In machine learning, an efficient classifier model design is mostly based on effective feature extraction and appropriate feature selection. This work mainly focused on different optimized feature selection algorithms for automatic biometric recognition system with the use of electrocardiogram (ECG) signals. Initially, the features are extracted from P-QRS-T segments of the ECG signal with position normalization. The extracted features are processed through different optimization algorithms for quality assessment prior to the classification stage. In this work, two different methods are proposed based on wrapper feature selection and embedded feature selection to get optimized feature datasets for the perfect identification of human biometrics. In the first proposed method, wrapper-oriented feature selection is implemented with genetic algorithm (GA) and particle swarm optimization. In the second proposed method, the embedded method is included as the least absolute shrinkage selection operator and elastic net (EN). The processed optimized feature vectors from the optimization phase are fed to popular machine learning techniques, such as support vector machine and random forest (RF) classifiers, for automatic biometric human recognition. Classifier performances are investigated using two publicly available open-source databases, Electrocardiogram identification and large Physikalisch-Technische Bundesanstalt diagnostic database with a dynamic number of subjects. The experimental results indicate that the proposed combination of feature selection with machine learning algorithms has enhanced the classification accuracy. Finally, GA and EN with RF classifier provide improved recognition rates of 95.30% and 94.90%, respectively.

An electrocardiogram (ECG) is a unique representation of a person’s identity, similar to fingerprints, and its rhythm and shape are completely different from person to person. Cloning and tampering with ECG-based biometric systems are very difficult. So, ECG signals have been used successfully in a number of biometric recognition applications where security is a top priority. The major challenges in the existing literature are (i) the noise components in the signals, (ii) the inability to automatically extract the feature set, and (iii) the performance of the system. This paper suggests a beat-based template matching deep learning (DL) technique to solve problems with traditional techniques. ECG beat denoising, R-peak detection, and segmentation are done in the pre-processing stage of this proposed methodology. These noise-free ECG beats are converted into gray-scale images and applied to the proposed deep-learning technique. A customized activation function is also developed in this work for faster convergence of the deep learning network. The proposed network can extract features automatically from the input data. The network performance is tested with a publicly available ECGID biometric database, and the proposed method is compared with the existing literature. The comparison shows that the proposed modified Siamese network authenticated biometrics have an accuracy of 99.85%, a sensitivity of 99.30%, a specificity of 99.85%, and a positive predictivity of 99.76%. The experimental results show that the proposed method works better than the state-of-the-art techniques.

The rising risk of diabetes, particularly in emerging countries, highlights the importance of early detection. Manual prediction can be a challenging task, leading to the need for automatic approaches. The major challenge with biomedical datasets is data scarcity. Biomedical data is often difficult to obtain in large quantities, which can limit the ability to train deep learning models effectively. Biomedical data can be noisy and inconsistent, which can make it difficult to train accurate models. To overcome the above-mentioned challenges, this work presents a new framework for data modeling that is based on correlation measures between features and can be used to process data effectively for predicting diabetes. The standard, publicly available Pima Indians Medical Diabetes (PIMA) dataset is utilized to verify the effectiveness of the proposed techniques. Experiments using the PIMA dataset showed that the proposed data modeling method improved the accuracy of machine learning models by an average of 9%, with deep convolutional neural network models achieving an accuracy of 96.13%. Overall, this study demonstrates the effectiveness of the proposed strategy in the early and reliable prediction of diabetes.