysis with other classification algorithm with the same time series data collected from different atrial fibrillation samples in the Internet of Things–based e-health system. Our proposed algorithm learned features were tested in atrial fibrillation classified signal with other conventional classifiers with various performance indices. We obtained an accuracy of 96.3 percent with 93.5-percent sensitivity and 97.5-percent precision. From the obtained result, processing with proposed deep convolutional neural network provides reliable timely assist and accurate classification of electrocardiogram signal in Internet of Things–based smart healthcare system. pubtype: Academic Journal doctype: algorithm equations & formulas pictorial research tables/charts tracings Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|