Analysis of static plantar pressure data with capsule networks: Diagnosing ataxia in MS patients with a deep learning-based approach.

In this study, it was aimed to detect ataxia in patients with Multiple Sclerosis (MS) by utilizing static plantar pressure data and capsule networks (CapsNet), one of the deep learning (DL) architectures. CapsNet is also equipped with a robust dynamic routing mechanism that determines the output of the next capsule. MS is a chronic nervous system disease that shows its effect in the central nervous system and manifests itself with attacks. One of the most common and challenging symptoms of MS is known as ataxia. Ataxia causes loss of control of limb muscle tone or gait disorders, leading to loss of balance and coordination. The diagnosis of ataxia in MS is applied employing the standard Expanded Disability Status Scale (EDSS) score. However, due to reasons such as physician misconception, diagnosis differences among physicians, and incorrect patient information, more unbiased solutions are required for the diagnosis. The results included Sensitivity at 96.34 % ± 1.71, Specificity at 98.11 % ± 2.04, Precision at 98.08 % ± 2.16, and Accuracy at 97.13 % ± 0.33. The main motivation of the study is to show that these deep learning methods can successfully detect ataxia in MS patients using static plantar pressure data. The high-performance measurements of sensitivity, specificity, precision and accuracy emphasize that the proposed system can be an effective tool in clinical practice. In addition, it was concluded that the proposed autonomous system would be a support mechanism to assist the physician in the detection of ataxia in patients with MS.

Human Psychological Disorder towards Cryptography: True Random Number Generator from EEG of Schizophrenics and Its Application in Block Encryption's Substitution Box.

Schizophrenia is a multifaceted chronic psychiatric disorder that affects the way a human thinks, feels, and behaves. Inevitably, natural randomness exists in the psychological perception of schizophrenic patients, which is our primary source of inspiration for this research because true randomness is the indubitably ultimate valuable resource for symmetric cryptography. Famous information theorist Claude Shannon gave two desirable properties that a strong encryption algorithm should have, which are confusion and diffusion in his fundamental article on the theoretical foundations of cryptography. Block encryption strength against various cryptanalysis attacks is purely dependent on its confusion property, which is gained through the confusion component. In the literature, chaos and algebraic techniques are extensively used to design the confusion component. Chaos- and algebraic-based techniques provide favorable features for the design of the confusion component; however, researchers have also identified potential attacks on these techniques. Instead of existing schemes, we introduce a novel methodology to construct cryptographic confusion component from the natural randomness, which are existing in the psychological perception of the schizophrenic patients, and as a result, cryptanalysis of chaos and algebraic techniques are not applicable on our proposed technique. The psychological perception of the brain regions was captured through the electroencephalogram (EEG) readings during the sensory task. The proposed design passed all the standard evaluation criteria and validation tests of the confusion component and the random number generators. One million true random bits are assessed through the NIST statistical test suite, and the results proved that the psychological perception of schizophrenic patients is a good source of true randomness. Furthermore, the proposed confusion component attains better or equal cryptographic strength as compared to state-of-the-art techniques (2020 to 2021). To the best of our knowledge, this nature of research is performed for the first time, in which psychiatric disorder is utilized for the design of information security primitive. This research opens up new avenues in cryptographic primitive design through the fusion of computing, neuroscience, and mathematics.

Can laboratory parameters be an alternative to CT and RT-PCR in the diagnosis of COVID-19? A machine learning approach.

In this study, a machine learning-based decision support system that uses routine laboratory parameters has been proposed in order to increase the diagnostic success in COVID-19. The main goal of the proposed method was to reduce the number of misdiagnoses in the RT-PCR and CT scans and to reduce the cost of testing. In this study, we retrospectively reviewed the files of patients who presented to the coronavirus outpatient. The demographic, thoracic CT, and laboratory data of the individuals without any symptoms of the disease, who had negative RT-PCR test and who had positive RT-PCR test were analyzed. CT images were classified using hybrid CNN methods to show the superiority of the decision support system using laboratory parameters. Detection of COVID-19 from CT images achieved an accuracy of 97.56% with the AlexNet-SVM hybrid method, while COVID-19 was classified with an accuracy of 97.86% with the proposed method using laboratory parameters.

Detection of ataxia with hybrid convolutional neural network using static plantar pressure distribution model in patients with multiple sclerosis.

OBJECTIVE: In this study, it is aimed to detect ataxia for Persons with Multiple Sclerosis (PwMS) through a deep learning-based approach using an image dataset containing static plantar pressure distribution. Here, an alternative and objective method will be proposed to assist physicians who diagnose PwMS in the early stages. METHODS: A total of 406 static bipedal pressure distribution image data for 43 ataxic PwMS and 62 healthy individuals were used in the study. After preprocessing, these images were given as input to pre-trained deep learning models such as VGG16, VGG19, ResNet, DenseNet, MobileNet, and NasNetMobile. The data of each model is utilized to generate its feature vectors. Finally, feature vectors obtained from static pressure distribution images were classified by SVM (Support Vector Machine), K-NN (K-Nearest Neighbors), and ANN (Artificial Neural Network). In addition, a cross-validation method was used to examine the validity of the classifier. RESULTS: The performance of the proposed models was evaluated with accuracy, sensitivity, specificity, and F1-measure criteria. The VGG19-SVM hybrid model showed the best performance with 95.12% acc, 94.91% sen, 95.31% spe, and 94.44% F1. CONCLUSIONS: In this study, a specific and sensitive automatic test evaluation system was proposed for Ataxic syndromes using digital images to observe the motor skills of the subjects. Comparative results show that the proposed method can be applied in practice for ataxia that is clinically difficult to detect or not yet symptomatic. It can be defined using only static plantar pressure distribution in the early stage and it can be recommended as an assistant system to physicians in clinical practice.

Scat-NET: COVID-19 diagnosis with a CNN model using scattergram images.

The acute respiratory syndrome COVID-19 disease, which is caused by SARS-CoV-2, has infected many people over a short time and caused the death of more than 2 million people. The gold standard in detecting COVID-19 is to apply the reverse transcription polymerase chain reaction (RT-PCR) test. This test has low sensitivity and produces false results of approximately 15%-20%. Computer tomography (CT) images were checked as a result of suspicious RT-PCR tests. If the virus is not infected in the lung, the virus is not observed on CT lung images. To overcome this problem, we propose a 25-depth convolutional neural network (CNN) model that uses scattergram images, which we call Scat-NET. Scattergram images are frequently used to reveal the numbers of neutrophils, eosinophils, basophils, lymphocytes and monocytes, which are measurements used in evaluating disease symptoms, and the relationships between them. To the best of our knowledge, using the CNN together with scattergram images in the detection of COVID-19 is the first study on this subject. Scattergram images obtained from 335 patients in total were classified using the Scat-NET architecture. The overall accuracy was 92.4%. The most striking finding in the results obtained was that COVID-19 patients with negative RT-PCR tests but positive CT test results were positive. As a result, we emphasize that the Scat-NET model will be an alternative to CT scans and could be applied as a secondary test for patients with negative RT-PCR tests.

Classification of normal sinus rhythm, abnormal arrhythmia and congestive heart failure ECG signals using LSTM and hybrid CNN-SVM deep neural networks.

Effective monitoring of heart patients according to heart signals can save a huge amount of life. In the last decade, the classification and prediction of heart diseases according to ECG signals has gained great importance for patients and doctors. In this paper, the deep learning architecture with high accuracy and popularity has been proposed in recent years for the classification of Normal Sinus Rhythm, (NSR) Abnormal Arrhythmia (ARR) and Congestive Heart Failure (CHF) ECG signals. The proposed architecture is based on Hybrid Alexnet-SVM (Support Vector Machine). 96 Arrhythmia, 30 CHF, 36 NSR signals are available in a total of 192 ECG signals. In order to demonstrate the classification performance of deep learning architectures, ARR, CHR and NSR signals are firstly classified by SVM, KNN algorithm, achieving 68.75% and 65.63% accuracy. The signals are then classified in their raw form with LSTM (Long Short Time Memory) with 90.67% accuracy. By obtaining the spectrograms of the signals, Hybrid Alexnet-SVM algorithm is applied to the images and 96.77% accuracy is obtained. The results show that with the proposed deep learning architecture, it classifies ECG signals with higher accuracy than conventional machine learning classifiers.

Incorporating feature selection methods into a machine learning-based neonatal seizure diagnosis.

The present study developed a feature selection (FS)-based decision support system using the electroencephalography (EEG) signals recorded from neonates with and without seizures. The study employed 10 different FS algorithms to reduce the classification cost by using fewer features and to improve the classification performance of the model by removing the irrelevant features. In doing so, the classification performance of each FS algorithm on each EEG channel difference was also evaluated. The dataset used in the study included EEG measurements and visual EEG annotations that were recorded from 79 term neonates. Multiple features were extracted from each channel difference using the Feature extraction (FE). Subsequently, a novel feature subset was generated for the classification using FS algorithms. The classification performance of each selected feature was assessed based on multiple criteria. The use of features extracted by the combined use of FS algorithms showed higher performance compared to the use of all features. In this study, 18 channel differences were analyzed. Better performance was achieved by using 3 of the selected 14 features or 2 of the selected features. The C4-P4 channel difference showed the highest classification performance (98.8%) among all channel differences. In the literature, FE has already been performed for the classification of the dataset used in the present study. The primary aim of the present study was to perform the same classification with the minimum number of features. The results indicated that feature reduction reduced the cost and also improved the performance of the classification. These results seem to be highly promising and thus can be used in clinical practice and shed light for future studies.

Is it possible to detect cerebral dominance via EEG signals by using deep learning?

Each brain hemisphere is dominant for certain functions such as speech. The determination of speech laterality prior to surgery is of paramount importance for accurate risk prediction. In this study, we aimed to determine speech laterality via EEG signals by using noninvasive machine learning techniques. The retrospective study included 67 subjects aged 18-65 years who had no chronic diseases and were diagnosed as healthy based on EEG examination. The subjects comprised 35 right-hand dominant (speech center located in the left hemisphere) and 32 left-hand dominant individuals (speech center located in the right hemisphere). A spectrogram was created for each of the 18 EEG channels by using various Convolutional Neural Networks (CNN) architectures including VGG16, VGG19, ResNet, MobileNet, NasNet, and DenseNet. These architectures were used to extract features from the spectrograms. The extracted features were classified using Support Vector Machines (SVM) and the classification performances of the CNN models were evaluated using Area Under the Curve (AUC). Of all the CNN models used in the study, VGG16 had a higher AUC value (0.83 ±â€¯0.05) in the determination of speech laterality compared to all other models. The present study is a pioneer investigation into the determination of speech laterality via EEG signals with machine learning techniques, which, to our knowledge, has never been reported in the literature. Moreover, the classification results obtained in the study are promising and lead the way for subsequent studies though not practically feasible.