Key result
ID-CNN with BILSTM achieves 81% accuracy for AFib detection, outperforming MobileNetV2 with BILSTM.
Why the study?
Traditional methods for AF detection are time consuming and can be prone to human error.
Do hybrid neural network models accurately detect atrial fibrillation?
Do hybrid neural network models accurately detect atrial fibrillation?
Absolute Event Rate: 81% vs 75%
A 1D-CNN with BILSTM neural network model achieved 81% accuracy in detecting atrial fibrillation, outperforming a MobileNetV2 with BILSTM model.
Hybrid models may aid AF detection research; leaves open clinical utility pending prospective validation.
Atrial fibrillation (AF) is the most common cardiac arrhythmia affecting about 50,000 new people each year in Latin America. AF is characterized by irregular and rapid heartbeats that can lead to serious complications, such as stroke, heart failure, and all-cause mortality. Traditional methods for AF detection are time consuming and can be prone to human error. Therefore, this work reports the results from two methods using machine learning techniques to assist the diagnosis of AF through 2 hybrid models of neural networks: The ID- CNN with BILSTM model and the MobileNetV2 with BILSTM model which reached 81 and 75% accuracy respectively.
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Aguilar et al. (2023) studied Atrial fibrillation. ID-CNN with BILSTM model vs. MobileNetV2 with BILSTM model was evaluated on Accuracy. The ID-CNN with BILSTM model achieved 81% accuracy for atrial fibrillation detection, compared to 75% accuracy with the MobileNetV2 with BILSTM model.
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