Fast multi-scale feature fusion for ECG heartbeat classification

Ai, Danni; Yang, Jian; Wang, Zeyu; Fan, Jingfan; Ai, Changbin; Wang, Yongtian

doi:10.1186/s13634-015-0231-0

Cited by 30 publications

(9 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Finally, the individual decisions of three different classifiers are fused together based on the majority voting [26]. Danni et al proposed a fast feature-fusion method of ECG heartbeat classification based on multi-linear subspace learning [27]. In different fusion levels, the complementary information contained in signals is different based on different forms of data, so the fusion results are not the same.…”

Section: Introductionmentioning

confidence: 99%

A Novel Two-Level Fusion Feature for Mixed ECG Identity Recognition

Liu

Yang

2021

Electronics

View full text Add to dashboard Cite

In recent years, with the increasing standard of biometric identification, it is difficult to meet the requirements of data size and accuracy in practical application for training a single ECG (electrocardiogram) database. The paper aims to construct a recognition model for processing multi-source data and proposes a novel ECG identification system based on two-level fusion features. Firstly, the features of Hilbert transform and power spectrum are extracted from the segmented heartbeat data, then two features are combined into a set and normalized to obtain the elementary fusion feature. Secondly, PCANet (Principal Component Analysis Network) is used to extract the discriminative deep feature of signal, and MF (MaxFusion) algorithm is proposed to fuse and compress the two layers learning features. Finally, a linear support vector machine (SVM) is used to obtain labels of single feature classification and complete the individual identification. The recognition results of the proposed two-level fusion PCANet deep recognition network achieve more than 95% on ECG-ID, MIT-BIH, and PTB public databases. Most importantly, the recognition accuracy of the mixed database can reach 99.77%, which includes 426 individuals.

show abstract

Section: Introductionmentioning

confidence: 99%

A Novel Two-Level Fusion Feature for Mixed ECG Identity Recognition

Liu

Yang

2021

Electronics

View full text Add to dashboard Cite

show abstract

“…Furthermore, the diagnosis results are affected by many subjective factors. In order to solve the problems, automatic ECG classification methods have been proposed to improve the diagnosis efficiency and accuracy, and some pioneer works have been done [2][3][4]. Most current research focuses on single label classification.…”

Section: Introductionmentioning

confidence: 99%

Multi-Label ECG Signal Classification Based on Ensemble Classifier

et al. 2020

View full text Add to dashboard Cite

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.

show abstract

“…Conventionally, the R-R interval is used to data extraction from the ECG signal in order to diagnose different types of arrhythmia [3][4][5][6][7][8]. However, the analysis of the R-R intervals is not able to measure changes on other ECG waves, such as the distortions on P wave for atrial fibrillation (AF) [9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Support system for classification of beat-to-beat arrhythmia based on variability and morphology of electrocardiogram

Queiroz

Azoubel

Barros

2019

EURASIP J. Adv. Signal Process.

View full text Add to dashboard Cite

Background: Several authors use the R-R interval, which is the temporal difference between the largest waves (R waves) of the electrocardiogram (ECG), to propose a support system for the diagnosis of arrhythmias. However, R-R interval analysis does not measure ECG waveform deformations such as P wave deformations for atrial fibrillation. Objective: In this study, we propose an arbitrary analysis the any segment of the heartbeat. This analysis is a generalization of a previous work that measures the wave deformations of the ECG signal. Methods: We proposed to investigate the voltage (mV) variation occurring at each heartbeat interval using statistical moments. Unlike the R-R interval in which each heartbeat is associated with a single real number, the proposed method associates each heartbeat to a set of points, that is, a vector. The heartbeats were obtained in the following databases: MIT-BIH Normal Sinus Rhythm, MIT-BIH Atrial Fibrillation (AF), and MIT-BIH Arrhythmia; and the classifiers used to evaluate the proposed method were linear discriminant analysis, k-nearest neighbors, and support vector machine. The experiments were conducted using 80% of the patients for training (16 healthy patients, 41 patients with arrhythmia, and 20 patients with AF) and 20% of the patients for testing (2 healthy patients, 6 patients with arrhythmia, and 3 patients with AF). Results: The proposed method proved to be efficient in solving global (accuracy is up to 99.78% in the arrhythmia classification) and local (accuracy of 100% in the AF classification) heartbeat problems. Conclusion: The results obtained by the proposed method can be used to support decision-making in clinical practices.

show abstract

Fast multi-scale feature fusion for ECG heartbeat classification

Cited by 30 publications

References 28 publications

A Novel Two-Level Fusion Feature for Mixed ECG Identity Recognition

A Novel Two-Level Fusion Feature for Mixed ECG Identity Recognition

Multi-Label ECG Signal Classification Based on Ensemble Classifier

Support system for classification of beat-to-beat arrhythmia based on variability and morphology of electrocardiogram

Contact Info

Product

Resources

About