Key result
Ensemble learning algorithm outperforms state-of-the-art methods for detecting AF from single-lead wearable ECGs.
Why the study?
Continuous real-time physiological monitoring via portable devices is expanding, making automatic AF detection from wearable single-lead ECG recordings an important focus.
Does an ensemble learning algorithm improve atrial fibrillation detection from single lead ECG recordings compared to state-of-the-art counterparts?
Does an ensemble learning algorithm improve atrial fibrillation detection from single lead ECG recordings compared to state-of-the-art counterparts?
Answers are grounded in this paper and cite their sources.
An ensemble learning algorithm combining multiple ECG data representations with Bayesian optimization improves automated atrial fibrillation detection from wearable devices.
With the popularity of wireless techniques and portable devices, continuous and real-time monitoring patient’s health status by collecting physiological signals is becoming more popular. Data mining of electrocardiograph (ECG) attracts widespread attention, where automatic atrial fibrillation (AF) detection has become a hot research field. In this paper, an ensemble learning algorithm is proposed for AF detection from single lead ECG recordings collected by wearable devices. This algorithm includes two modules. First, denoised 1-D time series (ECG), time-frequency spectrum and Poincare plot are used to train three component learners through a parallel style, respectively, and each component learner produces four probability values. Then, all the outputs are combined using a weighted matrix constructed by a Bayesian optimization algorithm, which is capable of producing the final classification result. Quantities of experiments have been implemented, and the results well prove the algorithm’s effectiveness and its advantage over some state-of-the-art counterparts.
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Liu et al. (2022) studied Atrial fibrillation. Ensemble learning algorithm vs. State-of-the-art counterparts was evaluated on Atrial fibrillation detection. An ensemble learning algorithm effectively detected atrial fibrillation from single-lead wearable ECG recordings, demonstrating advantages over state-of-the-art counterparts.
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