ECG analysis for human recognition using non‐fiducial methods

Srivastva, Ranjeet; Singh, Yatindra Nath

doi:10.1049/iet-bmt.2018.5093

Cited by 21 publications

(9 citation statements)

References 39 publications

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“…The comparison with other studies in the literature is not feasible since the selection of heartbeats, patients, or the classes to discriminate differs from one paper to another, in many of them, the choice seems to be made ad hoc. As far as we know, this is the first time that this milestone has been achieved for the QT database, since other authors consider specifically selected sets of patients of a much smaller size (see 27,32 and references therein). Moreover, a complete analysis is provided in the Supplementary Information including, specific-patient plots and statistics for the main FMM ecg parameters.…”

Section: Resultsmentioning

confidence: 93%

“…Moreover, the new parameters can be used in experimental assays to test medical and preventive strategies, to study the evolution of the heart's functioning and could even allow inferring personal identity, as well as www.nature.com/scientificreports/ circumstances as the emotional state at the time of data collection. The important question of biometric identification using ECG features has received attention recently in the literature (see 17,25,26,32,37,38 among others). The reduced error rate when the 105 QT patients are discriminated is a piece of evidence of the potential of FMM ecg parameters in biometric identification.…”

Section: Discussionmentioning

confidence: 99%

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The hidden waves in the ECG uncovered revealing a sound automated interpretation method

Rueda

Larriba

Lamela

2021

Sci Rep

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A novel approach for analysing cardiac rhythm data is presented in this paper. Heartbeats are decomposed into the five fundamental P, Q, R, S and T waves plus an error term to account for artifacts in the data which provides a meaningful, physical interpretation of the heart’s electric system. The morphology of each wave is concisely described using four parameters that allow all the different patterns in heartbeats to be characterized and thus differentiated This multi-purpose approach solves such questions as the extraction of interpretable features, the detection of the fiducial marks of the fundamental waves, or the generation of synthetic data and the denoising of signals. Yet the greatest benefit from this new discovery will be the automatic diagnosis of heart anomalies as well as other clinical uses with great advantages compared to the rigid, vulnerable and black box machine learning procedures, widely used in medical devices. The paper shows the enormous potential of the method in practice; specifically, the capability to discriminate subjects, characterize morphologies and detect the fiducial marks (reference points) are validated numerically using simulated and real data, thus proving that it outperforms its competitors.

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Section: Resultsmentioning

confidence: 93%

Section: Discussionmentioning

confidence: 99%

The hidden waves in the ECG uncovered revealing a sound automated interpretation method

Rueda

Larriba

Lamela

2021

Sci Rep

View full text Add to dashboard Cite

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“…This error rate is very low taking into account the difficult task of discrimination among the 105 patients. As far as we know, this is the first time that this milestone has been achieved for the QT database, since other authors consider specifically selected sets of patients of a much smaller size ( [31] and references therein). Moreover, a complete analysis is provided in the Supplementary Information, including specific-patient plots and statistics for the main F M M ecg parameters, see Figures S4-S10 and Table S4, respectively.…”

Section: Analysis Of Qt Database Signals Graphical and Analytical Res...mentioning

confidence: 93%

The hidden waves in the ECG uncovered: a sound automated interpretation method

Rueda,

Larriba,

Lamela

2020

Preprint

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A novel approach for analysing cardiac rhythm data is presented in this paper. Heartbeats are decomposed into the five fundamental P , Q, R, S and T waves plus an error term to account for artefacts in the data which provides a meaningful, physical interpretation of the heart's electric system. The morphology of each wave is concisely described using four parameters that allow to all the different patterns in heartbeats be characterized and thus differentiated This multi-purpose approach solves such questions as the extraction of interpretable features, the detection of the fiducial marks of the fundamental waves, or the generation of synthetic data and the denoising of signals. Yet, the greatest benefit from this new discovery will be the automatic diagnosis of heart anomalies as well as other clinical uses with great advantages compared to the rigid, vulnerable and black box machine learning procedures, widely used in medical devices.The paper shows the enormous potential of the method in practice; specifically, the capability to discriminate subjects, characterize morphologies and detect the fiducial marks (reference points) are validated numerically using simulated and real data, thus proving that it outperforms its competitors.

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“…For instance, studies such as [1,18] have applied fiducial feature extraction in their identification algorithms. Many other studies [19,20], on the other hand, have used non-fiducial feature extraction techniques. Other studies, such as [21,22], have combined the benefits of fiducial and non-fiducial extraction to form a hybrid approach for ECG-based authentication.…”

Section: Man In the Middle Attackmentioning

confidence: 99%

An Intelligent Multimodal Biometric Authentication Model for Personalised Healthcare Services

Ahamed¹,

Farid²,

Suleiman³

et al. 2022

Preprint

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With the advent of modern technologies, the healthcare industry is moving towards a more Personalised smart care model. The enablers of such care models are the Internet of Things (IoT) and Artificial Intelligence. These technologies collect and analyse data from persons in care to alert relevant parties if any anomaly is detected in a patient’s regular pattern. However, such reliance on IoT devices to capture continuous data extends the attack surfaces and demands high-security measures. Both patients and devices need to be authenticated to mitigate a large number of attack vectors. The biometric authentication method has been seen as a promising technique in these scenarios. To this end, this paper proposes an AI-based multimodal biometric authentication model for single and group-based users’ device-level authentication that increases protection against the traditional single modal approach. To test the efficacy of the proposed model, a series of AI models are trained and tested using physiological biometric features such as ECG (Electrocardiogram) and PPG (Photoplethysmography) signals from five publicly available datasets from Physionet and Mendeley data repositories. The multimodal fusion authentication model shows promising results with 99.8% accuracy and an Equal Error Rate (EER) of 0.16.

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ECG analysis for human recognition using non‐fiducial methods

Cited by 21 publications

References 39 publications

The hidden waves in the ECG uncovered revealing a sound automated interpretation method

The hidden waves in the ECG uncovered revealing a sound automated interpretation method

The hidden waves in the ECG uncovered: a sound automated interpretation method

An Intelligent Multimodal Biometric Authentication Model for Personalised Healthcare Services

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