A simplified method for relapsing-remitting multiple sclerosis detection: Insights from resting EEG signals.

BACKGROUND AND OBJECTIVE: Multiple sclerosis (MS) is a neurodegenerative autoimmune disease affecting the central nervous system, leading to various neurological symptoms. Early detection is paramount to prevent enduring damage during MS episodes. Although magnetic resonance imaging (MRI) is a common diagnostic tool, this study aims to explore the feasibility of using electroencephalography (EEG) signals for MS detection, considering their accessibility and ease of application compared to MRI. METHODS: The study involved the analysis of EEG signals during rest from 17 MS patients and 27 healthy volunteers to investigate MS-healthy patterns. Power spectral density features (PSD) were extracted from the 32-channel EEG signals. The study employed Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), Classification and Regression Trees (CART), and k-Nearest Neighbor (kNN) classifiers to identify channels with the highest accuracy. Notably, the study achieved 100% accuracy in MS detection using the "Fp1" and "Pz" channels with the LDA classifier. A statistical analysis, utilizing the independent sample t-test, was conducted to explore whether PSD features of these channels differed significantly between healthy individuals and those with MS. RESULTS: The results of the study demonstrate that effective detection of MS can be achieved using PSD features from only two channels of the EEG signal. Specifically, the "Fp1" and "Pz" channels exhibited 100% accuracy in MS detection with the LDA classifier. The statistical analysis further explored and confirmed the significant differences in PSD features between healthy individuals and MS patients. CONCLUSION: The study concludes that the proposed method, utilizing PSD features from specific EEG channels, offers a straightforward and efficient diagnostic approach for the effective detection of MS. The findings suggest the potential utility of EEG signals as a non-invasive and accessible alternative for MS detection, highlighting the importance of further research in this direction.

Determination of blood glucose parameter from human blood serum by using a quartz crystal microbalance sensor coated with phthalocyanines compounds.

Determining the blood glucose level is important for the prevention and treatment of diabetes mellitus. We developed a sensor system using Quartz Crystal Microbalance (QCM) to determine the blood glucose level from human blood serum. This study consists of two experimental stages: artificial glucose/pure water solution tests and human blood serum tests. In the first stage of the study, the QCM sensor with the highest performance was identified using artificial glucose solution concentrations. In the second stage of the study, human blood serum measurements were performed using QCM to determine blood glucose levels. QCM sensors were coated with phthalocyanines (Pcs) by jet spray method. The blood glucose values of 96 volunteers, which ranged from 71 mg/dL to 329 mg/dL, were recorded. As a result of the study, human glucose values were determined with an average error of 3.25%.

Quantitative classification of HbA1C and blood glucose level for diabetes diagnosis using neural networks.

In this study, artificial neural network structures were used for the quantitative classification of Haemoglobin A1C and blood glucose level for diabetes diagnosis as a non-invasive measurement technique. The neural network structures make inferences from the relationship between the palm perspiration and blood data values. For this purpose, feed forward multilayer, Elman, and radial basis neural network structures were used. The quartz crystal microbalance type and humidity sensors were used for the detection of palm perspiration rates. Total 297 volunteer's data is used in this study. Three quarters of the data was used to train the neural networks. The remaining data were used as test data. The best results for the quantitative classification were obtained from the feed forward NN structure for the detection of the glucose and HbA1C level quantities. And, the performances of all neural networks for the HbA1C value were better than the performances of these neural networks for the glucose level.

Log energy entropy-based EEG classification with multilayer neural networks in seizure.

In this study, normal EEG series recorded from healthy volunteers and epileptic EEG series recorded from patients within and without seizure are classified by using Multilayer Neural Network (MLNN) architectures with respect to several time domain entropy measures such as Shannon Entropy (ShanEn), Log Energy Entropy (LogEn), and Sample Entropy (Sampen). In tests, the MLNN is performed with several numbers of neurons for both one hidden layer and two hidden layers. The results show that segments in seizure have significantly lower entropy values than normal EEG series. This result indicates an important increase of EEG regularity in epilepsy patients. The LogEn approach, which has not been experienced in EEG classification yet, provides the most reliable features into the EEG classification with very low absolute error as 0.01. In particular, the MLNN can be proposed to distinguish the seizure activity from the seizure-free epileptic series where the LogEn values are considered as signal features that characterize the degree of EEG complexity. The highest classification accuracy is obtained for one hidden layer architecture.

E-Nose system for anesthetic dose level detection using artificial neural network.

In this study, an E-Nose system was realized for the anesthetic dose level prediction. For this purpose, sevoflurane anesthetic agent was measured using the E-Nose system implemented with sensor array of quartz crystal microbalances (QCM). In surgeries, anesthetic agents are given to the patients with carrier gases of oxygen (02) and nitrous oxide (N20). Frequency changes on QCM sensors to the eight sevoflurane anesthetic dose levels were recorded via RS-232 serial port. A multilayer feed forward artificial neural network (MLNN) structure was used to provide the relationship between the frequency change and the anesthetic dose level. The MLNNs were trained with the measured data using Levenberg-Marquardt algorithm. Then, the trained MLNNs were tested with random data. The results have showed that, acceptable anesthetic dose level predictions have been obtained successfully.

A fuzzy logic-based decision support system on anesthetic depth control for helping anesthetists in surgeries.

In this study, a fuzzy logic-based anesthetic depth decision support system (ADDSS) was realized for anesthetic depth control to help anesthetists in surgeries. Depth of anesthesia for a patient can change according to anesthetic agent and characteristic properties of a patient such as age, weight, etc. During the surgery, depth of anesthesia of a patient is determined by the experience of anesthetist controlling of systolic arterial pressure (SAP) and heart pulse rate (HPR) parameters. Anesthetists could have tired and lost attention by inhaling of anesthetic gas leaks in long lasted operations. For that reason, improper anesthetic depth could be applied to the patients. So anesthesia could not be safety and comfortable. To remove this unwanted situation, an ADDSS was proposed for anesthetists. By the help of this system, precise anesthetic depth could have provided. Thus, the anesthetist will spend less time to provide anesthetic and the patient will have a safer and less expensive operation. This study was performed under sevoflurane anesthetic.