ECG Identification Based on Wavelet Transform

Wang, Weiquan; Pan, Lu; Lin, Jun; Zhang, Jin

doi:10.2991/jimec-16.2016.89

Cited by 6 publications

(5 citation statements)

References 1 publication

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“…The wavelet transform method was also applied in the ECG biometric by Wei-Quan [6] conducting a detailed deduction of the wavelet transform and continued with the accuracy test through the MATLAB simulation. The research of Wei-Quan, however, did not give any reports about the authentication or classification method used.…”

Section: Introductionmentioning

confidence: 99%

One-Lead Electrocardiogram for Biometric Authentication using Time Series Analysis and Support Vector Machine

Hadiyoso¹,

Aulia²,

Rizal³

2019

ijacsa

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In this research, a person identification system has been simulated using electrocardiogram (ECG) signals as biometrics. Ten adult people were participated as the subjects in this research taken from their signal ECG using the one-lead ECG machine. A total of 65 raw ECG waves from the 10 subjects were analyzed. This raw signal is then processed using the Hjorth Descriptor and Sample Entropy (SampEn) to get the signal features. Support Vector Machine (SVM) algorithm was used as the classifier for the subject authentication based upon the record of ECG signal. The results of the research showed that the highest accuracy value of 93.8% was found in Hjorth Descriptor. Compared to SampEn, this method is quite promising to be implemented for having a good performance and fewer features.

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Section: Introductionmentioning

confidence: 99%

One-Lead Electrocardiogram for Biometric Authentication using Time Series Analysis and Support Vector Machine

Hadiyoso¹,

Aulia²,

Rizal³

2019

ijacsa

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show abstract

“…(8) (7) Here, the vector to the hyperplane is denoted as . If the data can be separated linearly, the hyperplane is expressed in Equation 7and Equation 8.…”

Section: Linear Support Vector Machine (Svm)mentioning

confidence: 99%

“…Many previous studies propose various methods of feature extraction, including analysis in the time domain, frequency, time-frequency, and wavelet. Wavelet-based analysis methods on ECG biometric simulations have been proposed in research [6][7][8][9][10][11]. Another proposed method for feature extraction on ECG biometrics applications is the frequency-based approach.…”

Section: Introductionmentioning

confidence: 99%

Biometric systems based on ECG using ensemble empirical mode decomposition and Variational Mode decomposition

Hadiyoso¹,

Wijayanto²,

Rizal³

et al. 2020

J Appl Eng Science

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Electrocardiogram (ECG) based biometric is challenging to be developed with the aim of high-security access. This biometric system is more diffi cult to falsify, compared to the conventional biometric systems. From previous proposed studies, there is still a gap to improve the accuracy of the system. Therefore in this study, a new protocol is proposed to improve the performance of the ECG biometric system compared to previously reported studies. This study decomposes the ECG signals using a method based on empirical mode decomposition (EMD) based, which are Variational Mode Decomposition (VMD) and Ensemble Empirical Mode Decomposition (EEMD). These two methods are the development of the EMD method to overcome one main problem of EMD. That is, the EMD method generates oscillations with the same time scales, which stored in different decomposition levels. A private ECG dataset, recorded using one lead ECG signal from 11 subjects, is used in this study. ECG signals from each person are then segmented into ten windows to become training data and test data. VMD and EEMD methods are used to decompose ECG signals into fi ve sub-signals. Feature extraction based on statistical calculations is applied at each level of decomposition to obtain the characteristics of the ECG signal. Mean, variance, skewness, kurtosis, and entropy are evaluated as predictors. Support vector machines and 10-fold cross-validation are used to validate the performance of the proposed method. Our simulations demonstrate that the proposed method outperforms several previous studies and achieves an accuracy of up to 98.2%.

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“…Some research has been done in the past to classify cardiac disease based on the ECG signal. Some of them were processed to time-domain feature extraction [8]- [13].…”

Section: Introductionmentioning

confidence: 99%

Multi-Distance Dispersion Entropy for ECG Signal Classification

et al. 2022

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Automatic detection of heartbeat is critical for early cardiovascular disease prevention and diagnosis. Traditional feature methodologies based on expert knowledge cannot abstract and represent multidimensional and multi-view information. Hence traditional research on heartbeat detection pattern recognition cannot produce adequate results. The proposed method in this research used Dispersion Entropy (DisEn) on Multidistance Signal Level Difference (MSLD) for feature extraction and Support Vector Machine (SVM) method for classifying the ECG signals. DisEn generates 20 DE values as feature vectors for each MSLD signal with a distance D of 1 to 20. The datasets used in this research were obtained from the MIT-BIH Arrhythmia database of ECG signals that consist of forty-five patients was captured using 200 [adu/mV] amplification and a sampling frequency of 360 Hz. The experiments result using 5-fold cross-validation revealed that at distance D= 1-15 had the highest accuracy of 91% to classify the ECG data into Normal Sinus Rhythm (NSR), Left Bundle Branch Block (LBBB), and Atrial Fibrillation (AFIB) from the MIT-BIH Arrhythmias database.

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ECG Identification Based on Wavelet Transform

Cited by 6 publications

References 1 publication

One-Lead Electrocardiogram for Biometric Authentication using Time Series Analysis and Support Vector Machine

One-Lead Electrocardiogram for Biometric Authentication using Time Series Analysis and Support Vector Machine

Biometric systems based on ECG using ensemble empirical mode decomposition and Variational Mode decomposition

Multi-Distance Dispersion Entropy for ECG Signal Classification

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