Technically, a feature represents a distinguishing property, a recognizable measurement, and a functional component obtained from a section of a pattern. Extracted features are meant to minimize the loss of important information embedded in the signal. In addition, they also simplify the amount of resources needed to describe a huge set of data accurately. This is necessary to minimize the complexity of implementation, to reduce the cost of information processing, and to cancel the potential need to compress the information. More recently, a variety of methods have been widely used to extract the features from EEG signals, among these methods are time frequency distributions (TFD), fast fourier transform (FFT), eigenvector methods (EM), wavelet transform (WT), and auto regressive method (ARM), and so on. In general, the analysis of EEG signal has been the subject of several studies, because of its ability to yield an objective mode of recording brain stimulation which is widely used in brain-computer interface researches with application in medical diagnosis and rehabilitation engineering. The purposes of this paper, therefore, shall be discussing some conventional methods of EEG feature extraction methods, comparing their performances for specific task, and finally, recommending the most suitable method for feature extraction based on performance.

Electrocardiograph (ECG) compression techniques are gaining momentum due to the huge database requirements and wide band communication channels needed to maintain high quality ECG transmission. Advances in computer software and hardware enable the birth of new techniques in ECG compression, aiming at high compression rates. In general, most of the introduced ECG compression techniques depend on their evaluation performance on either inaccurate measures or measures targeting random behavior of error. In this paper, a new wavelet-based quality measure is proposed. A new wavelet-based quality measure is proposed. The new approach is based on decomposing the segment of interest into frequency bands where a weighted score is given to the band depending on its dynamic range and its diagnostic significance. A performance evaluation of the measure is conducted quantitatively and qualitatively. Comparative results with existing quality measures show that the new measure is insensitive to error variation, is accurate, and correlates very well with subjective tests

Blindness is a state of lacking the visual perception due to physiological or neurological factors. The partial blindness represents the lack of integration in the growth of the optic nerve or visual centre of the eye, and total blindness is the full absence of the visual light perception. In this work, a simple, cheap, friendly user, smart blind guidance system is designed and implemented to improve the mobility of both blind and visually impaired people in a specific area. The proposed work includes a wearable equipment consists of head hat and mini hand stick to help the blind person to navigate alone safely and to avoid any obstacles that may be encountered, whether fixed or mobile, to prevent any possible accident. The main component of this system is the infrared sensor which is used to scan a predetermined area around blind by emitting-reflecting waves. The reflected signals received from the barrier objects are used as inputs to PIC microcontroller. The microcontroller is then used to determine the direction and distance of the objects around the blind. It also controls the peripheral components that alert the user about obstacle's shape, material, and direction. The implemented system is cheap, fast, and easy to use and an innovative affordable solution to blind and visually impaired people in third world countries.

A low-cost, fast, dependable, repeatable, non-invasive, portable, and simple-to-use vascular screening tool for coronary artery diseases (CADs) is preferred. Photoplethysmography (PPG), a low-cost optical pulse wave technology, is one method with this potential. PPG signals come from changes in the amount of blood in the microvascular bed of tissue. Therefore, these signals can be used to figure out anomalies within the cardiovascular system. This work shows how to use PPG signals and feature selection-based classifiers to identify cardiorespiratory disorders based on the extraction of time-domain features. Data were collected from 360 healthy and cardiovascular disease patients. For analysis and identification, five types of cardiovascular disorders were considered. The categories of cardiovascular diseases were identified using a two-stage classification process. The first stage was utilized to differentiate between healthy and unhealthy subjects. Subjects who were found to be abnormal were then entered into the second stage classifier, which was used to determine the type of the disease. Seven different classifiers were employed to classify the dataset. Based on the subset of features found by the classifier, the Naïve Bayes classifier obtained the best test accuracy, with 94.44% for the first stage and 89.37% for the second stage. The results of this study show how vital the PPG signal is. Many time-domain parts of the PPG signal can be easily extracted and analyzed to find out if there are problems with the heart. The results were accurate and precise enough that they did not need to be looked at or analyzed further. The PPG classifier built on a simple microcontroller will work better than more expensive ones and will not make the patient nervous.

Photoplethysmography (PPG) signals provide a non-invasive method for monitoring cardiovascular health, including blood pressure levels, which are critical for the early detection and management of hypertension. This study leverages wavelet transformation and special purpose deep learning model, enhanced by signal processing and normalization, to classify blood pressure stages from PPG signals. The primary objective is to advance non-invasive hypertension monitoring, improving the accuracy and efficiency of these assessments. The study employed continuous wavelet transform (CWT) to prepare PPG signals for analysis using a special purpose PPG-NET designed by applying advanced deep-learning models. PPG-NET was verified by applying several pre-trained models, including Inception, MobileNetV2, InceptionResNetV2, and others to the PPG data. Rigorously five-fold cross-validated models were conducted to obtain the models' performance to ensure robustness and repeatability of results. The PPG-NET model demonstrated superior performance, achieving a perfect accuracy of 100 % in classifying the four stages of hypertension—normal, prehypertension, stage 1, and stage 2. The evaluation metrics reported include precision, sensitivity, and specificity, with the PPG-NET model achieving 100 % across all metrics. Other models showed varying levels of accuracy, with InceptionV3 also reaching 91.5 %, while some, like VGG-19, underperformed significantly. Integrating CWT and PPG-NET offers a promising avenue for enhancing non-invasive blood pressure monitoring. The PPG-NET model, in particular, showed potential for clinical application due to its high accuracy and reliability. This study showed the effectiveness of combining advanced computational techniques with traditional PPG analysis, potentially leading to more personalized and accessible hypertension management strategies.