Atrial fibrillation classification and detection from ECG recordings

Objective: Atrial fibrillation (AF) heart rhythm disorder is investigated under two topics: Persistent AF (PeAF) and Paroxysmal AF (PAF). Diagnosis and detection of PeAF is relatively easier than PAF and PAF generally remains unrecognized. It is observed that a significant number of studies in the literature focused on detection of AF.Methods: In this study, four different approaches are examined for AF detection. The first one is based upon spectral features obtained from windowed ECG signals. In the second approach, distances between successor R peaks are used as features. Then in the third approach, P waves are detected from the ECG signals by using R peak positions and then the model is trained by those P waves. In those three approaches a deep learning ar-chitecture with bidirectional long short-term memory (BiLSTM) network is used. Finally, in the fourth approach, a convolutional long short-term memory (CLSTM) model with convolution and LSTM layers is used for classi-fication. The data set used in this work is obtained from 4th China Physiological Signal Challenge-2021.Results: As the result of experimental studies, it is seen that classification approach based on spectral features provided the best training accuracy (0.9788) and classification based on P wave detection provided the best test accuracy (0.8765).Significance: This study compares PeAF and PAF detection and classification methods based on deep learning models using different approaches. BiLSTM networks being capable of reflecting time sensitive features of ECG, appeared to be superior to CNN and LSTM cascades.

Automated summary

This study compares the detection and classification of persistent atrial fibrillation (PeAF) and paroxysmal atrial fibrillation (PAF) using deep learning models with different approaches. The results show that the classification approach based on spectral features achieves the highest training accuracy (0.9788), while the classification based on P wave detection achieves the highest test accuracy (0.8765). The bidirectional long short-term memory (BiLSTM) network outperforms the convolutional neural network (CNN) and LSTM cascades in capturing time-sensitive features of ECG signals.