Deep neural networks improve atrial fibrillation detection accuracy
A New AFIB Detection Method with Deep Neural Networks
Machine Learning
Summary
Atrial fibrillation is a heart condition where the heartbeat becomes irregular, which can be dangerous if not detected early. The authors developed a new way to find this irregular heartbeat by using deep neural networks that learn from a special type of heart rate data, making detection more precise. They tested their method on well-known heart signal datasets and found it works better than previous methods. This could help doctors spot atrial fibrillation more reliably.
What this means in practice
- •For medical device engineers: Build improved heart monitoring devices that use the new deep learning features to detect atrial fibrillation more accurately.$Commercial implications: Enables more accurate AFIB detection in wearable and clinical monitors, creating better diagnostic products for healthcare providers.
- •For clinical data analysts: Integrate this detection method into hospital data systems to enhance screening and diagnosis of atrial fibrillation from ECG data.
Authors
Sara Gjorgjieva, Ana Angjelevska, Dimitar Trajanov, Marjan Gusev
Abstract
Early detection of atrial fibrillation (AFIB), characterized by irregular heart rhythms, can potentially save thousands of lives annually [20]. Numerous studies have attempted to address this challenge using various algorithms and methodologies. This paper introduces a novel and robust method that enhances the precision of AFIB detection. Neural Networks (NN) are widely recognized for their ability to identify complex electrocardiogram (ECG) patterns, facilitating the accurate detection of irregular heart rhythms. This study explores multiple conventional machine learning algorithms alongside neural network approaches to develop a robust AFIB detection system. To evaluate the effective- ness and robustness of the proposed method, the MIT BIH arrhythmia database (MITDB) and the Long-term Atrial Fibrillation Database (LTAFDB), both available on the Physionet site. Our approach presents a novel feature engineering technique, employing specific features such as a time series of the differences in successive heart rates (dBPM) rather than traditional inter-beat intervals. The model analyzes inputs labeled into five distinct classes, using only clean intervals identified through a new labeling method for training while treating others as outliers. This strategy ensures a more accurate and reliable AFIB detection process. The best-performing model, a Deep Neural Network (DNN), achieved an F1 score of 95.18% for binary atrial fibrillation classification on a benchmark dataset distinct from the training set. Models trained using our approach have demonstrated superior performance than previously published models.