A Novel Heart Rate Robust Method for Short-Term Electrocardiogram Biometric Identification

Wang, Di; Si, Yujuan; Yang, Wei; Gong, Zhang; Liu, Tong

doi:10.3390/app9010201

Cited by 31 publications

(20 citation statements)

References 49 publications

(83 reference statements)

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“…However, if the signal is short-term, the variations are significantly reduced, hence making it difficult to differentiate among individuals. To tackle this issue, a research study proposed a new technique that can distinguish among short-term ECG signals [110]. This technique first removes the noise from the QRS-centred signal and uses a deep learning procedure, known as Principal Component Analysis Network (PCANet), to extract the features.…”

Section: Heart-based Biometricsmentioning

confidence: 99%

Biometric Systems Utilising Health Data from Wearable Devices

et al. 2020

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Health data is being increasingly sensed from the health-based wearable Internet of Things (IoT) devices, providing much-needed fitness and health tracking. However, data generated also presents opportunities within computer security, specifically w ith b iometric s ystems u sed f or i dentification an d authentication purposes. This paper performs a systematic review of health-based IoT data collected from wearable IoT technology. This involved performing research in the underlying data sources, what they are collected for in terms of their health monitoring, and the underlying data characteristics. Furthermore, it explores existing work in computer security using these data sources, identifying key themes of work, key limitations and challenges. Finally, key opportunities are provided as summaries to the potential of health-based IoT data, highlighting challenges that are yet to be addressed, which motivate areas of future work. CCS Concepts: • Security and privacy → Biometrics.

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Section: Heart-based Biometricsmentioning

confidence: 99%

Biometric Systems Utilising Health Data from Wearable Devices

et al. 2020

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“…DL has been suggested to likely achieve a more effective analysis of ECG signals because its proven significant and remarkable improvements in robustness to noise and variability in several pattern recognition applications help real-time classification of very complex signals, though this scenario also raises new challenges, like regarding data availability [53,179]. Apart from health monitoring and medical diagnosis, the use of ECG as a biometric trait for identification or authentication has gained momentum [180]. The ECG, compared with other biometric traits, has proven to be one of the most promising among them, and researchers have recently also started to use DL methodologies in this area, which is today a pioneering affair [53].…”

Section: Applications In Ecg Processingmentioning

confidence: 99%

Deep Learning and Big Data in Healthcare: A Double Review for Critical Beginners

et al. 2019

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In the last few years, there has been a growing expectation created about the analysis of large amounts of data often available in organizations, which has been both scrutinized by the academic world and successfully exploited by industry. Nowadays, two of the most common terms heard in scientific circles are Big Data and Deep Learning. In this double review, we aim to shed some light on the current state of these different, yet somehow related branches of Data Science, in order to understand the current state and future evolution within the healthcare area. We start by giving a simple description of the technical elements of Big Data technologies, as well as an overview of the elements of Deep Learning techniques, according to their usual description in scientific literature. Then, we pay attention to the application fields that can be said to have delivered relevant real-world success stories, with emphasis on examples from large technology companies and financial institutions, among others. The academic effort that has been put into bringing these technologies to the healthcare sector are then summarized and analyzed from a twofold view as follows: first, the landscape of application examples is globally scrutinized according to the varying nature of medical data, including the data forms in electronic health recordings, medical time signals, and medical images; second, a specific application field is given special attention, in particular the electrocardiographic signal analysis, where a number of works have been published in the last two years. A set of toy application examples are provided with the publicly-available MIMIC dataset, aiming to help the beginners start with some principled, basic, and structured material and available code. Critical discussion is provided for current and forthcoming challenges on the use of both sets of techniques in our future healthcare.

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“…In addition to these sets of algorithms, PCA and ICA are widely used in several different applications in the field of ECG processing. Examples of this include the feature extraction for bio-metric characterization [16][17][18], as well as to dimensionality reduction and beat classification [19,20], or ECG compression [21,22], among many others [23]. A number of applications take into account the physiological meaning of the PCA/ICA components applying the whole ECG, such as AF detectors or respiratory signal isolators, or the decoupling of originally separated overlapping signals, as fetal ECG, but none of them considers the localized isolation of elements that are present at individual waves in the existing ECG, as fragmentation-revealing requires.…”

Section: Introductionmentioning

confidence: 99%

Electrocardiographic Fragmented Activity (I): Physiological Meaning of Multivariate Signal Decompositions

et al. 2019

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Recent research has proven the existence of statistical relation among fragmented QRS and several highly prevalence diseases, such as cardiac sarcoidosis, acute coronary syndrome, arrythmogenic cardiomyopathies, Brugada syndrome, and hypertrophic cardiomyopathy. One out of five hundred people suffer from hypertrophic cardiomyopathies. The relation among the fragmentation and arrhythmias drives the objective of this work, which is to propose a valid method for QRS fragmentation detection. With that aim, we followed a two-stage approach. First, we identified the features that better characterize the fragmentation by analyzing the physiological interpretation of multivariate approaches, such as principal component analysis (PCA) and independent component analysis (ICA). Second, we created an invariant transformation method for the multilead electrocardiogram (ECG), by scrutinizing the statistical distributions of the PCA eigenvectors and of the ICA transformation arrays, in order to anchor the desired elements in the suitable leads in the feature space. A complete database was compounded incorporating real fragmented ECGs, surrogate registers by synthetically adding fragmented activity to real non-fragmented ECG registers, and standard clean ECGs. Results showed that the creation of beat templates together with the application of PCA over eight independent leads achieves 0.995 fragmentation enhancement ratio and 0.07 dispersion coefficient. In the case of ICA over twelve leads, the results were 0.995 fragmentation enhancement ratio and 0.70 dispersion coefficient. We conclude that the algorithm presented in this work constructs a new paradigm, by creating a systematic and powerful tool for clinical anamnesis and evaluation based on multilead ECG. This approach consistently consolidates the inconspicuous elements present in multiple leads onto designated variables in the output space, hence offering additional and valid visual and non-visual information to standard clinical review, and opening the door to a more accurate automatic detection and statistically valid systematic approach for a wide number of applications. In this direction and within the companion paper, further developments are presented applying this technique to fragmentation detection.

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A Novel Heart Rate Robust Method for Short-Term Electrocardiogram Biometric Identification

Cited by 31 publications

References 49 publications

Biometric Systems Utilising Health Data from Wearable Devices

Biometric Systems Utilising Health Data from Wearable Devices

Deep Learning and Big Data in Healthcare: A Double Review for Critical Beginners

Electrocardiographic Fragmented Activity (I): Physiological Meaning of Multivariate Signal Decompositions

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