Individual identification with high frequency ECG : Preprocessing and classification by neural network

Tashiro, Futoshi; Aoyama, T.; Shimuta, Toru; Ishikawa, Hiroyasu; Shimatani, Yuichi; Ishijima, Masa; Kyoso, Masaki

doi:10.1109/iembs.2011.6090753

Cited by 10 publications

(12 citation statements)

References 5 publications

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“…Saechia and colleagues focussed on the discriminative characteristics of frequency content of P, QRS and T waves [ 71 ]. Hou used the only QRS frequency patterns [ 72 ] Tashiro the high frequency components (40–300 Hz) of the entire heartbeat [ 58 ]. Lately, Odinaka performed a short time Fourier transform to reveal the spectrogram shape over the heartbeat cycle [ 54 ].…”

Section: Ecg As a Biometricmentioning

confidence: 99%

Individual identification via electrocardiogram analysis

et al. 2015

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BackgroundDuring last decade the use of ECG recordings in biometric recognition studies has increased. ECG characteristics made it suitable for subject identification: it is unique, present in all living individuals, and hard to forge. However, in spite of the great number of approaches found in literature, no agreement exists on the most appropriate methodology. This study aimed at providing a survey of the techniques used so far in ECG-based human identification. Specifically, a pattern recognition perspective is here proposed providing a unifying framework to appreciate previous studies and, hopefully, guide future research.MethodsWe searched for papers on the subject from the earliest available date using relevant electronic databases (Medline, IEEEXplore, Scopus, and Web of Knowledge). The following terms were used in different combinations: electrocardiogram, ECG, human identification, biometric, authentication and individual variability. The electronic sources were last searched on 1st March 2015. In our selection we included published research on peer-reviewed journals, books chapters and conferences proceedings. The search was performed for English language documents.Results100 pertinent papers were found. Number of subjects involved in the journal studies ranges from 10 to 502, age from 16 to 86, male and female subjects are generally present. Number of analysed leads varies as well as the recording conditions. Identification performance differs widely as well as verification rate. Many studies refer to publicly available databases (Physionet ECG databases repository) while others rely on proprietary recordings making difficult them to compare. As a measure of overall accuracy we computed a weighted average of the identification rate and equal error rate in authentication scenarios. Identification rate resulted equal to 94.95 % while the equal error rate equal to 0.92 %.ConclusionsBiometric recognition is a mature field of research. Nevertheless, the use of physiological signals features, such as the ECG traits, needs further improvements. ECG features have the potential to be used in daily activities such as access control and patient handling as well as in wearable electronics applications. However, some barriers still limit its growth. Further analysis should be addressed on the use of single lead recordings and the study of features which are not dependent on the recording sites (e.g. fingers, hand palms). Moreover, it is expected that new techniques will be developed using fiducials and non-fiducial based features in order to catch the best of both approaches. ECG recognition in pathological subjects is also worth of additional investigations.

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Section: Ecg As a Biometricmentioning

confidence: 99%

Individual identification via electrocardiogram analysis

et al. 2015

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“…(2) Collectability, that is an ECG signal can be easily measured compared to other biological signals [6]. (3) Uniqueness, which implies that an ECG signal is unique (4) Permanence, which means it is permanent and finally (5) Performance, which is similar to biometric system, it is secure, efficient and accurate [7,8].…”

Section: Introductionmentioning

confidence: 99%

Electrocardiogram Identification: Use a Simple Set of Features in QRS Complex to Identify Individuals

TuerxunWaili

Nor

Rahman

et al. 2016

Recent Advances in Information and Communication Technology 2016

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This paper presents a Multilayer Perception Neural Network developed to identify human subjects using electrocardiogram (ECG) signals. We use the amplitude values of Q, R and S as a features for our experiments. In this study, a total of 87 dataset were collected among 14 subjects from the Physikalisch-Technische Bundesanstalt (PTB) database. Out of the 14 subjects, Q-R-S feature points were taken from different day and time sessions to perform classification with MLP. Out of this data, 66 % is used as training dataset while the remaining 34 % is used for testing. Our method yields 96 % accuracy and demonstrates that the use of three fiducial points is sufficient to identify a subject despite the common practice of taking more feature points.

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“…For feature extraction, there are two main feature extraction algorithms that can be used: fiducial-based [12,24,31,[35][36][37][38][39][40] and non-fiducial-based [41][42][43][44][45][46]. In the classification stage, researchers have utilized different classifiers such as k-nearest neighbors (k-NN) algorithm, neural network (NN), random forest, and Support Vector Machine (SVM) [30,31,33,47,48].…”

Section: Introductionmentioning

confidence: 99%

ECG-Based Subject Identification Using Common Spatial Pattern and SVM

et al. 2019

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In this paper, a nonfiducial electrocardiogram (ECG, the process of recording the electrical activity of the heart over a period of time using electrodes placed on the skin) identification system based on the common spatial pattern (CSP) feature extraction technique is presented. The single- and multilead ECG signals of each subject are divided into nonoverlapping segments, and different segment lengths (1, 3, 5, 7, 10, or 15 seconds) are investigated. Features are extracted from each signal segment through projection on a CSP projection matrix. The extracted features are then used to train a radial basis function kernel-based Support Vector Machine (SVM) classifier, which is then employed in the identification phase. The proposed identification system was evaluated on 10, 20, …, 200 reference subjects of the Physikalisch-Technische Bundesanstalt (PTB) ECG database. Using a single limb-based lead (I) with 200 reference subjects, the system achieved an identification rate of 95.15% and equal error rate of 0.1. The use of a single chest-based lead (V3) for 200 reference subjects resulted in an identification rate of 98.92% and equal error rate of 0.08.

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Individual identification with high frequency ECG : Preprocessing and classification by neural network

Cited by 10 publications

References 5 publications

Individual identification via electrocardiogram analysis

Individual identification via electrocardiogram analysis

Electrocardiogram Identification: Use a Simple Set of Features in QRS Complex to Identify Individuals

ECG-Based Subject Identification Using Common Spatial Pattern and SVM

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