A novel LMS algorithm for ECG signal preprocessing and KNN classifier based abnormality detection

Venkatesan, C.; Karthigaikumar, P.; Varatharajan, R.

doi:10.1007/s11042-018-5762-6

Cited by 108 publications

(23 citation statements)

References 23 publications

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“…Different [27]. Many methodologies have been adopted to classify these extracted features, such as SVM [28], kNN rules [29], artificial neural networks [30], Bayesian network [31], wavelet transform and so on [32]. Little research work about multi-label ECG signal classification has been done.…”

Section: Related Workmentioning

confidence: 99%

Multi-Label ECG Signal Classification Based on Ensemble Classifier

et al. 2020

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Electrocardiogram (ECG) has been proved to be the most common and effective approach to investigate the cardiovascular disease because that it is simple, non-invasive and low cost. ECG signal automatic classification is a popular research topic and some efficient research work has been done on it. Most of current research work focuses on single ECG label classification, i.e. one ECG signal record corresponds to one label. In practice, one ECG signal usually embraces several cardiovascular diseases at the same time. It is more important to study multi-label ECG signal classification. To our knowledge, few research works have been done on the research topic. To resolve the multi-label ECG signal classification problems, we propose a novel ensemble multi-label classification model in this paper. The model combines several multi-label classification methods to generate a high performance classifier. Mutual information is used to measure the weight of each classifier. At last the ensemble multi-label classification model is used to analyze a clinic ECG signal dataset. The analysis results show that the overall classification performance is improved. It provides a feasible analysis method for multi-label ECG signal automatic classification.

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Section: Related Workmentioning

confidence: 99%

Multi-Label ECG Signal Classification Based on Ensemble Classifier

et al. 2020

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“…After being decomposed by the second stage, V 1 is decomposed into low frequency V 2 and high frequency W 2 . The figure above is a three-level decomposition of the space V 0 [32]. The decomposition process of this subspace can be recorded as follows.…”

Section: B Multiresolution Analysismentioning

confidence: 99%

Research on Hierarchical Data Fusion of Intelligent Medical Monitoring

Xue

Lu²,

Wang³

et al. 2020

IEEE Access

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The IOT-based intelligent telemedicine system is an important direction for future development. It combines information space and physical space to inform and network people's living environment. The distribution of medical resources in China is extremely uneven. There are many excellent medical resources in big cities. The resources of small and medium-sized cities are very few, which leads to patients going to large city hospitals for medical treatment, resulting in hospital overcrowding. Intelligent telemedicine system sinks excellent medical resources. Patients can also enjoy the excellent medical resources of the big city hospitals in the local area. Therefore, the use of advanced networks and science and technology, the integration of information technology and medical technology, the establishment of intelligent telemedicine system, will certainly optimize the hospital business process. This paper proposes research on multi-level data fusion under intelligent medical monitoring, which will improve its resource utilization and work efficiency, and promote hospital hierarchical data construction. Innovative use of Internet of Things technology promotes academic exchanges among hospitals, which improves doctors' technical skills, and hospital medical standards. The development of the Internet of Things is significant to the development of China's medical industry. It is hoped that this article can help the development of telemedicine in China.

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“…The NLMS algorithm [6] gives poor error reduction in a higher error noise environment. The ENLMS algorithm [13] gives good performance over NLMS. However, it is affected by a high primary signal noise variance environment.…”

Section: Introductionmentioning

confidence: 99%

Two Stage Step-Size Scaler Adaptive Filter Design for ECG Denoising

Prajapati

Darji

2021

2021 IEEE International Symposium on Circuits and Systems (ISCAS)

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In recent years, various healthcare wearable devices have increased to monitor electrocardiogram (ECG) continuously, but due to everyday life activities, artifacts are present in it, which may increases the false prediction of R-peak. This work presents the two-stage active noise cancellation (ANC) stepsize scaler adaptive filter to remove high and low frequencies artifacts from the ECG signal. Moreover, the convergence rate and mean square error (MSE) minimization analysis of step-size scaler adaptive filter in different simulated noise environments is presented and compared with the least mean square (LMS) adaptive filter variants. The analysis shows that the step-size scaler algorithm gives a 67.34% fast convergence rate compared to the normalized LMS (NLMS) algorithm.

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A novel LMS algorithm for ECG signal preprocessing and KNN classifier based abnormality detection

Cited by 108 publications

References 23 publications

Multi-Label ECG Signal Classification Based on Ensemble Classifier

Multi-Label ECG Signal Classification Based on Ensemble Classifier

Research on Hierarchical Data Fusion of Intelligent Medical Monitoring

Two Stage Step-Size Scaler Adaptive Filter Design for ECG Denoising

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