The world has a higher count of death rates as a result of Alcohol consumption. Identification is possible because Alcoholic EEG waves have a certain behavior that is totally different compared to the non-alcoholic individual. The available approaches take longer to provide the feedback because they analyze the data manually. For this reason, in the present paper we propose a novel approach applied to detect alcoholic EEG signals automatically by using deep learning methods. Our strategy has advantages as far as fast detection is concerned; hence people can help immediately when there is a need. The potential for a significant decrease in deaths from alcohol poisoning and improvement to public health is presented by this advancement. In order to create clusters and classify the alcoholic EEG signals, this research uses a cascaded process. To begin with, an initial clustering and feature extraction is done by LASSO regression. After that, a variety of meta-heuristics algorithms like Particle Swarm Optimization (PSO), Binary Coding Harmony Search (BCHS) as well as Binary Dragonfly Algorithm (BDA) are employed for feature minimization. When this method is used, normal and alcoholic EEG signals may be differentiated using non-linear features. PSO, BCHS, and BDA features allow for estimation of statistical parameters through t-test, Friedman statistic test, Mann-Whitney U test, and Z-Score with corresponding p-values for alcoholic EEG signals. Lastly, classification is done by the use of support vector machines (SVM) (including linear, polynomial, and Gaussian kernels), random forests, artificial neural networks (ANN), enhanced artificial neural networks (EANN), and LSTM models. Results showed that LASSO regression with BDA-based EANN proposed classifier have a classification accuracy of 99.59%, indicating that our method is highly accurate at classifying alcoholic EEG signals.

Cardiovascular disease (CVD) is connected with irregular cardiac electrical activity, which can be seen in ECG alterations. Due to its convenience and non-invasive aspect, the ECG is routinely exploited to identify different arrhythmias and automatic ECG recognition is needed immediately. In this paper, enhancement for the detection of CVDs such as Ventricular Tachycardia (VT), Premature Ventricular Contraction (PVC) and ST Change (ST) arrhythmia using different dimensionality reduction techniques and multiple classifiers are presented. Three-dimensionality reduction methods, such as Local Linear Embedding (LLE), Diffusion Maps (DM), and Laplacian Eigen (LE), are employed. The dimensionally reduced ECG samples are further feature selected with Cuckoo Search (CS) and Harmonic Search Optimization (HSO) algorithms. A publicly available MIT-BIH (Physionet) - VT database, PVC database, ST Change database and NSR database were used in this work. The cardiac vascular disturbances are classified by using seven classifiers such as Gaussian Mixture Model (GMM), Expectation Maximization (EM), Non-linear Regression (NLR), Logistic Regression (LR), Bayesian Linear Discriminant Analysis (BDLC), Detrended Fluctuation Analysis (Detrended FA), and Firefly. For different classes, the average overall accuracy of the classification techniques is 55.65 % when without CS and HSO feature selection, 64.36 % when CS feature selection is used, and 75.39 % when HSO feature selection is used. Also, to improve the performance of classifiers, the hyperparameters of four classifiers (GMM, EM, BDLC and Firefly) are tuned with the Adam and Grid Search Optimization (GSO) approaches. The average accuracy of classification for the CS feature-based classifiers that used GSO and Adam hyperparameter tuning was 79.92 % and 85.78 %, respectively. The average accuracy of classification for the HSO feature-based classifiers that used GSO and Adam hyperparameter tuning was 86.87 % and 93.77 %, respectively. The performance of the classifier is analyzed based on the accuracy parameter for both with and without feature selection methods and with hyperparameter tuning techniques. In the case of ST vs. NSR, a higher accuracy of 98.92 % is achieved for the LLE dimensionality reduction with HSO feature selection for the GMM classifier with Adam's hyperparameter tuning approach. The GMM classifier with the Adam hyperparameter tuning approach with 98.92 % accuracy in detecting ST vs. NSR cardiac disease is outperforming all other classifiers and methodologies.

Epilepsy is a chronic brain condition affecting one in 100 patients. EEG (Electroencephalogram) is a signal representing the influence of combining various processes in the brain. This paper aims to identify the likelihood levels of epilepsy from the features extracted from the EEG signals and to compare them with the output of the Singular Value Decomposition and Extreme Learning Machine (ELM). The SVD approach extracts functionality from EEG signals (that is, to minimize dimensionality) while the ELM is used as a classification system. A study of twenty patients is documented in this paper. The criteria such as the Performance Index (PI), Sensitivity (Se), Specificity (Sp), Average Detection (AD), and Good Detection Ratio (GDR) are evaluated using ELM to identify epilepsy. Results show that when SVD is classified with ELM at sigmoid activation function, an average detection of 98.94% and a GDR of 97.83% are obtained.

This paper explores the earlier detection of CHF, and its allied Cardio Vascular Diseases (CVD) like Atrial Fibrillation (Afib) from nature-inspired optimization techniques using cardiac signals. The Beth Israel Deaconess Medical Centre (BIDMC) CHF, MIT-BIH-Afib, and Normal Sinus Rhythm (NSR) databases from the Physionet repository are utilized in this research. The Flower Pollination Algorithm (FPA), Binary Flower Pollination Algorithm (BFPA), and BFPA with Greedy Crossover approaches are applied to extract the features of CHF, Afib, and NSR cardiac classes ECG signals. Then extracted features are given into appropriate classifiers such as the SVM (Linear), SVM (Polynomial), SVM (RBF), WKNN, and NBC classifiers. The classifiers’ performance is assessed and compared by the standard performance benchmarks, such as Accuracy, F1 Score, Geometric Mean, Kappa Coefficient and Error Rate. The SVM (RBF) classifier with BFPA features achieved 97.71% overall average accuracy along with a 0.9540 kappa coefficient in {CHF} vs. {NSR} cardiac abnormality detection. The SVM (RBF) classifier with BFPA features reached 89.59% overall average accuracy along with a 0.7935 kappa coefficient in {Afib} vs. {NSR} cardiac class detection. As a result, the SVM (RBF) classifier with BFPA features outclasses all other classification techniques in detecting {CHF} vs. {NSR} cardiac abnormality classes.

The Electrocardiogram (ECG) is a bioelectric record of heart activity. ECG signal classification is crucial for heart disease diagnosis. It is difficult to make an appropriate ECG classification. In this study, MIT-BIH Arrhythmia ECG data base from physionet has been used to classify Cardiogram Signals. The very first, raw ECG signal is preprocessed by using noise cancellation (filtering) methods are Adaptive noise cancellation techniques, low pass linear noise cancellation techniques and high pass linear filter. After preprocessing reduces the dimensionality of data by using Coordinated Factor Analysis (CFA) and finally, dimensionally reduced signals are classified by using Water Wave Optimization (WWO) classifier. The Performance metrics like Performance Index, Accuracy, Sensitivity, and Specificity are evaluated using WWO to analyze the classification of arrhythmias from ECG signals.