Principal Component Analysis Method for Detection and Classification of ECG Beat

Author

Yeh, Yun-Chi ; Chiang, Tung-Chien ; Lin, Hong-Jhih

Author_Institution

Dept. of Electron. Eng., Ching Yun Univ., Jhongli, Taiwan

fYear

2011

fDate

24-26 Oct. 2011

Firstpage

318

Lastpage

322

Abstract

This study proposes a simple and effective method, termed Principal Component Analysis (PCA) method, to analyze ECG signals for effectively determining the heartbeat case. This method is easily performed and does not require complex mathematic computations. The average time required for processing a 30-minute long of ECG data is less than 1 minute, and the required maximum memory is only about 10 MB. The ECG records available in the MIT-BIH arrhythmia database are utilized to illustrate the effectiveness of the proposed method. The experiment results show the total classification accuracy was approximately 90.85%.

Keywords

electrocardiography; medical disorders; medical signal processing; principal component analysis; signal classification; signal detection; ECG beat classification; ECG beat detection; ECG data; ECG recording; ECG signals; MIT-BIH arrhythmia database; PCA; complex mathematic computations; principal component analysis method; time 30 min; total classification accuracy; Algorithm design and analysis; Databases; Electrocardiography; Heart beat; Heart rate variability; Principal component analysis; Vectors; ECG signal; MIT-BIH database; Principal Component Analysis (PCA);

fLanguage

English

Publisher

ieee

Conference_Titel

Bioinformatics and Bioengineering (BIBE), 2011 IEEE 11th International Conference on

Conference_Location

Taichung

Print_ISBN

978-1-61284-975-1

Type

conf

DOI

10.1109/BIBE.2011.59

Filename

6089849