Atrial fibrillation classification and detection from ECG recordings. (April 2023)

• Record Type: Journal Article
• Authors: Fatih Gündüz, Ali; Fatih Talu, Muhammed
• Is Part Of: Biomedical signal processing and control. Volume 82(2023)
• Publication Date: 2023-04
• Subjects: Atrial fibrillation -- ECG -- Signal processing -- Classification -- Deep learning -- CNN -- LSTM; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2022.104531
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: Atrial fibrillation detection and its type classification have been studied. BiLSTM, cascade of CNN, and LSTM networks have been used and their accuracies have been compared. Spectral features, P waves and R-R distances in ECG signals have been used as network inputs. Determination of the class of "Paroxysmal atrial fibrillation" has been done by a simple decision mechanism based on normal/abnormal frame predictions in an ECG record. Abstract: 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 architecture 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 classification. 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.