A Real-Time Atrial Fibrillation Detection Algorithm Based on the Instantaneous State of Heart Rate

• Published in: PloS one 2015-09, Vol.10 (9), p.e0136544-e0136544
• Main Authors: Zhou, Xiaolin, Ding, Hongxia, Wu, Wanqing, Zhang, Yuanting
• Format: Article
• Language: English
• Subjects: Algorithms; Arrhythmia; Atrial fibrillation; Atrial Fibrillation - diagnosis; Atrial Fibrillation - physiopathology; Biosensors; Blood coagulation; Cardiac arrhythmia; Clinical decision making; Clotting; Computer applications; Data bases; Data processing; Decision making; Diagnosis; Diagnosis, Computer-Assisted - methods; Electrocardiography; Entropy; Entropy (Information theory); Fibrillation; Health aspects; Health informatics; Heart Rate; Laboratories; Machine Learning; Methods; Mortality; Parameters; Predictive Value of Tests; Probability distribution; Real time; ROC Curve; Stroke; Studies
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0136544

Atrial fibrillation (AF), the most frequent cause of cardioembolic stroke, is increasing in prevalence as the population ages, and presents with a broad spectrum of symptoms and severity. The early identification of AF is an essential part for preventing the possibility of blood clotting and stroke. In this work, a real-time algorithm is proposed for accurately screening AF episodes in electrocardiograms. This method adopts heart rate sequence, and it involves the application of symbolic dynamics and Shannon entropy. Using novel recursive algorithms, a low-computational complexity can be obtained. Four publicly-accessible sets of clinical data (Long-Term AF, MIT-BIH AF, MIT-BIH Arrhythmia, and MIT-BIH Normal Sinus Rhythm Databases) were used for assessment. The first database was selected as a training set; the receiver operating characteristic (ROC) curve was performed, and the best performance was achieved at the threshold of 0.639: the sensitivity (Se), specificity (Sp), positive predictive value (PPV) and overall accuracy (ACC) were 96.14%, 95.73%, 97.03% and 95.97%, respectively. The other three databases were used for independent testing. Using the obtained decision-making threshold (i.e., 0.639), for the second set, the obtained parameters were 97.37%, 98.44%, 97.89% and 97.99%, respectively; for the third database, these parameters were 97.83%, 87.41%, 47.67% and 88.51%, respectively; the Sp was 99.68% for the fourth set. The latest methods were also employed for comparison. Collectively, results presented in this study indicate that the combination of symbolic dynamics and Shannon entropy yields a potent AF detector, and suggest this method could be of practical use in both clinical and out-of-clinical settings.