Detection of ataxia in low disability MS patients by hybrid convolutional neural networks based on images of plantar pressure distribution.

BACKGROUND: This study aimed to detect ataxia in patients with multiple sclerosis (PwMS) with a deep learning-based approach based on images showing plantar pressure distribution of the patients. The secondary aim of the study was to investigate an alternative and objective method in the early diagnosis of ataxia in these patients. METHODS: A total of 105 images showing plantar pressure distribution of 43 ataxic PwMS and 62 healthy individuals were analyzed. The images were resized for the models including VGG16, VGG19, ResNet, DenseNet, MobileNet, NasNetMobile, and NasNetLarge. Feature vectors were extracted from the resized images and then classified using Support Vector Machines (SVM), K-Nearest Neighbors (K-NN), and Artificial Neural Network (ANN). A 10-fold cross-validation was applied to increase the validity of the classifiers. RESULTS: The VGG19-SVM hybrid model showed the highest accuracy, sensitivity, and specificity values (89.23%, 89.65%, and 88.88%, respectively). CONCLUSION: The proposed method provided an automatic decision support system for detecting ataxia based on images showing plantar pressure distribution in patients with PwMS. The performance of the proposed method indicated that this method can be applied in clinical practice to establish a rapid diagnosis of ataxia that is asymptomatic or difficult to detect clinically and that it can be recommended as a useful aid for the physician in clinical practice.

A deep learning-based decision support system for diagnosis of OSAS using PTT signals.

Sleep disorders, which negatively affect an individual's daily quality of life, are a common problem for most of society. The most dangerous sleep disorder is obstructive sleep apnea syndrome (OSAS), which manifests itself during sleep and can cause the sudden death of patients. Many important parameters related to the diagnosis and treatments of such sleep disorders are simultaneously examined. This process is exhausting and time-consuming for experts and also requires experience; thus, it can cause difference of opinion among experts. Because of this, automatic sleep staging systems have been designed. In this study, a decision support system was developed to determine OSAS patients. In the developed decision support system, unlike in the available published literature, patient and healthy individual classification was performed using only the Pulse Transition Time (PTT) parameter rather than other parameters obtained from polysomnographic data like ECG (Electrocardiogram), EEG (Electroencephalography), carbon dioxide measurement and EMG (Electromyography). The suggested method can perform feature extraction from PTT signals by means of a deep-learning method. AlexNet and VGG-16, which are two Convolutional Neural Network (CNN) models, have been used for feature extraction. With the features obtained, patients and healthy individuals were classified by the Support Vector Machine (SVM) and the k-nearest neighbors (k-NN) algorithms. When the performance of the study was compared with other studies in published literature, it was seen that satisfactory results were obtained.

True Random Number Generation from Bioelectrical and Physical Signals.

It is possible to generate personally identifiable random numbers to be used in some particular applications, such as authentication and key generation. This study presents the true random number generation from bioelectrical signals like EEG, EMG, and EOG and physical signals, such as blood volume pulse, GSR (Galvanic Skin Response), and respiration. The signals used in the random number generation were taken from BNCIHORIZON2020 databases. Random number generation was performed from fifteen different signals (four from EEG, EMG, and EOG and one from respiration, GSR, and blood volume pulse datasets). For this purpose, each signal was first normalized and then sampled. The sampling was achieved by using a nonperiodic and chaotic logistic map. Then, XOR postprocessing was applied to improve the statistical properties of the sampled numbers. NIST SP 800-22 was used to observe the statistical properties of the numbers obtained, the scale index was used to determine the degree of nonperiodicity, and the autocorrelation tests were used to monitor the 0-1 variation of numbers. The numbers produced from bioelectrical and physical signals were successful in all tests. As a result, it has been shown that it is possible to generate personally identifiable real random numbers from both bioelectrical and physical signals.