Key result
Hybrid CNN-LSTM model detects AF on Holter ECGs with ~95% accuracy despite regular R-R intervals.
Why the study?
Automatic AF detection accuracy needs improvement for large Holter ECG datasets, particularly for rare cases with regular R-R intervals that are difficult to identify.
Does a hybrid CNN-LSTM model with P/f-wave identification improve the accuracy of atrial fibrillation detection from Holter ECGs?
Does a hybrid CNN-LSTM model with P/f-wave identification improve the accuracy of atrial fibrillation detection from Holter ECGs?
Answers are grounded in this paper and cite their sources.
A novel two-stage deep learning model combining CNN-LSTM and P/f-wave identification demonstrates high accuracy and sensitivity for detecting atrial fibrillation, including challenging cases with regular R-R intervals, from Holter ECGs.
Atrial fibrillation (AF) is the most common arrhythmia that increases the risk of cardiac diseases such as stroke; hence, its early detection is important. Although many studies have been conducted on automatic AF detection, the accuracy of identification needs to be improved for large amounts of data such as Holter electrocardiograms (ECGs). In rare clinical cases, AF shows regular R‒R intervals (RRIs). These cases are difficult to diagnose even by experienced cardiologists or to detect automatically. To detect AF accurately with minimum number of oversights, identification of AF including regular RRIs is necessary. This paper presents a new method for improving the accuracy of AF detection. The detection is realized in two stages with different AF identification models, after eliminating noise in the ECG waveforms using a finite impulse response bandpass filter. In the first stage, a hybrid convolutional neural network (CNN)‒long short-term memory model trained with segmented ECG waveforms and their RRIs is used to identify AF with irregular RRIs. In the second stage, non-AF events are input. AF cases with regular RRIs are identified using an independent CNN model trained with waveforms that include P- or fibrillatory waves. In this study, 24-hour Holter ECG data obtained from 60 subjects were used to train both models. For evaluation, Holter ECG data from ten subjects not used for training were used, which yielded a detection accuracy of 94.7% and a sensitivity of 98.5% for AF. In addition, evaluation using another untrained dataset from two subjects whose AF had regular RRIs showed an accuracy of 96.2% and a sensitivity of 98.8% for AF. These results demonstrate the feasibility of the proposed method for AF detection.
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Kamozawa et al. (2025) studied Atrial Fibrillation (n=254). Hybrid CNN-LSTM and 1D CNN model was evaluated on Atrial fibrillation detection accuracy. A two-stage hybrid CNN-LSTM and 1D CNN model detected atrial fibrillation from 24-hour Holter ECGs with 94.7% accuracy and 98.5% sensitivity, and successfully identified cases with regular R-R intervals.
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