Automatic online detection of atrial fibrillation based on symbolic dynamics and Shannon entropy

BioMedical Engineering OnLine

Xiaolin Zhou²  Hongxia Ding²  Benjamin Ung¹  Emma Pickwell-MacPherson¹  Yuanting Zhang¹ 

[1] Department of Electronic Engineering, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong

[2] The Key Laboratory for Health Informatics of the Chinese Academy of Sciences at Shenzhen Institutes of Advanced Technology, Shenzhen 518055, China

关键词: Shannon entropy;    Symbolic dynamics;    Integer filter;    Nonlinear filter;    Atrial fibrillation;    RR interval;    ECG;   

Others  :  797173
DOI  :  10.1186/1475-925X-13-18

 received in 2013-11-03, accepted in 2014-01-22,  发布年份 2014

【 摘 要 】

Background

Atrial fibrillation (AF) is the most common and debilitating abnormalities of the arrhythmias worldwide, with a major impact on morbidity and mortality. The detection of AF becomes crucial in preventing both acute and chronic cardiac rhythm disorders.

Objective

Our objective is to devise a method for real-time, automated detection of AF episodes in electrocardiograms (ECGs). This method utilizes RR intervals, and it involves several basic operations of nonlinear/linear integer filters, symbolic dynamics and the calculation of Shannon entropy. Using novel recursive algorithms, online analytical processing of this method can be achieved.

Results

Four publicly-accessible sets of clinical data (Long-Term AF, MIT-BIH AF, MIT-BIH Arrhythmia, and MIT-BIH Normal Sinus Rhythm Databases) were selected for investigation. The first database is used as a training set; in accordance with the receiver operating characteristic (ROC) curve, the best performance using this method was achieved at the discrimination threshold of 0.353: the sensitivity (Se), specificity (Sp), positive predictive value (PPV) and overall accuracy (ACC) were 96.72%, 95.07%, 96.61% and 96.05%, respectively. The other three databases are used as testing sets. Using the obtained threshold value (i.e., 0.353), for the second set, the obtained parameters were 96.89%, 98.25%, 97.62% and 97.67%, respectively; for the third database, these parameters were 97.33%, 90.78%, 55.29% and 91.46%, respectively; finally, for the fourth set, the Sp was 98.28%. The existing methods were also employed for comparison.

Conclusions

Overall, in contrast to the other available techniques, the test results indicate that the newly developed approach outperforms traditional methods using these databases under assessed various experimental situations, and suggest our technique could be of practical use for clinicians in the future.

【 授权许可】

   
2014 Zhou et al.; licensee BioMed Central Ltd.

【 图 表 】

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