Key result
An automatic detection algorithm using Discrete Wavelet Transform and Support Vector Machine classified Atrial Fibrillation and Normal Sinus Rhythm from 12-hour RR interval sequences with 99.60% accuracy.
Why the study?
Increasing heart disease causes diagnostic difficulties, motivating the development of engineering algorithms to assist physicians and speed up detection of AF.
Does an algorithm based on RR interval sequences, Discrete Wavelet Transform, and Support Vector Machine accurately detect atrial fibrillation from ECG signals?
Does an algorithm based on RR interval sequences, Discrete Wavelet Transform, and Support Vector Machine accurately detect atrial fibrillation from ECG signals?
An algorithm utilizing RR interval sequences, Discrete Wavelet Transform, and Support Vector Machine can automatically detect atrial fibrillation with 99.60% accuracy, potentially aiding in rapid and automated diagnosis.
Promising for automated AF detection; leaves open prospective validation before clinical adoption.
Heart diseases are rapidly increasing worldwide and in our country. This increase causes difficulties in the diagnosis processes of heart diseases. Considering these problems, the studies of engineering applications related to medical science give effective results in terms of solutions. By means of engineering devices and algorithms, positive contributions are made to medical applications. These contributions assist physicians especially in the diagnosis stages and speed up these processes. In this study, a new algorithm is developped so that Atrial Fibrillation (AF), which is the most common type of arrhythmia encountered, can be automatically detected at a high success rate. Electrocardiogram (ECG) data used in this study were obtained from physiobank ATM database. 31 samples of Atrial Fibrillation Rhythm (AFR) and 31 samples of Normal Sinus Rhythm (NSR) were obtained from this database. RR Interval (RRI) sequences being 12 hours long are used in the study. The change of the RRI sequences is an important parameter for AF. The RRI sequences are re-sampled using signal pre-processing techniques. The Discrete Wavelet Transform (DWT) was then applied to the resampled signals. In this way, feature extraction process is performed and the wavelet energies of these signals are visually examined with boxplot. The wavelet energies of the RRI sequences are classified by the Support Vector Machine (SVM). Finally, AFR and NSR are successfully separated as 99.60% achievement.
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Güzeler et al. (2019) studied Atrial Fibrillation (n=62). Discrete Wavelet Transform and Support Vector Machine algorithm vs. Database annotations was evaluated on Classification accuracy of Atrial Fibrillation vs Normal Sinus Rhythm. An automatic detection algorithm using Discrete Wavelet Transform and Support Vector Machine classified Atrial Fibrillation and Normal Sinus Rhythm from 12-hour RR interval sequences with 99.60% accuracy.
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