Set-Based Discriminative Measure for Electrocardiogram Beat Classification

Li, Wei; Li, Jianqing; Qin, Qin

doi:10.3390/s17020234

Cited by 11 publications

(6 citation statements)

References 42 publications

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“…If the size of the sampling points before the first R peak or after the last R peak is less than our setting, the incomplete heartbeat has to be automatically neglected to keep the heartbeat length as the presetting. Generally, this segmentation can approximately capture the morphology of every heartbeat whilst avoiding the information redundancy or reduction as far as possible, according to both our previous research validation and other researchers' approval [59–63]. Some fast heartbeats may look thin and some slow ones may be fat in morphology, but such data variations will not impede any method demonstration actually.…”

Section: Methodsmentioning

confidence: 99%

“…The anomalous outliers of one class may stay in the proximity, or, more seriously, become the intruders of other irrelevant classes, so these outliers often damage or even disable the point-to-point distance measure. To address this problem, we suggest measuring the set-to-set distance instead of frequently-used point-to-point distance that merely depends on two samples, inspired by our previous success of set-based distances for other research topics including image-based human re-identification and heartbeat-based arrhythmia classification [59,61,64,65]. Generally, set-based distances are modelled on the basis of point-to-point distances.…”

Section: Set-based Distance Measurementioning

confidence: 99%

“…This is because the large intra-class variation and small inter-class difference of the samples are easy to make the centres of sets deviate from discriminative positions. Hence, besides APD, we turn to consider another two candidate setbased distances, Minimum Pointwise Distance (MPD) and MBD [59,64].…”

Section: Set-based Distance Measurementioning

confidence: 99%

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Turning waste into wealth: Person identification by emotion‐disturbed electrocardiogram

Fang

Zhu

et al. 2023

IET Biometrics

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The issue of electrocardiogram (ECG)‐based person identification has attracted intense research interests nowadays. Different than existing related researches that advocate accentuating useful information and attenuating noisy artefacts in sensor data processing, A novel strategy of ‘turning waste into wealth’ is proposed to exploit the new discriminative information from the relationship between noise disturbance and signal data for this issue. Specifically, the authors design a new and simple method, the Set‐Group Distance Measure, based on the suitable fusion of multiple minority‐based distance measurements, whose power has initially been discovered for the issue. This method takes advantage of the collaborative variation information from the relative relationship, which is named as ‘relative information’, between different types of emotional noise disturbances and ECG signal data, to tackle the problem of large intra‐class variation but small inter‐class difference during identification. Experimental results have demonstrated the reasonability, effectiveness, robustness, efficiency and practicability of the proposed method upon public benchmark databases. This proposal not only provides technological inspirations for the further study in ECG‐based person identification, but also shows a fresh feasible way to handle the noise‐signal relationship for more general topics of sensor data classification.

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Section: Methodsmentioning

confidence: 99%

Section: Set-based Distance Measurementioning

confidence: 99%

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Turning waste into wealth: Person identification by emotion‐disturbed electrocardiogram

Fang

Zhu

et al. 2023

IET Biometrics

Self Cite

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“…Li et al [93] have designed the WCF for the ECG heartbeats using the coefficients of the DWT with the mother wavelet selected from Reverse Bior6.8 (RBior6.8), Fejer-Korovkin22 (FK22), and so forth, and then adopted the Metric Learning to Rank (MLR) to improve the discriminative ability of the feature space, and finally measured the Minority Based Dissimilarity (MBD) between the feature sets for multiple-beat arrhythmia classification. Abdullah et al [94] have concatenated the WCF with the TF which is extracted based on the R peaks detected by the DWT together as the representation of each ECG heartbeat, and then implemented the quadratic discriminant analysis on the basis of this representation for arrhythmia recognition.…”

Section: D: Besides Neural Network and Support Vector Machine Many mentioning

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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“…Therefore, automatic ECG testing is necessary. In spite of decades of innovations in filtering, examination, and also developing various types of ECG, (6)(7)(8)(9) noise disturbance over ECG, divided into types of signs and symptoms, and even a specific variant challenge both the credibility and precision of ECG signal classification.…”

Section: Introductionmentioning

confidence: 99%

Automatic Electrocardiogram Sensing Classifier Based on Improved Backpropagation Neural Network

Mao¹,

Chang²

2020

Sensors and Materials

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As hear t disease is among the common diseases endangering human life, the electrocardiogram (ECG) recognition of various categories of abnormal heartbeat rhythms is essential for boosting the success rate of treatments for this illness. In this paper, we propose an automated ECG recognition method based on a backpropagation (BP) neural network. First, biorthogonal (bior) wavelet denoising was adopted to eliminate baseline drift as well as highfrequency noise in the ECG. Then, a dyadic spline wavelet was used to detect the QRS, T, and P waves. Six amplitude features and 15 range features were extracted to better represent the local and global features of the ECG, respectively. Finally, the optimum BP neural network (BPNN) model was utilized to identify the ECG signal. The optimum BPNN model exhibited a steady precision of more than 99% in the recognition of ECG signals, which is superior to the results for a support vector machine (SVM) and a convolutional neural network (CNN), with a significantly improved correct recognition rate of type 5 ECG signals.

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Set-Based Discriminative Measure for Electrocardiogram Beat Classification

Cited by 11 publications

References 42 publications

Turning waste into wealth: Person identification by emotion‐disturbed electrocardiogram

Turning waste into wealth: Person identification by emotion‐disturbed electrocardiogram

Wavelets for Electrocardiogram: Overview and Taxonomy

Automatic Electrocardiogram Sensing Classifier Based on Improved Backpropagation Neural Network

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