Heartbeat biometrics: a sensing system perspective

Israel, Steven A.; Irvine, John

doi:10.1504/ijcb.2012.046514

Cited by 15 publications

(10 citation statements)

References 113 publications

(104 reference statements)

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“…Previous attempts to summarise ECG-based recognition techniques can be traced back to the work of Nasri [ 18 ], Odinaka [ 17 ] and Israel and Irvine [ 19 ].…”

Section: Introductionmentioning

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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“…Previous attempts to summarise ECG-based recognition techniques can be traced back to the work of Nasri [ 18 ], Odinaka [ 17 ] and Israel and Irvine [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

Individual identification via electrocardiogram analysis

et al. 2015

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“…Previous literature reviews of some of the methods that have been applied to ECG based identification can be found in the reports of Nasri et al [28], Sufi et al [29], Chauhan et al [30], and Israel and Irvine [31].…”

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confidence: 99%

ECG Biometric Recognition: A Comparative Analysis

Odinaka

Lai

Kaplan

et al. 2012

IEEE Trans.Inform.Forensic Secur.

300

194

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The electrocardiogram (ECG) is an emerging biometric modality that has seen about thirteen years of development, in peer-reviewed literature, and as such deserves a systematic review and discussion of the associated methods and findings. In this paper, we review most of the techniques that have been applied to the use of the electrocardiogram for biometric recognition. In particular, we categorize the methodologies based on the features and the classification schemes. Finally, a comparative analysis of the authentication performance of a few of the ECG biometric systems is presented, using our in-house database. The comparative study includes the cases where training and testing data come from the same and different sessions (days). The authentication results show that most of the algorithms that have been proposed for ECG based biometrics perform well, when the training and testing data come from the same session. However, when training and testing data come from different sessions, a performance degradation occurs. Multiple training sessions were incorporated to diminish the loss in performance. That notwithstanding, only a few of the proposed ECG recognition algorithms appear to be able to support performance improvement due to multiple training sessions. Only three of these algorithms produced This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. 2 equal error rates (EERs) in the single digits, including an EER of 5.5% using a method proposed by us.

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“…During the learning of auscultation, students make use of tables and graphs like the one shown here [17]. These graphs are used to give a visual representation of the sounds that one is expected to hear during auscultation.…”

Section: Figmentioning

confidence: 99%

Virtual Reality Simulator for Medical Auscultation Training

BAlint

Macías

Althoefer

2019

Towards Autonomous Robotic Systems

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According to the Oxford English dictionary, auscultation is "the action of listening to sounds from the heart, lungs, or other organs, typically with a stethoscope, as a part of medical diagnosis." In this work, we describe a medical simulator that includes audio, visual, pseudo-haptic, and spatial elements for training medical students in auscultation. In our training simulator, the user is fully immersed in a virtual reality (VR) environment. A typical hospital bedside scenario was recreated, and the users can see their own body and the patient to increase immersion. External tracking devices are used to acquire the user's movements and map them into the VR environment. The main idea behind this work is for the user to associate the heart and lung sounds, as heard through the stethoscope with the corresponding health-related problems. Several sound parameters including the volume, give information about the type and severity of the disease. Our simulator can reproduce sounds belonging to the heart and lungs. Through the proposed VR-based training, the medical student ideally will learn to relate sounds to illnesses in a realistic setting, accelerating the learning process.

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Heartbeat biometrics: a sensing system perspective

Cited by 15 publications

References 113 publications

Individual identification via electrocardiogram analysis

Individual identification via electrocardiogram analysis

ECG Biometric Recognition: A Comparative Analysis

Virtual Reality Simulator for Medical Auscultation Training

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