HRX 2024 AbstracX Library-Analysis of a Single Heartbeat with DL for Prediction of AF in Cryptogenic Stroke

Analysis of a Single Heartbeat with DL for Prediction of AF in Cryptogenic Stroke

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The document discusses the application of deep learning (DL) for predicting atrial fibrillation (AF) in cases of cryptogenic stroke (CS) using a single heartbeat analysis. Cryptogenic strokes account for 20-40% of ischemic cerebrovascular incidents, and 20-30% of these patients may have undetected paroxysmal atrial fibrillation, which typically requires detailed cardiac monitoring for diagnosis. Current monitoring methods, like implantable loop recorders (ILRs), are costly and involve ongoing fees, highlighting the need for more efficient prediction models to aid in clinical decision-making.<br /><br />The analysis presented achieves an accuracy rate of 78% and an F1 score of 0.54, with 80% correct classification at the patient level. Key insights from the study include the potential to augment small ECG datasets without generating synthetic data and the importance of focusing on the biological signal to improve the signal-to-noise ratio. This can be accomplished through intelligent sampling methods, which help reduce noise and artifacts in ECG data, ultimately enhancing model performance.<br /><br />The insights and techniques discussed aim to provide more effective point-of-care decision-making methods that are less dependent on expensive and labor-intensive monitoring systems. The study, led by Dr. Ravi Shah, emphasizes the potential for deep learning models to substantially improve the management of cryptogenic stroke by enabling early detection of AF, thus potentially preventing further complications.

Keywords

deep learning

atrial fibrillation

cryptogenic stroke

single heartbeat analysis

ECG datasets

signal-to-noise ratio

intelligent sampling

point-of-care decision-making

Dr. Ravi Shah

early detection