Key result
A deep Recurrent Neural Network with Bi-directional Long Short-Term Memory demonstrated better performance than other AI algorithms for classifying atrial fibrillation episodes.
Why the study?
Paroxysmal AFib episodes challenge manual diagnosis, but real-time monitoring with wearable health trackers could improve detection of this unpredictable event.
Does a deep RNN with Bi-directional LSTM improve the real-time detection of AFib episodes using instantaneous heart rate compared to other AI algorithms?
Does a deep RNN with Bi-directional LSTM improve the real-time detection of AFib episodes using instantaneous heart rate compared to other AI algorithms?
A deep RNN with Bi-directional LSTM algorithm provides superior performance for real-time detection of atrial fibrillation using instantaneous heart rate, with potential applications in wearable health trackers.
Answers are grounded in this paper and cite their sources.
May support real-time AF detection in wearables; leaves open prospective clinical validation.
Atrial Fibrillation (AFib) is an abnormal heart rhythm (arrhythmia) condition that may cause a fatal cardioembolic stroke. The episode of AFib can be paroxysmal which increases challenges for its clinical manual diagnosis and affects the quality of life. Real-time cardiac monitoring with wearable health trackers can improve the chances of detecting this unpredictable event. In this paper, various Artificial Intelligence (AI) algorithms have been developed to classify beat-to-beat variation of AFib episodes in real-time using Instantaneous Heart Rates (IHR). Publicly-available clinical datasets from Physionet.org have been used for training and testing the AI algorithms. The accuracy, sensitivity, specificity, precision, F1 score, recall, and area under the receiver operating characteristic curve of these algorithms are evaluated and compared. It was found that, in comparison to other AI algorithms, the deep Recurrent Neural Network (RNN) with Bi-directional Long Short-Term Memory (LSTM) demonstrates better performance for classifying the AFib episodes. The models developed can be integrated into wireless health tracker-based mHealth applications to detect AFib using IHR in real-time.
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Panindre et al. (2020) studied Atrial Fibrillation. Deep Recurrent Neural Network (RNN) with Bi-directional Long Short-Term Memory (LSTM) vs. Other AI algorithms was evaluated on Accuracy, sensitivity, specificity, precision, F1 score, recall, and area under the receiver operating characteristic curve for classifying AFib episodes. A deep Recurrent Neural Network with Bi-directional Long Short-Term Memory demonstrated better performance than other AI algorithms for classifying atrial fibrillation episodes.
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