Neural Network Detection of Atrial Fibrillation by Lorenz Plot Images of Interbeat Interval Variation

Masaya Kisohara, Yuto Masuda, Emi Yuda, Junichiro Hayano

研究成果: Conference contribution

2 被引用数 (Scopus)

抄録

We developed artificial intelligence (AI) atrial fibrillation (AF) detection system using the big data of Allostatic State Mapping by Ambulatory electrocardiogram (ECG) Repository (ALLSTAR). Detection of atrial fibrillation (AF) is important for preventing acute cerebral infarction, but some waveforms that clinicians can easily diagnose existed that cannot be successfully detected by conventional programs. We let AI learn the detection of AF by convolutional neural network (CNN) using Lorenz plot of heart rate variability of 24-h ECG in subjects with sinus rhythm (SR) and AF whose diagnosis was confirmed as teacher data. Among 10000 datasets for SR and AF each, 80% of data was used as teacher data. With remaining 20% validation data, the CNN developed by AI detected AF with 100% sensitivity and 100% specificity.

本文言語English
ホスト出版物のタイトル2018 IEEE 7th Global Conference on Consumer Electronics, GCCE 2018
出版社Institute of Electrical and Electronics Engineers Inc.
ページ177-180
ページ数4
ISBN(電子版)9781538663097
DOI
出版ステータスPublished - 2018 12 12
外部発表はい
イベント7th IEEE Global Conference on Consumer Electronics, GCCE 2018 - Nara, Japan
継続期間: 2018 10 92018 10 12

出版物シリーズ

名前2018 IEEE 7th Global Conference on Consumer Electronics, GCCE 2018

Other

Other7th IEEE Global Conference on Consumer Electronics, GCCE 2018
国/地域Japan
CityNara
Period18/10/918/10/12

ASJC Scopus subject areas

  • コンピュータ ネットワークおよび通信
  • 電子工学および電気工学
  • 安全性、リスク、信頼性、品質管理
  • 器械工学

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