Effect of multiscale PCA de-noising on EMG signal classification for diagnosis of neuromuscular disorders

Different approaches have been applied for quantitative analysis of EMG signals. This study introduces the effect of Multiscale Principal Component Analysis (MSPCA) denoising method in ElectroMyoGram (EMG) signal classification. The effect of the MSPCA denoising method discussed on EMG signal classification. In addition, effect of Multiple Single Classification (MUSIC) feature extraction method presented and compared for the classification of EMG signals. The results were accomplished on the basis of EMG signal data to classify into normal, ALS or myopathic. Furthermore, total accuracy of classifiers such as k-Nearest Neighbor (k-NN), Artificial Neural Network (ANN) and Support Vector Machines (SVMs) were discussed. Significant results were found by using MSPCA denoising method. The comparisons between the developed classifiers were based on a number of scalar performances such as sensitivity, specificity, accuracy, F-measure and area under ROC curve (AUC). The results show that MSPCA de-noising has considerably increased the accuracy as compared to EMG data without MSPCA de-noising.     

Primary malignant melanoma of the nasal cavity

Primary malignant melanoma of the nose and paranasal sinus mucosa is a rare disease and seen in less than 1% among all melanomas. Malignant melanomas have 2 origins: cutaneous and mucosal. The mucosal form has a worse prognosis because of its aggressiveness compared with that of the cutaneous form. Mucosal melanomas often occur at a rate of 2% to 3% among all melanomas and are typically found in the nasal cavity and paranasal sinuses. Generally, it is more common in males and in those older than 50 years. In this study, 4 patients were observed at the Cumhuriyet University Faculty of Medicine; 2 of them were a 64-year-old man and an 82-year-old woman who had a malignant melanoma originating from the nasal septal mucosa, 1 patient was a 72-year-old woman whose malignant melanoma originated from the inferior turbinate, and 1 patient was a 77-year-old woman with a sinonasally located melanoma. The conditions of these patients were discussed under the light of literature instructions.     

Application of adaptive neuro-fuzzy inference system for epileptic seizure detection using wavelet feature extraction

Intelligent computing tools such as artificial neural network (ANN) and fuzzy logic approaches are demonstrated to be competent when applied individually to a variety of problems. Recently, there has been a growing interest in combining both these approaches, and as a result, neuro-fuzzy computing techniques have been evolved. In this study, a new approach based on an adaptive neuro-fuzzy inference system (ANFIS) was presented for epileptic seizure detection. The proposed ANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach. Decision making was performed in two stages: feature extraction using the wavelet transform (WT) and the ANFIS trained with the backpropagation gradient descent method in combination with the least squares method. Some conclusions concerning the impacts of features on the detection of epileptic seizures were obtained through analysis of the ANFIS. The results are highly promising, and a comparative analysis suggests that the proposed modeling approach outperforms ANN model in terms of training performances and classification accuracies. The results confirmed that the proposed ANFIS model has some potential in epileptic seizure detection. The ANFIS model achieved accuracy rates which were higher than that of the stand-alone neural network model.     

Primary leptomeningeal melanomatosis successfully treated with PD-1 inhibitor pembrolizumab: A case report

Rationale:Primary leptomeningeal melanoma is an extremely rare disease of the central nervous system. There are no standard treatment protocols with a poor prognosis in very few reported cases. Immunotherapy in primary brain melanoma has not been successfully applied so far.Patient concerns:We describe a female patient 72-year-old diagnosed in the Neurosurgery Department which presented with generalized seizures.Diagnoses:Histological examination confirmed atypical melanocytes immunohistochemically positive for melan A, HMB45 and S-100 protein in the meninges, BRAF V600E negative. Dermatological, ophthalmological examinations, and 18-FDG PET/CT were negative.Interventions:The patient was successfully treated with pembrolizumab 2 mg/kg every 3 weeks for 2 years.Outcomes:The disease was stable for 2 years and the patient had no significant toxicity.Lessons:Our report describes durable intracranial tumor response suggesting the efficacy of PD-1 inhibitor pembrolizumab for central nervous system primary leptomeningeal melanoma.     

Muscle Fatigue Detection in EMG Using Time–Frequency Methods, ICA and Neural Networks

The electromyography (EMG) signals give information about different features of muscle function. Real-time measurements of EMG have been used to observe the dissociation between the electrical and mechanical measures that occurs with fatigue. The purpose of this study was to detect fatigue of biceps brachia muscle using time–frequency methods and independent component analysis (ICA). In order to realize this aim, EMG activity obtained from activated muscle during a phasic voluntary movement was recorded for 14 healthy young persons and EMG signals were observed in time–frequency domain for determination of fatigue. Time–frequency methods are used for the processing of signals that are non-stationary and time varying. The EMG contains transient signals related to muscle activity. The proposed method for the detection of muscle fatigue is automated by using artificial neural networks (ANN). The results show that ANN with ICA separates EMG signals from fresh and fatigued muscles, hence providing a visualization of the onset of fatigue over time. The system is adaptable to different subjects and conditions since the techniques used are not subject or workload regime specific.     

Application of Classical and Model-Based Spectral Methods to Describe the State of Alertness in EEG

Electrophysiological recordings are considered a reliable method of assessing a person's alertness. Sleep medicine is asked to offer objective methods to measure daytime alertness, tiredness and sleepiness. In this study, EEG signals recorded from 30 subjects were processed by PC-computer using classical and model-based methods. The classical method (fast Fourier transform) and three model-based methods (Burg autoregresse, moving average, least-squares modified Yule–Walker autoregressive moving average methods) were selected for processing EEG signals to discriminate the alertness level of subject. Power spectra of EEG signals were obtained by using these spectrum analysis techniques. These EEG spectra were then used to compare the applied methods in terms of their frequency resolution and the effects in determination of vigilance state of subject. It is found that, FFT and MA methods have low spectral resolution, these two methods are not appropriate for the analysis of the a wake–sleep correlation. Burg AR and least-squares modified Yule–Walker ARMA methods' performance characteristics have been found extremely valuable for the determination of vigilance state of a healthy subject, because of their clear spectra.     

Synthesis and antimicrobial activity of some novel derivatives of benzofuran: part 1. Synthesis and antimicrobial activity of (benzofuran-2-yl)(3-phenyl-3-methylcyclobutyl) ketoxime derivatives

The reaction of salicylaldehyde with 1-phenyl-1-methyl-3-(2-chloro-1-oxoethyl) cyclobutane (1) and potassium carbonate was used to prepare (benzofuran-2-yl)(3-methyl-3-phenylcyclobutyl) methanone (2) for the starting reagent purposes. (benzofuran-2-yl)(3-phenyl-3-methyl cyclobutyl) ketoxime (3) was synthesized from the reaction of the compound (2) with hidroxylamine. New derivatives of (benzofuran-2-yl)(3-phenyl-3-methyl cyclobutyl) ketoxime (3) such as, O-glycidylketoxime (4) and O-phenylacylketoxime (5a-c) were obtained very high yields. Alkyl, allyl and aryl substituted N-oxime ethers (6a-e) were obtained from the reaction compound 3 and various halogen contained compounds. The syntheses of the compounds (7a-f) were carried out from the reaction of the compound (4) and different amines such as, isopropyl amine, natrium azide, morpholine and piperazine. All of the synthesized compounds were tested for antimicrobial activity against Staphylococcus aureus ATCC 6538, Staphylococcus epidermidis ATCC 12228, Escherichia coli ATCC 8739, Klebsiella pneumoniae ATCC 4352, Pseudomonas aeruginosa ATCC 1539, Salmonella typhi, Shigella flexneri, Proteus mirabilis ATCC 14153 and Candida albicans ATCC 10231. Among the synthesized compounds (benzofuran-2-yl)(3-phenyl-3-methyl cyclobutyl)-O-[2-hydroxy-3-(N-methylpiperazino)] propylketoxime (7d) was found the most active derivative against S. aureus ATCC 6538. The compounds 2, 5b, 6b, 6c, 7b and 7f showed very strong and the same antimicrobial effect against C. albicans ATCC 10231. Similarly (benzofuran-2-yl)(3-phenyl-3-methylcyclobutyl)-O-benzylketoxime 6a showed good antimicrobial effect against C. albicans ATCC 10231. None of the other compounds exhibited activity against the other test microorganisms.     

Neural Networks with Periodogram and Autoregressive Spectral Analysis Methods in Detection of Epileptic Seizure

Approximately 1% of the people in the world suffer from epilepsy. Careful analyses of the electroencephalograph (EEG) records can provide valuable insight and improved understanding of the mechanisms causing epileptic disorders. Predicting the onset of epileptic seizure is an important and difficult biomedical problem, which has attracted substantial attention of the intelligent computing community over the past two decades. The purpose of this work was to investigate the performance of the periodogram and autoregressive (AR) power spectrum methods to extract classifiable features from human electroencephalogram (EEG) by using artificial neural networks (ANN). The feedforward ANN system was trained and tested with the backpropagation algorithm using a large data set of exemplars. We present a method for the automatic comparison of epileptic seizures in EEG, allowing the grouping of seizures having similar overall patterns. Each channel of the EEG is first broken down into segments having relatively stationary characteristics. Features are then calculated for each segment, and all segments of all channels of the seizures of a patient are grouped into clusters of similar morphology. This clustering allows labeling of every EEG segment. Examples from 5 patients with scalp electrodes illustrate the ability of the method to group seizures of similar morphology. It was observed that ANN classification of EEG signals with AR preprocessing gives better results, and these results can also be used for the deduction of epileptic seizure.