Highly Accurate ECG Beat Classification Based on Continuous Wavelet Transformation and Multiple Support Vector Machine Classifiers

Zellmer, Erik R.; Shang, Fei; Zhang, Hao

doi:10.1109/bmei.2009.5305280

Cited by 15 publications

(7 citation statements)

References 7 publications

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“…Poree et al [ 25 ] used the maximal correlation coefficient applied over a 12-lead ECG, and a cross correlation of a 12-lead ECG was used for human verification [ 26 ] and identification [ 27 ]. Many researchers have suggested various sophisticated algorithms to classify the ECG signals, e.g., neural networks [ 20 , 28 , 29 ], independent component analysis [ 30 ], k-nearest neighborhood [ 31 , 32 , 33 , 34 ], and support vector machine [ 35 , 36 , 37 , 38 , 39 ]. Recently, multi-modal identifications based on data fusion and dimensionality reduction have been investigated.…”

Section: Introductionmentioning

confidence: 99%

Individual Biometric Identification Using Multi-Cycle Electrocardiographic Waveform Patterns

Lee

Kim

2018

Sensors

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The electrocardiogram (ECG) waveform conveys information regarding the electrical property of the heart. The patterns vary depending on the individual heart characteristics. ECG features can be potentially used for biometric recognition. This study presents a new method using the entire ECG waveform pattern for matching and demonstrates that the approach can potentially be employed for individual biometric identification. Multi-cycle ECG signals were assessed using an ECG measuring circuit, and three electrodes can be patched on the wrists or fingers for considering various measurements. For biometric identification, our-fold cross validation was used in the experiments for assessing how the results of a statistical analysis will generalize to an independent data set. Four different pattern matching algorithms, i.e., cosine similarity, cross correlation, city block distance, and Euclidean distances, were tested to compare the individual identification performances with a single channel of ECG signal (3-wire ECG). To evaluate the pattern matching for biometric identification, the ECG recordings for each subject were partitioned into training and test set. The suggested method obtained a maximum performance of 89.9% accuracy with two heartbeats of ECG signals measured on the wrist and 93.3% accuracy with three heartbeats for 55 subjects. The performance rate with ECG signals measured on the fingers improved up to 99.3% with two heartbeats and 100% with three heartbeats of signals for 20 subjects.

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Section: Introductionmentioning

confidence: 99%

Individual Biometric Identification Using Multi-Cycle Electrocardiographic Waveform Patterns

Lee

Kim

2018

Sensors

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“…The usage of wavelet-compressed data from the St. Petersburg Institute of Cardiological Technics 12lead arrhythmia database was our first step. In general, the MIT-BIH arrhythmia database is used by most research that is performed on ECG signals [1][2][3][4][5][6][7][8][9][10][11][12]14,16,19,[21][22][23]25,26], and classification is executed on a single channel [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][21][22][23][24][25][26][27]. Our research was performed on 12 channels.…”

Section: Discussionmentioning

confidence: 99%

Bagged tree classi cation of arrhythmia using wavelets for denoising, compression, and feature extraction

Tomak¹,

Kayıkçıoğlu²

2018

Turk J Elec Eng & Comp Sci

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Arrhythmia, also known as dysrhythmia, is a condition involving an irregular heartbeat. A problem in the heart may cause problems in other organs, and as time passes, this will lead to more severe problems. Arrhythmia must be detected at an early stage to prevent such a problem occurring in the heart. Detection of arrhythmia from an electrocardiogram is an easy method that does not need much equipment and does not harm the patient. The purpose of this research is to find a faster and more accurate system to classify nine classes of arrhythmia. The St. Petersburg Institute of Cardiological Technics 12-lead arrhythmia database was used for training and testing. Data were compressed and preprocessed (denoising, trend elimination, baseline correction, and normalization) before being sent to the system for feature calculation. The wavelet coefficients that displayed the most significant effect on classification were chosen and used as features. Standard deviation and variance were also added to the feature set. Later, principal component analysis (PCA) was used to reduce the number of features further. After deciding the features, the performance of the basic classification methods and spiking neural network was checked to determine whether there was a better classifier to be used for our research. Tenfold cross-validation was applied to the training dataset. Bagged trees were found to produce better results. The classifiers' performance was tested by sensitivity, specificity, and accuracy.

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“…Unlike the short time Fourier transformation (STFT) the wavelet transformation has very good time and frequency resolution making it ideal in the analysis of non-stationary signals such as an ECG signal. The continuous wavelet transformation (CWT) of a signal x(t) is the convolution product of x (t) with a scaled and translated kernel function [15] (1)…”

Section: Continuous Wavelet Transform (Cwt)mentioning

confidence: 99%

ANN-based Classifier for PAF Prediction

AnwarFahmy¹,

Raddady²

2013

IJCA

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In this paper we propose an artificial neural network as a based classifier for prediction of Paroxysmal Atrial Fibrillation (PAF). PAF is a really life threatening disease and it is the result of irregular and repeated depolarization of the atria. We used PAF prediction data base which include 30-min. period of 100 ECG recorded signals. We divide the 30-min preceding the PAF into 6 periods with 5-min each. In each suggested period we get the classification result using ANN. The results show that we can predict the PAF accurately in 5-min & 20-min prior the PAF. In these two periods, the measured sensitivity, specificity, positive predictivity and accuracy show better and significant results comparable to the other periods. Also the results outperform the obtained results in the same field in the literature.

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Highly Accurate ECG Beat Classification Based on Continuous Wavelet Transformation and Multiple Support Vector Machine Classifiers

Cited by 15 publications

References 7 publications

Individual Biometric Identification Using Multi-Cycle Electrocardiographic Waveform Patterns

Individual Biometric Identification Using Multi-Cycle Electrocardiographic Waveform Patterns

Bagged tree classi cation of arrhythmia using wavelets for denoising, compression, and feature extraction

ANN-based Classifier for PAF Prediction

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