ECG Identification Based on the Gramian Angular Field and Tested with Individuals in Resting and Activity States
Abstract
:1. Introduction and Related Work
2. Methods and Materials
2.1. Dataset and Preprocessing
2.2. Gramian Angular Field
2.3. Transfer Learning Network
3. Results
Analysis with Subjects under a Set of Conditions
4. Analysis and Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Acc. Training | Acc. Validation | Acc. Test |
---|---|---|
98% | 91% | 91% |
Proposal | Approach | Database | Accuracy | FRR | FAR |
---|---|---|---|---|---|
Choi et al. [31] | MLP (handcrafted; fiducial) | Proprietary | 93.8% | 0.085 | 0.085 |
Liu et al. [32] | RF (handcrafted; fiducial) | Proprietary | 93.1% | 0.046 | 0.010 |
Pinto et al. [12] | MLP (handcrafted; non-fiducial) | Proprietary | 92.4% | 0.033 | 0.033 |
Pathoumvanh et al. [33] | ED (handcrafted; non-fiducial) | Proprietary | 97.0 % | — | — |
Labati et al. [1] | CNN (handcrafted; fiducial) | E-HOL-03-0202-003 , PTB | 100% | 0.02 | 0.02 |
Abdeldayem et al. [2] | 2D-CNN (non-handcrafted; non-fiducial) | CEBSDB , Physionet-NSRDB FANTASIA and others | 95.6% | 0.001 | 0.022 |
Zhang et al. [34] | 1D-CNN (non-handcrafted; non-fiducial) | CEBSDB , Physionet-NSRDB FANTASIA and others | 93.5% | — | — |
Hammad et al. [35] | CNN VGG-Net (non-handcrafted; non-fiducial) | MWM-HIT , PTB and CYBHi | 96.8% | 0.03 | 0.03 |
Our proposal | Tuned VGG19-net (non-handcrafted; non-fiducial) | Physionet-NSRDB | 91.0% | 0.092 | 0.0102 |
ECG-GUDB | 91.6% | 0.081 | 0.0094 |
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Camara, C.; Peris-Lopez, P.; Safkhani, M.; Bagheri, N. ECG Identification Based on the Gramian Angular Field and Tested with Individuals in Resting and Activity States. Sensors 2023, 23, 937. https://doi.org/10.3390/s23020937
Camara C, Peris-Lopez P, Safkhani M, Bagheri N. ECG Identification Based on the Gramian Angular Field and Tested with Individuals in Resting and Activity States. Sensors. 2023; 23(2):937. https://doi.org/10.3390/s23020937
Chicago/Turabian StyleCamara, Carmen, Pedro Peris-Lopez, Masoumeh Safkhani, and Nasour Bagheri. 2023. "ECG Identification Based on the Gramian Angular Field and Tested with Individuals in Resting and Activity States" Sensors 23, no. 2: 937. https://doi.org/10.3390/s23020937