MULTIMODAL PHYSIOLOGICAL SIGNALS INTEGRATION FOR ROBUST BIOMETRIC IDENTIFICATION: A NOVEL APPROACH USING ECG, ICG, AND BLOOD PRESSURE SIGNALS WITH ADVANCED CLASSIFIERS

In response to the dynamic landscape of biometric identification, this research explores a novel paradigm by integrating physiological signals, including electrocardiography (ECG), impedance cardiography (ICG), and blood pressure (BP), to enhance precision and reliability in identity verification systems. Leveraging an unexplored database comprising these three signals collected from 30 individuals, advanced classifiers, specifically fine Gaussian support vector machines (FG-SVM) and a bi-layered artificial neural network (Bi-ANN) were employed to construct a robust identification model. Our investigation involved extracting diverse statistical and entropy features from the signals, enriching the dataset. Results indicate the FG-SVM model achieved an 88.14% accuracy during training, with a recall of 95.09%, precision of 94.33%, and a Kappa coefficient of 87.7%. In the test set, FG-SVM demonstrated 93.33% accuracy, balanced recall and precision of 93.33%, and a Kappa coefficient of 92.9%. The Bi-layered ANN model exhibited superior training performance, attaining 93.3% accuracy, 94.56% recall, 93.17% precision, and a Kappa coefficient of 93.1%. Notably, in the test set, Bi-layered ANN achieved perfect accuracy, recall, precision, and Kappa coefficient of 100%. Our study includes a critical comparison with previous studies, this comparison highlights the novel insights gained from our approach, emphasizing the potential for accurate and dependable biometric identification in real-world applications. Additionally, our research contributes to the field by emphasizing the significance of non-handcrafted features, further enhancing the richness of the dataset and reinforcing the robustness of the identification model.