Temporal and spectral features of single lead ECG for human identification

Kaveh, A; Chung, Wayne

doi:10.1109/bioms.2013.6656143

Cited by 13 publications

(4 citation statements)

References 21 publications

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“…[21], morphological features [22], autocorrelation (AC) [23], and heart rate variability feature values [24] are used. The features in the frequency domain are extracted using discrete wavelet transform (DWT) [25] and wavelet transform [3,18], Mel frequency cepstrum coefficients (MFCC) [17], ensemble empirical mode decomposition (EEMD) [26]. Features in the phase space domain have not been studied in Korea and are extracted using Lyapunov exponent (LE), the correlation dimension (CD), root mean square (RMS) by Lorentz attraction [20] in abroad.…”

Section: Ecg-based Biometrics Technologymentioning

confidence: 99%

“…Various studies are being conducted on biometrics technology using bio-signals. Biometrics technology using the ECG includes individual authentication, identification, and disease recognition [3,4,5 biometrics technology using heart sound is individual authentication and identification [6,7], and biometrics technology using EEG is individual authentication, situation recognition [8,9], and biometrics technology using EMG is individual authentication and situation recognition technologies [10,11].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Biometrics System Technology Trends Based on Bio-signal

Choi¹,

Pan²

2021

ReBICTE

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Biometrics is a technology that authenticates, identifies, and recognizes user using individual uniquephysical or behavioral characteristics. The scope of services is expanding with necessity and utility ina wide range of fields such as finance, security, access control, medical welfare, public service, quarantine,and entertainment. Research using bio-signal inside the body than bio-information outsidethe body is being actively conducted. In this paper, we analyze research about technologies of biometricssystems using bio-signals such as ECG, heart sound, EMG, EEG, and present the necessarytechnologies for the development direction. In the future, bio-signal based database construction incomplex conditions, deep learning network design through analyzes big data, and biometrics systemtechnologies applied in a real-time environment are expected to be studied.

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Section: Ecg-based Biometrics Technologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Biometrics System Technology Trends Based on Bio-signal

Choi¹,

Pan²

2021

ReBICTE

View full text Add to dashboard Cite

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“…Detection of such characteristic points is however a challenging process due to noise. Therefore, following [33], we do not consider fiducial dependent approaches [25], [34]- [43]. The latter category considers ECG signal as a set of heartbeats or just a time series without segmenting it to heartbeats [9]- [21].…”

Section: A Electrocardiogrammentioning

confidence: 99%

Feature Selection for Nonstationary Data: Application to Human Recognition Using Medical Biometrics

Komeili

Louis

Armanfard

et al. 2018

IEEE Trans. Cybern.

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Electrocardiogram (ECG) and transient evoked otoacoustic emission (TEOAE) are among the physiological signals that have attracted significant interest in biometric community due to their inherent robustness to replay and falsification attacks. However, they are time-dependent signals and this makes them hard to deal with in across-session human recognition scenario where only one session is available for enrollment. This paper presents a novel feature selection method to address this issue. It is based on an auxiliary dataset with multiple sessions where it selects a subset of features that are more persistent across different sessions. It uses local information in terms of sample margins while enforcing an across-session measure. This makes it a perfect fit for aforementioned biometric recognition problem. Comprehensive experiments on ECG and TEOAE variability due to time lapse and body posture are done. Performance of the proposed method is compared against seven state-of-the-art feature selection algorithms as well as another six approaches in the area of ECG and TEOAE biometric recognition. Experimental results demonstrate that the proposed method performs noticeably better than other algorithms.

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“…However, a deep computational analysis may be used to detect small differences and possible diseases. To allow for such automatic detection, several features may be extracted from ECG signals such as, heart rate variability (HRV) triangular index [7], morphological features [8] through the temporal-domain analysis [7,9] and frequency-domain [1,7,10], and wavelet transform coefficients [11,12,13,14]. Furthermore, automatic methods to correctively identify diseases or patterns from these signals may be reached through statistical Markov models [15], artificial neural networks (ANN) [1,3,6,16], linear discriminant analysis [17], and support vector machine (SVM) [18].…”

Section: Introductionmentioning

confidence: 99%

Design of an artificial neural network and feature extraction to identify arrhythmias from ECG

Roza

Almeida

Postolache

2017

2017 IEEE International Symposium on Medical Measurements and Applications (MeMeA)

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This paper presents a design of an artificial neural network (ANN) and feature extraction methods to identify two types of arrhythmias in datasets obtained through electrocardiography (ECG) signals, namely arrhythmia dataset (AD) and supraventricular arrhythmia dataset (SAD). No special ANN toolkit was used; instead, each neuron and necessary calculus were modeled and individually programmed. Thus, four temporal-based features are used: heart rate (HR), R-peaks root mean square (R-RMS), RR-peaks variance (RR-VAR), and QSRcomplex standard deviation (QSR-SD). The network architecture presents four neurons in the input layer, eight in hidden layer and an output layer with two neurons. The proposed classification method uses the MIT-BIH Dataset (Massachusetts Institute of Technology-Beth Israel Hospital) for training, validation and execution or test phases. Preliminary results show the high efficiency of the proposed ANN design and its classification method, reaching accuracies between 98.76% and 98.91%, when in the identification of NSRD and arrhythmic ECG; and accuracies of 86.37% (AD) and 76.35% (SAD), when analyzing only classifications between both arrhythmias.

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Temporal and spectral features of single lead ECG for human identification

Cited by 13 publications

References 21 publications

Biometrics System Technology Trends Based on Bio-signal

Biometrics System Technology Trends Based on Bio-signal

Feature Selection for Nonstationary Data: Application to Human Recognition Using Medical Biometrics

Design of an artificial neural network and feature extraction to identify arrhythmias from ECG

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