The Minirocket algorithm achieved an accuracy of 0.9803 for detecting atrial fibrillation from 4-second intracardiac electrogram signals, outperforming conventional machine learning algorithms.
Does the Minirocket algorithm improve the accuracy and computational efficiency of atrial fibrillation detection from intracardiac electrograms compared to conventional machine learning algorithms?
The Minirocket algorithm provides highly accurate and computationally efficient detection of atrial fibrillation from intracardiac electrograms, even with short signal durations, making it a promising tool for real-time monitoring devices.
Absolute Event Rate: 0.9803% vs 0.9232%
Atrial Fibrillation (AF) detection from intracardiac Electrogram (EGM) signals is a critical aspect of cardiovascular health monitoring. This study explores the application of Minirocket, a time series classification (TSC) algorithm, for robust and efficient AF detection. A comparative analysis is conducted against a deep learning approach using a subset of the dataset from Rodrigo et al. (2022). The study investigates the robustness of Minirocket in the face of shorter EGM sequences and varying training sizes, essential for real-world applications such as wearable and implanted devices. Empirical runtime analysis further assesses the efficiency of Minirocket in comparison to conventional machine learning (ML) algorithms. The results showcase Minirocket's notable performance, especially in scenarios with shorter signals and varying training sizes, making it a promising candidate for streamlined AF detection in emerging cardiovascular monitoring technologies. This research contributes to the optimization of AF detection algorithms for increased efficiency and adaptability to dynamic clinical scenarios.
Celal Alagöz (Tue,) conducted a other in Atrial Fibrillation and Atrial Tachycardia (n=44). Minirocket algorithm vs. Conventional machine learning algorithms (e.g., XGBoost, Random Forest) was evaluated on Accuracy of atrial fibrillation detection from 4-second intracardiac electrogram signals. The Minirocket algorithm achieved an accuracy of 0.9803 for detecting atrial fibrillation from 4-second intracardiac electrogram signals, outperforming conventional machine learning algorithms.