A New Statistical-based Algorithm for ECG Identification

Zeng, Fufu; Tseng, Kuo-Kun; Huang, Huang; Tu, Shu-Yi; Pan, Jeng‐Shyang

doi:10.1109/iih-msp.2012.79

Cited by 14 publications

(4 citation statements)

References 17 publications

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“…Taking MIT-BIH ECG database as examples, Fatemian et al [ 44 ] used the template matching approach for ECG subject identification, and reported an average of 99.61% verification accuracy when evaluated with MIT-BIH normal sinus rhythm database; Zokaee et al [ 45 ] proposed using Mel-Frequency Cepstrum Coefficients (MFCC) for ECG feature extraction and k-Nearest Neighbors (KNN) for classification, and achieved 100% identification accuracy for MIT-BIH normal sinus rhythm database and 89% for ECG data gathered from 50 subjects in a hospital with three records of different times. Sasikala et al [ 46 ] tested the feature extraction approach on ECG verification performance using the MIT-BIH arrhythmia database and achieved 99.0% accuracy; Zeng et al [ 47 ] implemented the reduced binary pattern template matching on MIT-BIH arrhythmia and normal sinus rhythm databases and achieved a success rate of 95.79% and 90.19% separately. As a comparison, our proposed two-stage classification algorithm achieved a 99.43% accuracy for the MIT-BIH arrhythmia database and 99.98% for the MIT-BIH normal sinus rhythm database.…”

Section: Resultsmentioning

confidence: 99%

Toward Improving Electrocardiogram (ECG) Biometric Verification using Mobile Sensors: A Two-Stage Classifier Approach

Tan

Perkowski

2017

Sensors

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Electrocardiogram (ECG) signals sensed from mobile devices pertain the potential for biometric identity recognition applicable in remote access control systems where enhanced data security is demanding. In this study, we propose a new algorithm that consists of a two-stage classifier combining random forest and wavelet distance measure through a probabilistic threshold schema, to improve the effectiveness and robustness of a biometric recognition system using ECG data acquired from a biosensor integrated into mobile devices. The proposed algorithm is evaluated using a mixed dataset from 184 subjects under different health conditions. The proposed two-stage classifier achieves a total of 99.52% subject verification accuracy, better than the 98.33% accuracy from random forest alone and 96.31% accuracy from wavelet distance measure algorithm alone. These results demonstrate the superiority of the proposed algorithm for biometric identification, hence supporting its practicality in areas such as cloud data security, cyber-security or remote healthcare systems.

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Section: Resultsmentioning

confidence: 99%

Toward Improving Electrocardiogram (ECG) Biometric Verification using Mobile Sensors: A Two-Stage Classifier Approach

Tan

Perkowski

2017

Sensors

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“…Zeng et al [14] use a statistical-based algorithm called the reduced binary pattern (RBP). This algorithm converts signals into concise binary patterns and performs statistical counting and ranking for identification.…”

Section: Literature Of Reviewmentioning

confidence: 99%

Sum of Sine Modeling Approach as a New Processing Technique For a Biometric System Based on ECG Signal

2020

International Journal of Biology and Biomedical Engineering

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Biometrics is considered in current research as one of the best methods for authenticating human beings. In our paper, the heartbeat biometric, also called Electrocardiographic (ECG), is working on. This biometric is chosen because human ECGs cannot be falsely created and replicated. This study aims to find the best features from this biometric that can identify a person, given the extractions and classification algorithms for the heartbeat biometric signal. Depending on a literature study we work to propose a new and more efficient technique based on a new method for ECG features extraction and these features will be the inputs for pattern recognition classifier. This methodology will be tested on real experimental ECG data that is collected. The Data collected from 10 subjects by a commercial ECG device taking the data from lead 1. The pre-processing steps start with the Empirical Mode Decomposition (EMD) before digital filters which are: low pass, high pass, and derivative pass filters. Features extraction steps are peak detection, segmentation, and wave modeling for each segment. The classification used the Multi-Layer Perceptron and compared it to classification using Radial Basis Function were the results of MLP were much better for these applications since the accuracy of the final results of MLP is 99% and that related to the RBF is 95%.

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“…Pattern. This algorithm uses the frequency and rank order statistics of the underlying pattern of the input ECG data [30]. Such data exists in serial = { 1 , 2 , 3 , .…”

Section: Reduced Binarymentioning

confidence: 99%

ECG Sensor Verification System with Mean-Interval Algorithm for Handling Sport Issue

Tseng

Zeng

et al. 2016

Journal of Sensors

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With the development of biometric verification, we proposed a new algorithm and personal mobile sensor card system for ECG verification. The proposed new mean-interval approach can identify the user quickly with high accuracy and consumes a small amount of flash memory in the microprocessor. The new framework of the mobile card system makes ECG verification become a feasible application to overcome the issues of a centralized database. For a fair and comprehensive evaluation, the experimental results have been tested on public MIT-BIH ECG databases and our circuit system; they confirm that the proposed scheme is able to provide excellent accuracy and low complexity. Moreover, we also proposed a multiple-state solution to handle the heat rate changes of sports problem. It should be the first to address the issue of sports in ECG verification.

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A New Statistical-based Algorithm for ECG Identification

Cited by 14 publications

References 17 publications

Toward Improving Electrocardiogram (ECG) Biometric Verification using Mobile Sensors: A Two-Stage Classifier Approach

Toward Improving Electrocardiogram (ECG) Biometric Verification using Mobile Sensors: A Two-Stage Classifier Approach

Sum of Sine Modeling Approach as a New Processing Technique For a Biometric System Based on ECG Signal

ECG Sensor Verification System with Mean-Interval Algorithm for Handling Sport Issue

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