Atrial Fibrillation Episodes Detection Based on Classification of Heart Rate Derived Features

被引：0

作者：

Henzel, Norbert ^{[1
]}

Wrobel, Janusz ^{[1
]}

Horoba, Krzysztof ^{[1
]}

机构：

[1] Inst Med Technol & Equipment ITAM, Roosevelta 118, PL-41800 Zabrze, Poland

来源：

PROCEEDINGS OF THE 24TH INTERNATIONAL CONFERENCE MIXED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS - MIXDES 2017 | 2017年

关键词：

Atrial fibrillation; Machine learning; Classification; GLM; AUTOMATIC DETECTION; RATE-VARIABILITY; FETAL; ACCURACY; RR; PHOTOPLETHYSMOGRAPHY; REDUCTION; SYSTEM; RHYTHM;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Atrial fibrillation (AF) is one of the most common cardiac arrhythmia and effects nearly 1-2 of every 100 persons of the population. This paper evaluates the effectiveness of Machine Learning (ML) approach to detect AF episodes. Features, determined exclusively on the basis of beat intervals, are classified with linear classifier. Performances of the proposed approach are evaluated by means of the MIT-BIH Atrial Fibrillation Database.

引用

页码：571 / 576

页数：6

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