Automated Arrhythmia Detection Based on RR Intervals

Automated Arrhythmia Detection Based on RR Intervals

Blog Article

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 BLACK RADISH 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%, Engine Pulleys 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.