An Improvement To The k-Nearest Neighbor Classifier For ECG Database

Jaafar, Harlina Suzana; Ramli, Norazan Mohamed; Nasir, Aimi Salihah Abdul

doi:10.1088/1757-899x/318/1/012046

Cited by 7 publications

(8 citation statements)

References 8 publications

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“…That is, kNN finds the k nearest instances to the instance (query) and determines its class by observing the single most common class label. The potential of the kNN classification model has been shown in an ECG-based biometric recognition system, yet there are some limitations regarding its practicality, which includes [356], [357]: (i) they possess high storage requirements, (ii) the classification is sensitive to the local distribution of the training samples, which may potentially result in the instability of performance (iii) the classification lacks a reputable means to choose k, besides by cross-validation or a related computationally-expensive method.…”

Section: ) Knn Classification Modelsmentioning

confidence: 99%

“…Notably, choosing a proper value of k is crucial as it influences the performance of the classification task of the k-NN algorithm. For example, consider the following rationale why a kNN classifier may wrongly classify a training sample: 1) The kNN is an instance-based machine learning algorithm as the learning process immediately applies the available training set, making the classification sensitive to the local distribution of the training samples leading to instability of performance [357], [358], and 2) for the basic kNN algorithm, the data cluster density influences its performance, which results in wrong decision-making [357], [358]. In [359], a kNN linear SVM and neural network were used as the classifier model for ECG-based human recognition on MIT-BIH and ECG-ID database.…”

Section: ) Knn Classification Modelsmentioning

confidence: 99%

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A Comprehensive Survey on ECG Signals as New Biometric Modality for Human Authentication: Recent Advances and Future Challenges

Uwaechia

Ramli

2021

IEEE Access

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Electrocardiogram (ECG) has extremely discriminative characteristics in the biometric field and has recently received significant interest as a promising biometric trait. However, ECG signals are susceptible to several types of noises, such as baseline wander, powerline interference, and high/lowfrequency noises, making it challenging to realize biometric identification systems precisely and robustly. Therefore, ECG signal denoising is a major pre-processing step and plays a crucial role in ECG-based biometric human identification. ECG signal analysis for biometric recognition can combine several steps, such as preprocessing, feature extraction, feature selection, feature transformation, and classification which is a very challenging task. Moreover, the selected success measures and proper structure of the ECG signal database play significant roles in biometric system analysis, considering that publicly available databases are essential by the research community to evaluate the performance of their proposed algorithms. In this survey, we review most of the techniques employed for the ECG as biometrics for human authentication. Firstly, we present an overview and discussion on ECG signal preprocessing, feature extraction, feature selection, and feature transformation for ECG-based biometric systems. Secondly, we present a survey of the available ECG databases to evaluate and compare the acquisition protocol, acquisition hardware, and acquisition resolution (bits) for ECG-based biometric systems. Thirdly, we also present a survey on different techniques, including deep learning methods: deep supervised learning, deep semi-supervised learning, and deep unsupervised learning, for ECG signal classification. Lastly, we present the state-of-art approaches of information fusion in multimodal biometric systems.

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Section: ) Knn Classification Modelsmentioning

confidence: 99%

Section: ) Knn Classification Modelsmentioning

confidence: 99%

A Comprehensive Survey on ECG Signals as New Biometric Modality for Human Authentication: Recent Advances and Future Challenges

Uwaechia

Ramli

2021

IEEE Access

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“…In classical K NN [28], given a set U of patients described by a set A − of features, a normalization function is first applied to the set N ⊂ A − to scale the numerical features to the interval [0, 1] to prevent features with large values from outweighing those with smaller values. A normalized feature…”

Section: Preliminariesmentioning

confidence: 99%

“…It uses rough set theory (RST) techniques to handle categorical features and classical distance metrics to handle numerical features. As such, K NNV does not convert categorical feature values into numbers, thereby making all the features numerical and then using distance metrics to identify nearest neighbors, as is done in classical K NN (see for example, [28][29][30]). By applying RST techniques to categorical features, K NNV solves two problems at once: incompleteness of those features and vagueness of the proper value of K .…”

Section: Knnv Algorithmmentioning

confidence: 99%

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Accurate Classification of COVID-19 Based on Incomplete Heterogeneous Data using a KNN Variant Algorithm

2021

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Great efforts are now underway to control the coronavirus 2019 disease (COVID-19). Millions of people are medically examined, and their data keep piling up awaiting classification. The data are typically both incomplete and heterogeneous which hampers classical classification algorithms. Some researchers have recently modified the popular K NN algorithm as a solution, where they handle incompleteness by imputation and heterogeneity by converting categorical data into numbers. In this article, we introduce a novel K NN variant (K NNV) algorithm that provides better results as demonstrated by thorough experimental work. We employ rough set theoretic techniques to handle both incompleteness and heterogeneity, as well as to find an ideal value for K. The K NNV algorithm takes an incomplete, heterogeneous dataset, containing medical records of people, and identifies those cases with COVID-19. We use in the process two popular distance metrics, Euclidean and Mahalanobis, in an effort to widen the operational scope. The K NNV algorithm is implemented and tested on a real dataset from the Italian Society of Medical and Interventional Radiology. The experimental results show that it can efficiently and accurately classify COVID-19 cases. It is also compared to three K NN derivatives. The comparison results show that it greatly outperforms all its competitors in terms of four metrics: precision, recall, accuracy, and F-Score. The algorithm given in this article can be easily applied to classify other diseases. Moreover, its methodology can be further extended to do general classification tasks outside the medical field.

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An end-to-end model for ECG signals classification based on residual attention network

Wang²,

Zhang³

et al. 2023

Biomedical Signal Processing and Control

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An Improvement To The k-Nearest Neighbor Classifier For ECG Database

Cited by 7 publications

References 8 publications

A Comprehensive Survey on ECG Signals as New Biometric Modality for Human Authentication: Recent Advances and Future Challenges

A Comprehensive Survey on ECG Signals as New Biometric Modality for Human Authentication: Recent Advances and Future Challenges

Accurate Classification of COVID-19 Based on Incomplete Heterogeneous Data using a KNN Variant Algorithm

An end-to-end model for ECG signals classification based on residual attention network

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