Diagnostic decision support systems for atrial fibrillation based on a novel electrocardiogram approach

The electrocardiogram (ECG) is one of the most non-invasive techniques to give support to the atrial fibrillation (AF) diagnosis. Several authors use the temporal difference between two consecutive R waves, a method known as RR interval, to perform the AF diagnosis. However, RR interval-based analysis does not detect distortions on the other ECG waves.

Thus, the present work proposes a diagnostic decision support systems for AF based on higher order spectrum analysis of the voltage variation on the ECG..

The proposed method was used aiming AF classifying. The classifier is composed by two screening stages: one based on the average and another on the average deviation of kurtosis of the ECG signals. Heartbeat obtained from the MIT-BIH atrial fibrillation and MIT-BIH normal were used.

ECG signal featured by kurtosis outperforms second order statistics based metrics in up to 476 times, and up to 110 times above the RR interval. The screening methods obtained sensitivity equal to 100% and specificity is up to 84.04%. The two screening methods combined provided an AF classifier with an accuracy rate at diagnosis of 100%. The results presented take into account windows of up to five heartbeats and a 99.73% confidence interval.

The results obtained by the proposed method can be used to support decision-making in clinical practices with a diagnostic accuracy rate of 90.04% to 100%.