AF detection from ECG recordings using feature selection, sparse coding, and ensemble learning

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
reserve...

Source: Physiological Measurement - December 24, 2018 Category: Physiology Authors: Muhammed Rizwan, Bradley M Whitaker and David V Anderson Source Type: research