Automated Arrhythmia Detection Based on RR Intervals

• Published in: Diagnostics (Basel) Vol. 11; no. 8; p. 1446
• Main Authors: Faust, Oliver, Kareem, Murtadha, Ali, Ali, Ciaccio, Edward J., Acharya, U. Rajendra
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 10.08.2021; MDPI
• Subjects: Algorithms; arrhythmia detection; atrial fibrillation; atrial flutter; Automation; Cardiac arrhythmia; Datasets; Deep learning; Electrocardiography; Heart; heart rate; Patients; RR interval; Signal processing
• ISSN: 2075-4418; 2075-4418
• DOI: 10.3390/diagnostics11081446

Abnormal heart rhythms, also known as arrhythmias, can be life-threatening. AFIB and AFL are examples of arrhythmia that affect a growing number of patients. This paper describes a method that can support clinicians during arrhythmia diagnosis. We propose a deep learning algorithm to discriminate AFIB, AFL, and NSR RR interval signals. The algorithm was designed with data from 4051 subjects. With 10-fold cross-validation, the algorithm achieved the following results: ACC = 99.98%, SEN = 100.00%, and SPE = 99.94%. These results are significant because they show that it is possible to automate arrhythmia detection in RR interval signals. Such a detection method makes economic sense because RR interval signals are cost-effective to measure, communicate, and process. Having such a cost-effective solution might lead to widespread long-term monitoring, which can help detecting arrhythmia earlier. Detection can lead to treatment, which improves outcomes for patients.