Biomedical Engineering: Applications, Basis and Communications

Covid-19 invaded the world very quickly and caused the loss of many lives; maximum emergency was activated all over the world due to its rapid spread. Consequently, it became a huge burden on emergency and intensive care units due to the large number of infected individuals and the inability of the medical staff to deal with patients according to the degree of severity. Covid-19 can be diagnosed based on the artificial intelligence (AI) model. Based on AI, the CT images of the patient’s chest can be analyzed to identify the patient case whether it is normal or he/she has Covid-19. The possibility of employing physiological sensors such as heart rate, temperature, respiratory rate, and SpO2 sensors in diagnosing Covid-19 was investigated. In this paper, several articles which used intelligent techniques and vital signs for diagnosing Covid-19 have been reviewed, classified, and compared. The combination of AI and physiological sensors reading, called AI-PSR, can help the clinician in making the decisions and predicting the occurrence of respiratory failure in Covid-19 patients. The physiological parameters of the Covid-19 patients can be transmitted wirelessly based on a specific wireless technology such as Wi-Fi and Bluetooth to the clinician to avoid direct contact between the patient and the clinician or nursing staff. The outcome of the AI-PSR model leads to the probability of recording and linking data with what will happen later, to avoid respiratory failure, and to help the patient with one of the mechanical ventilation devices.

The increase in aged population is a global trend. Inculcating healthy behaviors such as regular exercises in the elderly has a significant impact on the financial and medical burden globally. Moreover, air pollution and the outbreak of the coronavirus disease 19 (COVID-19) pose a serious threat to public health. In order to improve the health conditions of the population, this study developed a motion feedback system named MoveV that can be used for several indoor training exercises. This system provides instant motion feedback by synchronizing exercise training videos on the website using a motion analysis algorithm that is applicable on smartphones, and a cloud database platform is used to record health behaviors. Feature extraction is performed based on force intensity, motion velocity, and exercise direction. The resultant accuracy of the motion feedback system was tested by a motion science expert and presented as the confidence level. For perfect movement, a confidence level of up to 90.5% was achieved, indicating that the MoveV system was able to record users’ exercise frequency and distinguish whether the user was performing well in the exercise movements. The proposed system is convenient and does not incur additional expenditure by purchasing any new device. Furthermore, it provides visual and voice feedback, companionship, and exercise motivation to the users, all of which are important factors when using online exercise platforms.

For many years, the uncertainty of lie-detection systems has been one of the concerns of tax organizations. Clearly, the results of these systems must be generalized by a high value of accuracy to be acceptable by related systems to identify tax fraud. In this paper, a new method based on P300-based component has been proposed for detection of tax fraud. To this end, the test protocol is designed based on Odd-ball paradigm concealed information recognition. This test was done on 40 people and their brain signals were acquired. After prepossessing, the classic features are extracted from each single trial. After that, time–frequency (TF) transformation is applied on the sweeps and TF features are produced thereupon. Then, the best combinational feature vector is selected in order to improve classifier accuracy. Finally, guilty and innocent persons are classified by K-nearest neighbor (KNN) and multilayer perceptron (MLP) classifiers. We found that combination of time–frequency and classic features has better ability to achieve higher amount of accuracy to identify the unrealistic tax returns. The obtained results show that the proposed method can detect deception by the accuracy of 91% which is better than other previously reported methods. This study, for the first time, succeeded in presenting a novel method for identifying unrealistic tax returns through EEG signal processing, which has significantly improved the yield of this study compared to the previous literature.

Wireless Sensor Networks (WSNs) have significantly impacted healthcare applications by giving the possibility of monitoring the patient’s physiological parameters using different sensors. The use of WSN and the wireless Body Area Networks (WBANs) offers possible solutions for monitoring the health parameters in remote areas. On the other hand, the use of wireless communication medium and information security is the primary concern in WBANs. Because WBANs use the different small sensors placed on the human body to collect the physiological data. They need resource and computational restrictions, thus, building the use of complex and advanced encryption algorithms infeasible. It is essential in the WBAN to monitor and transmit the data to provide reliable and secure communication. Wrong and incomplete information can create difficulties in patient health which can be sometimes more dangerous. This gives the motivation to make such security protocols or algorithms to achieve high security in WBANs. So, the research has been currently focused on reliable communication between the doctor and patient, routing algorithms, and the data’s security by using various new technologies. This paper discusses the different security threats and solutions for designing healthcare applications and routing and layer attacks. Furthermore, the paper has been focused on the Data Distribution Service Models for data security. The paper also includes artificial intelligence and machine learning algorithms in healthcare implemented by various companies.

Among the different bio-signals modalities, Electromyographic signal (EMG) has been one of the frequently used signals in the bio-robotics applications field. This is due to the fact that the EMG reflects directly the muscle activity of the user following the human motion intention. Consequently, the decoding of this intention is an essential task for controlling devices such as prosthetic hands and exoskeletons, based on EMG signals. This paper deals with the processing of EMG signals of the forearm muscles, in order to control two degrees of freedom (2 DoFs) robotic hand. The main contribution of this paper is the proposal of a hybrid approach that combines a pattern and a non-pattern recognition-based strategy. The proposed approach aims to take advantage of both strategies and overcome their shortcomings leading to a better analysis of the user movement intention. The EMG recorded signals are processed for feature extraction based on a Wavelet Packet Decomposition (WPD) method and classification using an Artificial Neural Network (ANN). Furthermore, we investigate the effect of the various parameters such as the applied force level, the number of the EMG channels and the window length of the EMG signal. The proposed approach is validated experimentally under realistic conditions. Very interesting results have been obtained for user intention decoding.

To meet the challenge of regenerating bone lost to disease or trauma, biodegradable scaffolds are being investigated as a way to regenerate bone without the need for an auto- or allograft. Herein, we prepared poly (lactic acid) (PLA)/chitosan(CS)/nano-hydroxyapatite biomaterials through solution polymerization and solvent volatilization. Cefadroxil was used as a model drug for loading on biomaterials using supercritical carbon dioxide. In addition, we investigated the in vitro drug release effect, and the in vitro release results showed that the drug could release more than 73% of the drug load within 48h. This excellent drug release property could allow continuous drug use at the wound site, further broadening its application in the medical field. A three-dimensional finite element (FE) model of bone screws was established, and the mechanical properties of the screws were numerically calculated. The stress and deformation of the bone screws under different external conditions were simulated. The bending simulation showed that the screw can withstand the maximum deformation of 0.418mm and equivalent stress of 566.94MPa at a force of 700 N. The maximum equivalent stress of the screw reached 321.84MPa, and the corresponding torque was 779.68 N⋅mm when the torsion angle was gradually increased to 30${}^{\circ }$. The fabricated material has excellent mechanical properties and can be used for bone repair. This study provides a new direction for preparing drug-loaded polymer biomaterials and developing new materials for bone repair.

Recently, there is an immense increase in the mortality rate of humans due to dangerous diseases, which is becoming a greater issue across the globe. The only solution to this issue is the early detection of infectious diseases, so that the seriousness of their symptoms can be reduced before reaching an adverse stage. In recent days, associative rule mining, which is a computational insight strategy is being more commonly utilized for early risk prediction of the disease. In the case of rule mining, there is a massive count of the frequent patterns that might deviate from the detection mechanism. Therefore, different customized algorithms are being implemented. Among them, the Apriori algorithm is a standardized model which is good in detecting the more frequent patterns. But, owing to a huge count of candidates as well as scans of the database, the ties technique has become inefficient. Therefore, to override these issues and to find a promising solution for the early disease prediction, “a new 2-phase parallel processing based Coalesce based Binary (CBB) Table” is introduced in this paper. The proposed disease prediction model involves: pre-processing, 2-phase parallel processing, weighted coalesces rule generation, optimal feature extraction, and classification. Particularly, for selecting the optimal features, the Grey Wolf Levy update – dragonfly algorithm (GWU–DA) algorithm is used and a hybrid classification model that incorporates “Support vector Machine (SVM) and Deep Belief Network (DBN)” is used to predict the presence of disease. Finally, the validation of this work over the extant models is accomplished in terms of various performance measures.

This study presents a novel liver disease classification method by applying pattern recognition technique to automatically segmented liver from the images of computed tomographic (CT) scans. The methodology comprises of disease classification by the extraction of textural features from focal liver region bearing tumors. Two types of liver textures are investigated in this study for classification accuracy judgement. First, original liver texture is considered for feature extraction. Second, liver is used for feature extraction. The CT image dataset comprises 308 liver samples with 193 samples of malignant tumor and 115 samples of benign tumor. The entire liver tissue bearing tumor is segmented from the CT image automatically in the pre-processing stage using fuzzy transformation function and morphological processing. Four sets of textural feature matrices are applied to the liver for feature extraction. Gray level co-occurrence matrix (GLCM), standard deviation gray level co-occurrence matrix (SDGLCM), seven-moment matrix (7MM) and seven-moment gray level co-occurrence matrix (7MGLCM) are the combinational feature matrices applied to classify the liver as malignant or benign using support vector machines (SVMs). The best classification accuracy is achieved for original liver texture by 7MGLCM, which is 97% with AUC = 0.99 for training dataset and 97.8% with AUC = 1 for test dataset.