Usage of Model Driven Environment for the Classification of ECG features: A Systematic Review

Iqbal, Uzair; Wah, Teh Ying; Rehman, Muhammad Habib ur; Mastoi, Qurat-ul-ain

doi:10.1109/access.2018.2828882

Cited by 13 publications

(10 citation statements)

References 36 publications

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“…From Figure 4, we can see that the classification performance can improve clearly as the parameter k edit increases within an interval ( [3,12] in this example). However, when k edit exceeded an upper boundary (k edit = 12 in this example), the classification performance no longer improved ideally.…”

Section: Parameter Analysismentioning

confidence: 85%

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EEkNN: k-Nearest Neighbor Classifier with an Evidential Editing Procedure for Training Samples

Jiao

Geng

2019

Electronics

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The k-nearest neighbor (kNN) rule is one of the most popular classification algorithms applied in many fields because it is very simple to understand and easy to design. However, one of the major problems encountered in using the kNN rule is that all of the training samples are considered equally important in the assignment of the class label to the query pattern. In this paper, an evidential editing version of the kNN rule is developed within the framework of belief function theory. The proposal is composed of two procedures. An evidential editing procedure is first proposed to reassign the original training samples with new labels represented by an evidential membership structure, which provides a general representation model regarding the class membership of the training samples. After editing, a classification procedure specifically designed for evidently edited training samples is developed in the belief function framework to handle the more general situation in which the edited training samples are assigned dependent evidential labels. Three synthetic datasets and six real datasets collected from various fields were used to evaluate the performance of the proposed method. The reported results show that the proposal achieves better performance than other considered kNN-based methods, especially for datasets with high imprecision ratios.

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Section: Parameter Analysismentioning

confidence: 85%

“…Classification of patterns is an important area of research and practical applications in a variety of fields including biology [1], psychology [2], medicine [3], electronics [4], marketing [5], military affairs [6], etc. In the past several decades, a wide variety of approaches has been developed towards this task [7].…”

Section: Introductionmentioning

confidence: 99%

EEkNN: k-Nearest Neighbor Classifier with an Evidential Editing Procedure for Training Samples

Jiao

Geng

2019

Electronics

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“…However, in real applications, the heuristics of designing process and the complicacy of ECG data unavoidably limit the feature power. Therefore, learning the classifiers that can further improve the intra-class compactness and inter-class separation of heartbeat samples in the feature space is also important and indispensable for classification [1], [72], [73].…”

Section: Wavelets For Heartbeat Classification 1) Philosophymentioning

confidence: 99%

Wavelets for Electrocardiogram: Overview and Taxonomy

2019

IEEE Access

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Physiological and pathological information within electrocardiogram (ECG) is crucial for the diagnosis of heart diseases. Computer-aided diagnosis for the ECG signals has drawn growing research attention up to date. Automatic ECG analysis mainly includes signal denoising, wave detection, and heartbeat classification. These three issues are relevant that the signal denoising can help attenuate the noises and accentuate the typical waves in ECG signals for wave detection, and wave detection can help locate the typical ECG waves and acquire the diagnostically valuable heartbeats based on these waves for the heartbeat classification. The wavelet-based methods play important roles in the three issues, but these methods are scattered and unorganized in the literature. In order to manifest the value of these methods, this paper contributes an overview and taxonomy on them. This paper does the comprehensive summary and systematic categorization on the methods for signal denoising, wave detection, and heartbeat classification according to the deep analysis of their methodological characteristics. By doing so, this paper not only uncovers the inner mechanism that why wavelet-based methods are suitable for ECG analysis but also reveals the designing principles that these methods potentially follow. Finally, this paper has provided an outlook for the developing prospect of ''wavelets for ECG'' in the future. INDEX TERMS Wavelets for electrocardiogram, signal denoising, wave detection, heartbeat classification, overview and taxonomy. I. OUTLINE AND INTRODUCTION A. OUTLINE AND CONTRIBUTION

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“…Automatic classification of patterns is an important problem in a variety of engineering and scientific disciplines such as biology [1], psychology [2], medicine [3], marketing [4], military affairs [5], etc. Generally, complete statistical knowledge regarding the conditional density of each class is rarely available, which precludes applications of the optimal Bayes classification procedure [6].…”

Section: Introductionmentioning

confidence: 99%

BP$k$ NN: $k$ -Nearest Neighbor Classifier With Pairwise Distance Metrics and Belief Function Theory

Jiao

Geng

2019

IEEE Access

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The k-nearest neighbor (kNN) rule is one of the most popular classification algorithms in pattern recognition field because it is very simple to understand but works quite well in practice. However, the performance of the kNN rule depends critically on its being given a good distance metric over the input space, especially in small data set situations. In this paper, a new kNN-based classifier, called BPkNN, is developed based on pairwise distance metrics and belief function theory. The idea of the proposal is that instead of learning a global distance metric, we first decompose it into learning a group of pairwise distance metrics. Then, based on each learned pairwise distance metric, a pairwise kNN (PkNN) sub-classifier can be adaptively designed to separate two classes. Finally, a polychotomous classification problem is solved by combining the outputs of these PkNN sub-classifiers in belief function framework. The BPkNN classifier improves the classification performance thanks to the new distance metrics which provide more flexibility to design the feature weights and the belief function-based combination method which can better address the uncertainty involved in the outputs of the sub-classifiers. Experimental results based on synthetic and real data sets show that the proposed BPkNN can achieve better classification accuracy in comparison with some state-of-the-art methods. INDEX TERMS Pattern classification, k-nearest-neighbor classifier, pairwise distance metric, belief function theory.

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Usage of Model Driven Environment for the Classification of ECG features: A Systematic Review

Cited by 13 publications

References 36 publications

EEkNN: k-Nearest Neighbor Classifier with an Evidential Editing Procedure for Training Samples

EEkNN: k-Nearest Neighbor Classifier with an Evidential Editing Procedure for Training Samples

Wavelets for Electrocardiogram: Overview and Taxonomy

BP$k$ NN: $k$ -Nearest Neighbor Classifier With Pairwise Distance Metrics and Belief Function Theory

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