Subject identification via ECG fiducial-based systems: Influence of the type of QT interval correction

Gargiulo, Francesco; Fratini, Antonio; Sansone, Mario

doi:10.1016/j.cmpb.2015.05.012

Cited by 31 publications

(30 citation statements)

References 32 publications

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“…Based on the promising results presented in the paper, we envision that hubness-aware techniques will be used in further biomedical recognition tasks such as ECG-based person identification [7], diagnosis of schizophrenia [4] or link prediction in biomedical networks [27]. In order to accelerate this process,…”

Section: Discussionmentioning

confidence: 99%

Classification of gene expression data: A hubness-aware semi-supervised approach

Búza

2016

Computer Methods and Programs in Biomedicine

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Background and Objective Classification of gene expression data is the common denominator of various biomedical recognition tasks. However, obtaining class labels for large training samples may be difficult or even impossible in many cases. Therefore, semi-supervised classification techniques are required as semi-supervised classifiers take advantage of unlabeled data.Methods Gene expression data is high-dimensional which gives rise to the phenomena known under the umbrella of the curse of dimensionality, one of its recently explored aspects being the presence of hubs or hubness for short. Therefore, hubness-aware classifiers have been developed recently, such as Naive Hubness-Bayesian k-Nearest Neighbor (NHBNN).In this paper, we propose a semi-supervised extension of NHBNN which follows the self-training schema. As one of the core components of selftraining is the certainty score, we propose a new hubness-aware certainty score. ResultsWe performed experiments on publicly available gene expression data. These experiments show that the proposed classifier outperforms its competitors. We investigated the impact of each of the compo-1 nents (classification algorithm, semi-supervised technique, hubness-aware certainty score) separately and showed that each of these components are relevant to the performance of the proposed approach. ConclusionsOur results imply that our approach may increase classification accuracy and reduce computational costs (i.e., runtime). Based on the promising results presented in the paper, we envision that hubnessaware techniques will be used in various other biomedical machine learning tasks. In order to accelerate this process, we made an implementation of hubness-aware machine learning techniques publicly available in the PyHubs software package (http://www.biointelligence.hu/pyhubs) implemented in Python, one of the most popular programming languages of data science.

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Section: Discussionmentioning

confidence: 99%

Classification of gene expression data: A hubness-aware semi-supervised approach

Búza

2016

Computer Methods and Programs in Biomedicine

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“…Activation of the sympathetic system results in the reduction of the interbeat interval (temporal distance between consecutive R points) variability, and a width reduction in the P and T complexes due to an increase in conductivity [40]. Indeed, the time intervals among the fiducial points change with varying heart rate [40, 75]. However, the differences in the ECG morphology induced by different physiological conditions (e.g., exhaustion, stress, relaxation, and anxiety), would not compromise the performance accuracy of ECG-based biometric systems, if some kind of normalization regarding the heart rate was to be applied to the temporal distances between fiducial points [35, 36, 40, 69, 70, 75, 76].…”

Section: Introductionmentioning

confidence: 99%

Beat-ID: Towards a computationally low-cost single heartbeat biometric identity check system based on electrocardiogram wave morphology

2017

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In recent years, safer and more reliable biometric methods have been developed. Apart from the need for enhanced security, the media and entertainment sectors have also been applying biometrics in the emerging market of user-adaptable objects/systems to make these systems more user-friendly. However, the complexity of some state-of-the-art biometric systems (e.g., iris recognition) or their high false rejection rate (e.g., fingerprint recognition) is neither compatible with the simple hardware architecture required by reduced-size devices nor the new trend of implementing smart objects within the dynamic market of the Internet of Things (IoT). It was recently shown that an individual can be recognized by extracting features from their electrocardiogram (ECG). However, most current ECG-based biometric algorithms are computationally demanding and/or rely on relatively large (several seconds) ECG samples, which are incompatible with the aforementioned application fields. Here, we present a computationally low-cost method (patent pending), including simple mathematical operations, for identifying a person using only three ECG morphology-based characteristics from a single heartbeat. The algorithm was trained/tested using ECG signals of different duration from the Physionet database on more than 60 different training/test datasets. The proposed method achieved maximal averaged accuracy of 97.450% in distinguishing each subject from a ten-subject set and false acceptance and rejection rates (FAR and FRR) of 5.710±1.900% and 3.440±1.980%, respectively, placing Beat-ID in a very competitive position in terms of the FRR/FAR among state-of-the-art methods. Furthermore, the proposed method can identify a person using an average of 1.020 heartbeats. It therefore has FRR/FAR behavior similar to obtaining a fingerprint, yet it is simpler and requires less expensive hardware. This method targets low-computational/energy-cost scenarios, such as tiny wearable devices (e.g., a smart object that automatically adapts its configuration to the user). A hardware proof-of-concept implementation is presented as an annex to this paper.

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“…These n decisions can be implemented in parallel if the number of users is high and several computational units are available. 2 At each of the above decisions, a simple decision threshold of 0.5 could be applied. It means that given a model trained to distinguish between the typing patterns of users u and v, if the model outputs less than 0.5 when a new time series is presented to the model, then the decision is u; otherwise the decision is v. However, the simple threshold of 0.5 may be suboptimal.…”

Section: Nearest Neighbour Regression With Error Correctionmentioning

confidence: 99%

A New Proposal for Person Identification Based on the Dynamics of Typing: Preliminary Results

Búza

Neubrandt

2017

Theor. Appl. Inform.

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The availability of cheap and widely applicable person identification techniques is essential due to a wide-spread usage of online services. The dynamics of typing is characteristic to particular users, and users are hardly able to mimic the dynamics of typing of others. State-of-the-art solutions for person identification from the dynamics of typing are based on machine learning. The presence of hubs, i.e., few instances that appear as nearest neighbours of surprisingly many other instances, have been observed in various domains recently and hubness-aware machine learning approaches have been shown to work well in those domains. However, hubness has not been studied in the context of person identification yet, and hubnessaware techniques have not been applied to this task. In this paper, we examine hubness in typing data and propose to use ECkNN, a recent hubness-aware regression technique together with dynamic time warping for person identification. We collected time-series data describing the dynamics of typing and used it to evaluate our approach. Experimental results show that hubness-aware techniques outperform state-of-the-art time-series classifiers.

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Subject identification via ECG fiducial-based systems: Influence of the type of QT interval correction

Cited by 31 publications

References 32 publications

Classification of gene expression data: A hubness-aware semi-supervised approach

Classification of gene expression data: A hubness-aware semi-supervised approach

Beat-ID: Towards a computationally low-cost single heartbeat biometric identity check system based on electrocardiogram wave morphology

A New Proposal for Person Identification Based on the Dynamics of Typing: Preliminary Results

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