[Identification of patients with atrial fibrillation using HRV parameters]. / Identifizieren von Patienten mit Vorhofflimmern anhand von HRV-Parametern.

Atrial fibrillation is the most common sustained cardiac rhythm disturbance. One of the most drastic complications is embolism, particularly stroke. Patients with atrial fibrillation have to be identified. This can lead to early therapy and thus avoiding strokes. The algorithm presented here detects atrial fibrillation securely and reliably. It is based on a single-channel ECG, which takes 60 min. First, the R-peaks are detected from the ECG and the RR interval is calculated. To be independent from pulse variations, the RR interval is normalized to 60 bpm. A parameter of heart rate variability is calculated in time domain (SDSD) and the so-called Poincaré plot is generated. The image analysis of the figures of the Poincaré plot is made automatically. The results from analysis in time domain, as well as image analysis, yield a risk level, which indicates the probability for the occurrence of atrial fibrillation. Even if there is no atrial fibrillation in the ECG while analyzing, it is possible to identify patients with atrial fibrillation. The sensitivity depends on the burden of atrial fibrillation. Even if a burden of 0% is assumed, the results still prove satisfactory (sensitivity of nearly 83%).

Algorithm for identifying patients with paroxysmal atrial fibrillation without appearance on the ECG.

Although atrial fibrillation is the most common sustained cardiac rhythm disturbance, it remains under-diagnosed. One of the most drastic complications is embolism, and strokes in particular. Patients having atrial fibrillation must be identified in order to reduce the number of strokes. The algorithm presented detects atrial fibrillation, even without it being indicated in the analyzed ECG. Based on parameters of heart rate variability, only a 60-minute single channel ECG is required. At first, all R peaks are detected and all RR intervals are calculated. After normalizing the RR intervals, the time domain parameter SDSD is calculated and the so-called Poincaré Plot is generated. The image and the time domain analysis assess a risk level, which determines whether the patient is suffering from atrial fibrillation. The resulting sensitivity calculated for ECG recordings from the MIT-BIH Atrial Fibrillation Database is 91.5% and the specificity determined for the ECG recordings from the MIT-BIH Normal Sinus Rhythm Database is 96.9%. The sensitivity depends on the atrial fibrillation burden. Even if a burden of 0 % is assumed, the results still prove satisfactory (sensitivity nearly 83%).