Epilepsy is a brain disorder disease that affects people’s quality of life. Electroencephalography (EEG) signals are used to diagnose epileptic seizures. This paper provides a computer-aided diagnosis system (CADS) for the automatic diagnosis of epileptic seizures in EEG signals. The proposed method consists of three steps, including preprocessing, feature extraction, and classification. In order to perform the simulations, the Bonn and Freiburg datasets are used. Firstly, we used a band-pass filter with 0.5–40 Hz cut-off frequency for removal artifacts of the EEG datasets. Tunable-Q Wavelet Transform (TQWT) is used for EEG signal decomposition. In the second step, various linear and nonlinear features are extracted from TQWT sub-bands. In this step, various statistical, frequency, and nonlinear features are extracted from the sub-bands. The nonlinear features used are based on fractal dimensions (FDs) and entropy theories. In the classification step, different approaches based on conventional machine learning (ML) and deep learning (DL) are discussed. In this step, a CNN–RNN-based DL method with the number of layers proposed is applied. The extracted features have been fed to the input of the proposed CNN–RNN model, and satisfactory results have been reported. In the classification step, the K-fold cross-validation with k = 10 is employed to demonstrate the effectiveness of the proposed CNN–RNN classification procedure. The results revealed that the proposed CNN–RNN method for Bonn and Freiburg datasets achieved an accuracy of 99.71% and 99.13%, respectively.

Goal: Human voice is a promising noninvasive indicator for diagnosing Parkinson's Disease (PD). It is also unique since it can be collected remotely, increasing accessibility to a wide range of underprivileged patients. However, recognizing PD's signature in the human voice is nontrivial since the available features are many, and the signal may be noisy. Methods: A new mechanism based on Interval Type-2 Fuzzy Analytical Hierarchy Process is proposed here for choosing a reduced feature set from 339 dysphonia speech features, based on five criteria of 1) Robustness, 2) Relief, 3) Minimum Redundancy and Maximum Relevance, 4) Gaussian mixture model separation, and 5) Classifier separation ability. A Least Squares Support Vector Machine then categorizes the samples as belonging to either a healthy subject or a patient with PD. The database of 47 subjects with an average age of 67 is obtained from the elderly in nursing homes and Parkinson's specialized clinics. By reducing signal quality similar to a standard phone line, we study the teleoperation prospect of the proposed technique. Results: Ten-fold cross-validation shows an overall accuracy of 95.32%(93.11%) for noiseless(noisy) conditions, with separate analysis for male, female, and both genders populations. Furthermore, Leave-One-Speaker-Out analysis yields an overall accuracy of 93.11%(84.61%) for noiseless(noisy) conditions. Conclusion: The proposed strategy offers viable remote PD diagnosis with higher accuracy for the male population. Significance: The proposed method suggests reduced feature sets that meet differing objectives of simplicity, performance, and robustness. Results could be particularly significant in PD diagnosis in remote areas.

Background: Spastic hemiparetic cerebral palsy (CP) occurs due to damage to the hemisphere of the brain responsible for controlling movement. Objectives: This study aims to propose a novel bimanual training approach utilizing a specific computer game to improve hand movement function and enhance working memory in children with hemiparetic CP. Methods: Five children with hemiparetic CP participated in a 15-session intervention three times a week, each lasting 30 minutes, incorporating bimanual training through a computer game. The Fugl-Meyer assessment (FMA-UE) and the Corsi block-tapping test evaluated sensorimotor and cognitive abilities. Statistical analyses were conducted using SPSS software. Results: The mean FMA-UE score before and after the intervention was 51 ± 3.31 and 59.5 ± 1.14, respectively, indicating a statistically significant difference (P = 0.008) and an increase in total upper extremity motor function score. Additionally, the mean Corsi block-tapping test score for memory span before and after the intervention was 2.3 ± 0.84 and 3.1 ± 1.14, respectively, demonstrating improved cognitive ability. Conclusions: The combination of bimanual hand training and video games has been shown to be effective in enhancing motor-cognitive abilities in children with hemiparetic CP.

Introduction: Application of biofeedback techniques in rehabilitation has turned into an exciting research area during the recent decade. Providing an appropriate visual or auditory biofeedback signal is the most critical requirement of a biofeedback technique. In this regard, changes in Surface Electromyography (SEMG) signals during wrist movement can be used to generate an indictable visual biofeedback signal for wrist movement rehabilitation via SEMG biofeedback. This paper proposes a novel methodology for selecting the most appropriate features out of wrist muscle SEMG signals. Methods: To this end, the surface EMG signals from flexor and extensor muscle groups during wrist joint movements were recorded and analyzed. Some linear and nonlinear features in frequency, time, and time-frequency domains were extracted from the recorded surface EMG signals of the flexor and extensor muscles. Experiments and analyses were performed on ten healthy subjects and four stroke patients with wrist muscle spasticity as the movement disorder subjects. Some heuristic feature selection measures were applied. The main motivation behind choosing applied heuristic feature selection measures was meeting. In the first step, the designed visual biofeedback signal should indicate a healthy wrist motion profile as its successful tracking by the patient guarantees rehabilitation. In addition, the visual biofeedback signal should be a smooth curve thus preventing the patient from discomfort while tracking it on a monitor during the biofeedback therapy. Results: In this pilot study, after using the introduced feature selection measures, quantitative and qualitative analyses of the extracted features indicated that Shannon entropy is the most appropriate feature for generating a visual biofeedback signal as a healthy wrist motion profile to improve the ability of stroke patients in controlling wrist joint motion. In addition, it was shown that when the wrist joint moves between a flexed and rest position, the flexor muscle EMG signal should be used for generating a visual biofeedback signal. However when the wrist joint moves between a rest position and an extended position, the extensor muscle EMG signal is appropriate for providing a visual biofeedback signal. It is worth noting that the achieved pilot study results should be confirmed by the future studies with larger samples. Conclusion: According to the obtained results, it can be concluded that among the analyzed features, the Shannon entropy was the most appropriate feature. It can be employed for generating a visual biofeedback signal for reduction of spasticity in patients with stroke.