Abstract
The electrocardiogram (ECG) is one of the most important techniques for heart disease diagnosis. Many traditional methodologies of feature extraction and classification have been widely applied to ECG analysis. However, the effectiveness and efficiency of such methodologies remain to be improved, and much existing research did not consider the separation of training and testing samples from the same set of patients (so called inter-patient scheme). To cope with these issues, in this paper, we propose a method to classify ECG signals using wavelet packet entropy (WPE) and random forests (RF) following the Association for the Advancement of Medical Instrumentation (AAMI) recommendations and the inter-patient scheme. Specifically, we firstly decompose the ECG signals by wavelet packet decomposition (WPD), and then calculate entropy from the decomposed coefficients as representative features, and finally use RF to build an ECG classification model. To the best of our knowledge, it is the first time that WPE and RF are used to classify ECG following the AAMI recommendations and the inter-patient scheme. Extensive experiments are conducted on the publicly available MIT–BIH Arrhythmia database and influence of mother wavelets and level of decomposition for WPD, type of entropy and the number of base learners in RF on the performance are also discussed. The experimental results are superior to those by several state-of-the-art competing methods, showing that WPE and RF is promising for ECG classification.
Highlights
The electrocardiogram (ECG) records the tiny electrical activity produced by the heart over a period of time by placing electrodes on a patient’s body, which has become the most widely used non-invasive technique for heart disease diagnoses in the clinics
Recommendations and the inter-patient scheme, which made the proposed method reproducible and more practical; (3) the experimental results on the publicly accessed the MIT–BIH Arrhythmia dataset [40] show that the proposed method is promising for ECG classification; and (4) types of entropy, mother wavelets of wavelet packet decomposition (WPD), decomposed levels for WPD and the tree numbers of random forests (RF) were discussed and the suggestions on these settings were given
In order to evaluate the performance of the proposed work, we compared it with some state-of-the-art methods of feature extraction and classification
Summary
The electrocardiogram (ECG) records the tiny electrical activity produced by the heart over a period of time by placing electrodes on a patient’s body, which has become the most widely used non-invasive technique for heart disease diagnoses in the clinics. The classification of ECG signals has four phases: preprocessing, segmentation, feature extraction and classification. The preprocessing phase is mainly aimed at detecting and attenuating frequencies of the ECG signal related to artifacts, which usually performs signal normalization and enhancement. Segmentation divides the signal into smaller segments, which can better express the electrical activity of the heart [1]. The researchers can get good results from preprocessing and segmentation by some popular techniques or tools [2]
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