ArticleSensors (Basel, Switzerland)2019
ECG Signal as Robust and Reliable Biometric Marker: Datasets and Algorithms Comparison.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A PRISMA-based systematic review on advances in identity recognition and authentication using human biometric signals (2018-2023).Biomedical engineering online · 2026Pooled it
- A Novel Instruction Driven 1-D CNN Processor for ECG Classification.Sensors (Basel, Switzerland) · 2024Article
- Electrocardiogram-Based Biometric Identification Using Mixed Feature Extraction and Sparse Representation.Sensors (Basel, Switzerland) · 2023Article
- Initial Study Using Electrocardiogram for Authentication and Identification.Sensors (Basel, Switzerland) · 2022Article
- The Identification of ECG Signals Using WT-UKF and IPSO-SVM.Sensors (Basel, Switzerland) · 2022Article
- A Flexible, Wearable, and Wireless Biosensor Patch with Internet of Medical Things Applications.Biosensors · 2022Article
- Compressed-Domain ECG-Based Biometric User Identification Using Compressive Analysis.Sensors (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this paper, the possibility of using the ECG signal as an unequivocal biometric marker for authentication and identification purposes has been presented. Furthermore, since the ECG signal was acquired from 4 sources using different measurement equipment, electrodes positioning and number of patients as well as the duration of the ECG record acquisition, we have additionally provided an estimation of the extent of information available in the ECG record. To provide a more objective assessment of the credibility of the identification method, some selected machine learning algorithms were used in two combinations: with and without compression. The results that we have obtained confirm that the ECG signal can be acclaimed as a valid biometric marker that is very robust to hardware variations, noise and artifacts presence, that is stable over time and that is scalable across quite a solid (~100) number of users. Our experiments indicate that the most promising algorithms for ECG identification are LDA, KNN and MLP algorithms. Moreover, our results show that PCA compression, used as part of data preprocessing, does not only bring any noticeable benefits but in some cases might even reduce accuracy.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.