Nearest neighbor search with locally weighted linear regression for heartbeat classification

Park, Ju‐Young; Bhuiyan, Zakirul Alam; Kang, Min-Hee; Son, Junggab; Kang, Kyungtae

doi:10.1007/s00500-016-2410-9

Cited by 14 publications

(4 citation statements)

References 28 publications

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“…Next the start and end points of the QRS complex will be used to extract the PR interval, QRS interval, and QT interval. In recent years, there are many methods to identify PVC based on PVC disease characteristics, and we used 7 features (R amplitude [16], PR interval, QRS interval, QT interval, QRS area [17], pre_RR interval [18, 19], and post_RR interval [20]) for the study. In order to facilitate the subsequent calculation and understanding, the seven features are, respectively, recorded as R_amp, PR, QRS, QT, QRS_area, pre_RR, and post_RR.…”

Section: Pvc Identification Methods Based On Featuresmentioning

confidence: 99%

Intelligent Analysis of Premature Ventricular Contraction Based on Features and Random Forest

Xie

Shen

et al. 2019

Journal of Healthcare Engineering

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Premature ventricular contraction (PVC) is one of the most common arrhythmias in the clinic. Due to its variability and susceptibility, patients may be at risk at any time. The rapid and accurate classification of PVC is of great significance for the treatment of diseases. Aiming at this problem, this paper proposes a method based on the combination of features and random forest to identify PVC. The RR intervals (pre_RR and post_RR), R amplitude, and QRS area are chosen as the features because they are able to identify PVC better. The experiment was validated on the MIT-BIH arrhythmia database and achieved good results. Compared with other methods, the accuracy of this method has been significantly improved.

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Section: Pvc Identification Methods Based On Featuresmentioning

confidence: 99%

Intelligent Analysis of Premature Ventricular Contraction Based on Features and Random Forest

Xie

Shen

et al. 2019

Journal of Healthcare Engineering

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“…The length of the interval between the R peak of the current beat and its following heartbeat is called pos-RR. These two values are combined into an average RR interval [avg-RR, calculated by (1)] to provide instantaneous rhythm information of the heartbeat…”

Section: Heartbeat Segmentationmentioning

confidence: 99%

“…Cardiac arrhythmias result from any change in rate, regularity, place of origin or conduction of cardiac electrical impulses, and their symptoms range from small palpitations and chest pain to fainting and sudden death depending on the type and severity of the problem [1]. The electrocardiogram (ECG or EKG) is a good alternative for the analysis and diagnosis of arrhythmias, as it is non‐invasive, inexpensive and contains physiological signals which are essential for the analysis of cardiac functions and pathologies [2].…”

Section: Introductionmentioning

confidence: 99%

Heartbeat classification with low computational cost using Hjorth parameters

Leite

Moreno

2018

IET signal process.

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A method for electrocardiogram (ECG) feature extraction is presented for automatic classification of heartbeats, using values of RR intervals, amplitude and Hjorth parameters. Hjorth parameters have been used in a variety of research areas, but their application to ECG signal processing is still little explored. This paper also introduces a new approach to heartbeat segmentation, which avoids mixing information from adjacent beats and improves classification performance. The proposed model is validated in the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) Arrhythmia database and presents an overall accuracy of 90.4%, better than other state-of-the-art methods. There is an improvement over other models in positive predictivity for class S (66.6%) of supraventricular ectopic beats, and sensitivity for class N (93.0%). Results obtained indicate that the techniques used in this study can be successfully applied to the problem of automatic heartbeat classification. In addition, this new approach has low computational cost, which allows its later implementation in hardware devices with limited resources.

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“…Many works have been done on the classification algorithms, such as healthcare (Kumar and Inbarani, 2016;Zhang et al, 2016;Park et al, 2016), network Palmieri et al, 2014;Fiore et al, 2013) and imaging (Li et al, 2015;Alok et al, 2015;Elguebaly and Bouguila, 2015). On biometric field some works have been done for the processing of minutiae templates such as the orientation field reconstruction (Roy and Trivedi, 2014;Oehlmann et al, 2015), the matching algorithm (Jain and Pankanti, 2000;Kumar et al, 2014), the fingerprint protection (Jayaraman et al, 2014;Vigila et al, 2014).…”

Section: Related Workmentioning

confidence: 99%