Automated classification of five arrhythmias and normal sinus rhythm based on RR interval signals. (1st November 2021)

• Record Type: Journal Article
• Authors: Faust, Oliver; Acharya, U. Rajendra
• Is Part Of: Expert systems with applications. Volume 181(2021)
• Publication Date: 2021-11-01
• Subjects: Computer aided diagnosis -- Arrhythmia detection -- Deep learning -- Residual Neural Network -- M-health; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2021.115031
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: Automated arrhythmia detection with high accuracy. Residual Neural Network for time series data classification. Six class problem: five arrhythmias and normal. RR intervals: Convenient data acquisition and cost efficient data handling. Algorithmic foundation for smart m-health applications. Abstract: Arrhythmias are abnormal heart rhythms that can be life-threatening. Atrial Fibrillation (AFIB), Atrial Flutter (AFL), Supraventricular Tachycardia (SVT), Sinus Tachycardia (ST), and Sinus Bradycardia (SB) are common arrhythmias that affect a growing number of patients. In this paper we describe a method to detect these arrhythmias in RR interval signals. We propose a deep learning algorithm to discriminate these fife arrhythmias and Normal Sinus Rhythm (NSR). The deep learning model was trained and tested with data from 10093 subjects. We used 10-fold cross-validation to establish the performance results. The overall accuracy for the six-class problem was 98.37%. When considering the binary problem of arrhythmia versus NSR, where the arrhythmia group is formed by combining the data from all fife arrythmias, the performance results are: Accuracy (ACC) = 98.55%, Sensitivity (SEN) = 99.40%, Specificity (SPE) = 94.30%. These results indicate that it is possible to discriminate RR interval sequences from SVT, ST, SB, AFIB, AFL, and NSR subjects with minimal error. Furthermore, the proposed model can provide a robust and independent second opinion when it comes to a decision if arrhythmia is present or not. Another positive aspect of the proposed arrhythmia detection algorithm is economic viability. RR interval signals are cost-effective to measure, communicate, and process. The discriminate powers of the proposed algorithm together with the advent of wearable technology and m-health infrastructure might lead to pervasive long-term arrhythmia monitoring. The detection results can support early diagnosis which helps to reduce the burden of the disease.