Deep Learning for ECG-Based Identity Verification
Author Information
Author(s): Maleki Lonbar Sajjad, Beigi Akram, Bagheri Nasour, Peris-Lopez Pedro, Camara Carmen
Primary Institution: Shahid Rajaee Teacher Training University
Hypothesis
Can ECG signals be effectively used for identity verification through deep learning techniques?
Conclusion
The study demonstrates that ECG signals can achieve high accuracy in identity verification using deep learning methods.
Supporting Evidence
- The model achieved an accuracy of 99.3% on the NSRDB dataset.
- The model demonstrated an accuracy of 99.004% on the MITDB dataset.
- The Equal Error Rate (EER) was 0.8% for both datasets.
- Deep learning techniques significantly improved the performance of ECG-based authentication.
Takeaway
This study shows that your heart's electrical signals can be used to confirm your identity, just like a fingerprint.
Methodology
The study used ECG signals processed through the Wigner-Ville distribution and analyzed with a convolutional neural network (GoogleNet) for identity verification.
Limitations
The study may be limited by the noise present in ECG signals and the need for extensive preprocessing.
Participant Demographics
The study included 48 individuals from the MITDB dataset and 18 from the NSRDB dataset, with ages ranging from 20 to 89 years.
Statistical Information
P-Value
0.8
Statistical Significance
p<0.05
Digital Object Identifier (DOI)
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