An XGBoost machine learning pipeline classified single lead ECG waveforms with a hidden test F1 score of 0.8125, including an F1 score of 0.8156 for atrial fibrillation.
Does a step-by-step machine learning pipeline using XGBoost accurately classify single-lead ECG waveforms for atrial fibrillation?
An XGBoost-based machine learning pipeline achieved an F1 score of 0.8125 on the hidden test set for classifying single-lead ECGs into normal sinus rhythm, atrial fibrillation, other rhythm, or noisy.
Abstract This paper presents a detailed overview of our submission to the 2017 Physionet Challenge where competitors were asked to build a model to classify a single lead ECG waveform as either normal sinus rhythm, atrial fibrillation, other rhythm, or noisy. A step-by-step machine learning pipeline was assembled, which included signal conditioning, R-peak detection and filtering, and feature extraction. A suite of over 300 features, falling into one of three main feature groups; template features, RRI features, and full waveform features, were extracted from each waveform and an XGBoost, tree-based, gradient boosting classifier was used as the machine learning algorithm. The model produced a cross-validation F 1 score of 0.8245, a hidden sub-test score of 0.82, and a hidden test score of 0.8125. The score breakdown for each class (normal sinus rhythm, atrial fibrillation, other rhythm, and noisy) was as follows: F 1, NRS = 0.9024, F 1, AF = 0.8156, F 1, OR = 0.7194, F 1, Noise = 0.5705.
Goodfellow et al. (Wed,) conducted a other in Atrial fibrillation. Step-by-step machine learning pipeline (XGBoost classifier) was evaluated on Classification of single lead ECG waveform as normal sinus rhythm, atrial fibrillation, other rhythm, or noisy (F1 score). An XGBoost machine learning pipeline classified single lead ECG waveforms with a hidden test F1 score of 0.8125, including an F1 score of 0.8156 for atrial fibrillation.
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