心房顫動與心室上心律不整之偵測

Detection of Atrial Fibrillation and Supraventricular arrhythmia

林竹萱(Chu-Hsuan LIN)

指導教授 ： 趙福杉

共同指導教授 ： 廖文劍

國立臺灣大學/工學院/醫學工程學研究所/碩士(2014年)

https://doi.org/10.6342/NTU.2014.01103

摘要

心房顫動為最常見的心律不整疾病之一，並存在著許多風險如中風或全身性血管栓塞，但心房顫動患者通常不會有明顯的症狀，容易被忽略而嚴重影響日後生活，因此如何早期準確檢測出心房顫動為一項重要課題。
本研究以三個心房顫動主要特徵(1)心臟跳動不規則(2)P波不明顯(3)心房活動關係為基礎，提出了一套特徵擷取及特徵選取方式，為避免心房顫動與其他心室上心律不整疾病混淆，使用了三種不同的資料庫進行比較分析，配合Support Vector Machine分類器以及交叉驗證方式，心房顫動及正常心電圖辨識率達95.67%，而心室上心律不整與正常心電圖辨識率則為96.67%。

關鍵字

心房顫動 ； 心室上心律不整 ； 支持向量機 ； 希爾伯特-黃 轉換 ； 非線性特徵

並列摘要

Atrial fibrillation (AF) is a common arrhythmia that can lead several risks to people who suffer from the illness, such as stroke or heart failure. However, the patients do not have obvious symptom, making it easy to ignore and seriously affect the future of life. Therefore, early and truly detection of AF becomes an important issue.
In this study, we raise a feature extraction and feature selection method base on three main physiological characteristics of AF: (1) heart rate irregular (2) P wave unobvious, and (3) atrial activity relationship. In order to avoid AF and other supraventricular arrhythmia be confused, we use three different database for comparative analysis. Finally, the SVM classifier and cross validation method were used to discriminate between AF and Normal ECG with a 95.67% accuracy, and supraventricular arrhythmia, and Normal ECG with 96.67% accuracy by considering only three features. For the discrimination of three categories, a recognition rate of 92.23% was achieved.

並列關鍵字

Atrial fibrillation ； supraventricular arrhythmia ； support vector machine ； Hilbert-Huang transform ； non-linear feature.

參考文獻

[3] Camm, A. John, et al. "Guidelines for the management of atrial fibrillation The Task Force for the Management of Atrial Fibrillation of the European Society of Cardiology (ESC)." European heart journal 31.19 (2010): 2369-2429.

[5] Lee, Jinseok, et al. "Atrial fibrillation detection using an iphone 4s." (2013): 1-1.

[6] Tateno, K., and L. Glass. "A method for detection of atrial fibrillation using RR intervals." Computers in Cardiology 2000. IEEE, 2000.

[7] Thong, Tran, et al. "Prediction of paroxysmal atrial fibrillation by analysis of atrial premature complexes." Biomedical Engineering, IEEE Transactions on 51.4 (2004): 561-569.

[8] Rieta, J. J., et al. "Atrial fibrillation, atrial flutter and normal sinus rhythm discrimination by means of blind source separation and spectral parameters extraction." Computers in Cardiology, 2002. IEEE, 2002.