Key result
A decision tree-based ensemble learning classifier using feature selection and sparse coding achieved an overall F1 score of 0.80 for detecting atrial fibrillation from ECG recordings.
Why the study?
The study was conducted to provide an algorithm for accurate, automated detection of atrial fibrillation from ECG signals.
Can a machine learning algorithm using feature selection and ensemble learning accurately detect atrial fibrillation from ECG recordings?
Can a machine learning algorithm using feature selection and ensemble learning accurately detect atrial fibrillation from ECG recordings?
Feature selection and ensemble learning can improve the performance of automated ECG-based classification of atrial fibrillation.
Algorithm for automated AF detection from varied ECGs may aid screening; leaves open prospective clinical validation before routine use.
OBJECTIVE: The objective of this paper is to provide an algorithm for accurate, automated detection of atrial fibrillation (AF) from ECG signals. Four types of ECG signals are considered: normal signals, signals representing symptoms of AF, other signals, and noisy signals. This paper represents follow-up work to the authors' entry in the 2017 PhysioNet Challenge as reported in the 2017 Computing in Cardiology Conference. APPROACH: Our approach involves extracting features from the ECG waveform and training a machine learning classifier. In feature extraction, we calculate several statistical features related to the ECG signal and fiduciary points. We also used a disciplined method of feature selection to reduce the dimensionality of the feature space. We also employ sparse coding as an unsupervised feature extraction tool. The classifier we use is a decision tree-based ensemble learning classifier. MAIN RESULTS: When applied to the hidden test data reserved by the PhysioNet Challenge organizers, our classifier reports F1 scores of 0.91, 0.78, and 0.71 for the Normal, AF, and Other classes, respectively. The overall test score is 0.80, and is obtained by averaging the F1 scores for these three classes. SIGNIFICANCE: This work demonstrates that feature selection and ensemble learning can be used to improve the performance of ECG-based classification of AF.
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Rizwan et al. (2018) studied Atrial fibrillation. Decision tree-based ensemble learning classifier with feature selection and sparse coding was evaluated on Overall F1 score for classification of ECG signals. A decision tree-based ensemble learning classifier using feature selection and sparse coding achieved an overall F1 score of 0.80 for detecting atrial fibrillation from ECG recordings.
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