Development of Heartbeat Based Biometric System Using Wavelet Transform

Chin, Chee Yeen; Ramli, Dzati Athiar

doi:10.21315/jes2018.14.2

Cited by 5 publications

(3 citation statements)

References 12 publications

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“…The research of Wei-Quan, however, did not give any reports about the authentication or classification method used. Wavelet transformations as an ECG biometric base have also been reported in the research of Chee Yeen [12] with a focus to study the effects of various features used for the performance or accuracy of authentication. Chee Yeen intended to obtain a dominant feature producing the best performance in authentication.…”

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

“…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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“…applied to new biometric systems. Among biosignal modalities, electrocardiogram (ECG) waves are one of the new biometric methods that are starting to be proposed [1].…”

mentioning

confidence: 99%

ECG Based Biometric using Wavelet Packet Decomposition

Hadiyoso,

Rizal,

Wijayanto

2019

IJEAT

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Biometric technology has been commonly used for authentication. Fingerprint or iris become one of the biometrics that is widely applied. However, this type of biometrics tends to be easily falsified and damaged. So it is misused for manipulating actions and even crime. Therefore a new biometric method is needed to overcome this problem. One potential modality is biometrics based on an electrocardiogram (ECG) signal. This research simulates a one-lead ECG waveform for person authentication. ECG waves were taken from eleven healthy adult volunteers with a length of 60 seconds. ECG waves from each person are segmented into 10 sections so that a total of 110 ECG waves are used for person authentication simulations. All noise of the ECG waves was removed using a bandpass filter to reduce artifacts and high-frequency noise. Wavelet packet decomposition (3 Level) was applied to decompose the signal in several intrinsic parts so that typical wave information can be retrieved. Entropy-based feature extraction applied to all decomposed signals. A total of 14 entropy features have been calculated and used as predictors in the classification process. Validation and performance tests are carried out by cross-validation combined with linear discriminant analysis and support vector machines with five scenarios. The proposed method provides the highest accuracy of 71.8% using discriminant analysis and cubic support vector machine. The best accuracy value was achieved if all entropy features from all wavelet decomposition levels are used as predictors in the classification process. This research is expected to be a reference that ECG has the potential to become a future biometric modality

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Development of Heartbeat Based Biometric System Using Wavelet Transform

Cited by 5 publications

References 12 publications

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

ECG Based Biometric using Wavelet Packet Decomposition

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