Convolutional neural network propagation on electroencephalographic scalograms for detection of schizophrenia.

OBJECTIVE: Electroencephalographic analysis (EEG) has emerged as a powerful tool for brain state interpretation. Studies have shown distinct deviances of patients with schizophrenia in EEG activation at specific frequency bands. METHODS: Evidence is presented for the validation of a Convolutional Neural Network (CNN) model using transfer learning for scalp EEGs of patients and controls during the performance of a speeded sensorimotor task and a working memory task. First, we trained a CNN on EEG data of 41 schizophrenia patients (SCZ) and 31 healthy controls (HC). Secondly, we used a pretrained model for training. Both models were tested in an external validation set of 15 SCZ, 16 HC, and 12 first-degree relatives. RESULTS: Using the layer-wise relevance propagation on the classification decision, a heatmap was produced for each subject, specifying the pixel-wise relevance. The CNN model resulted in the first case in a balanced accuracy of 63.7% and 81.5% in the second case, on the external validation test 64.5% and 83.2%, respectively. CONCLUSIONS: The theta and alpha frequency bands of the EEG signals had significant relevance to the CNN classification decision and predict the first-degree relatives indicating potential heritable functional deviances. SIGNIFICANCE: The proposed methodology results in important advancements for the identification of biomarkers in schizophrenia heritability.

Identifying Differences in the P600 Component of ERP-Signals between OCD Patients and Controls Employing a PNN-based Majority Vote Classification Scheme.

In the present study an attempt was made to focus in the differences between Obsessive-Compulsive Disorder (OCD) patients and healthy controls, as reflected by the P600 component of event-related potential (ERP) signals, to locate brain areas that may be related to Working Memory (WM) deficits. Neuropsychological research has yielded contradicting results regarding WM in OCD. Eighteen patients with OCD symptomatology and 20 normal controls (age and sex matched) were subjected to a computerized version of the digit span Wechsler test. EEG activity was recorded from 15 scalp electrodes (leads). A dedicated computer software was developed to read the ERP signals and to calculate features related to the ERP P600 component (500-800 ms). Nineteen features were generated, from each ERP-signal and each lead, and were employed in the design of the Probabilistic Neural Network (PNN) classifier. Highest single-lead precision (86.8%) was found at the Fp2 and C6 leads. When the output from all single-lead PNN classifiers fed a Majority Vote Engine (MVE), the system classified correctly all subjects, providing a powerful classification scheme. Findings indicated that OCD patients differed from normal controls at the prefrontal and temporo-central brain regions.

Upgrading undergraduate biomedical engineering laboratory training.

This paper presents the upgrading of biomedical engineering laboratory training at the Department of Medical Instrumentation Technology of the Technological Educational Institution of Athens (TEI-A), taking place in the framework of the "Upgrading of Undergraduate Curricula of TEI-A" project. The educational material of selected specialized laboratory sectors is totally renewed, and new sectors are introduced, so that student-centered learning is promoted utilizing advanced computer-enhanced educational environments. The current implementation status is presented for the laboratories dealing with biosignal acquisition, medical data digital processing and, more extensively, computer networks applications in medicine, where a training application simulating a Radiology Department computer network was developed. Benefits of the use of a balanced training approach, combining hands-on experience with computer simulations, are discussed.

Design and implementation of an SVM-based computer classification system for discriminating depressive patients from healthy controls using the P600 component of ERP signals.

A computer-based classification system has been designed capable of distinguishing patients with depression from normal controls by event-related potential (ERP) signals using the P600 component. Clinical material comprised 25 patients with depression and an equal number of gender and aged-matched healthy controls. All subjects were evaluated by a computerized version of the digit span Wechsler test. EEG activity was recorded and digitized from 15 scalp electrodes (leads). Seventeen features related to the shape of the waveform were generated and were employed in the design of an optimum support vector machine (SVM) classifier at each lead. The outcomes of those SVM classifiers were selected by a majority-vote engine (MVE), which assigned each subject to either the normal or depressive classes. MVE classification accuracy was 94% when using all leads and 92% or 82% when using only the right or left scalp leads, respectively. These findings support the hypothesis that depression is associated with dysfunction of right hemisphere mechanisms mediating the processing of information that assigns a specific response to a specific stimulus, as those mechanisms are reflected by the P600 component of ERPs. Our method may aid the further understanding of the neurophysiology underlying depression, due to its potentiality to integrate theories of depression and psychophysiology.

Upgrading the biomedical engineering undergraduate curriculum based on current trends in higher education.

This paper presents the reforming of the curriculum of the Department of Medical Instrumentation Technology at the Technological Educational Institution of Athens (TEI-A), as inspired by current trends in higher education. The reforming is taking place in the framework of the "Upgrading of Undergraduate Curricula of TEI-A" project The project-funded upgrading focuses on a core of eight laboratory sectors, with particular emphasis placed on student-centered learning, taking advantage of computer-enhanced educational environment. The existing and proposed curricula are compared. The student workload in the proposed curriculum is reduced, while maintaining an extensive set of basic and applied knowledge related to biomedical engineering. The overall aim is to provide a curriculum that will help in developing multi-skilled individuals that can relate to the demands of this field within a dynamic social and economical environment.

Independent component analysis applied to the P600 component of event-related potentials.

The analysis of the P600 component of Event-related Potentials (ERPs) has attracted attention due to its relation to covert cognitive mechanisms, in connection to memory processes. The component may often be low-amplitude, compared to other components such as the P300. Independent component analysis (ICA) techniques have been successfully applied in ERP processing, in the framework of blind source separation (BSS) for unmixing recorded potentials into a sum of temporally independent and spatially fixed components. In the present work ICA was used for reconstructing averaged ERPs in the time window of the P600 component, selecting a subset of independent components' projections to the original electrode recording positions. The selection is based on two empirical criteria, selecting the projection that reconstructs a P600 nearest temporally to the original P600, or selecting the projection combination - up to a preselected maximum number of combined projections providing maximum reconstructed P600 amplitude. The techniques are tested on ERPs recorded from healthy subjects and psychiatric patients, notably improving the differentiation of the two groups, based on either the amplitude or the latency of the reconstructed P600 component, in comparison to results achieved using the original ERPs.

Simulated generation of evoked potentials components using networks with distinct excitatory and inhibitory neurons.

Long latency evoked potentials (EP's) are electrical potentials related to brain information processing mechanisms. In this paper, three-layered neurophysiologically based artificial neural network model is presented whose neurons obey to Dale's law. The first two layers of the network can memorize and recall sparsely coded patterns, oscillating at biologically plausible frequencies. Excitatory low-pass filtering synapses, from the second to the third layer, create evoked current dipoles, when the network retrieves memories related to stimuli. Based on psychophysiological indications, simulated intracranial dipoles are straightforwardly transformed into long latency EP components such as N100 and P300 that match laboratory-measured scalp EP's.

Psychophysiological evaluation of short-term neurotoxicity after prophylactic brain irradiation in patients with small cell lung cancer: a study of event related potentials.

BACKGROUND: The aim of this study was to show, whether a certain prophylacting applicable radiation affects the cognition, particularly, the specific cognitive components P50, N100, P300 and N400 of auditory event related potentials (ERPs) during a short memory test. METHODS AND MATERIALS: Eleven patients with small cell lung cancer (SCLC), who had presented complete response of disease after chemotherapy and radical radiotherapy in the lung, were prescribed to receive a prophylacting cranial irradiation (PCI) with a 6 MeV linear accelerator. The dose schedule was consisting of a total dose up to 30 Gy in 10 fractions, within 12 days (5 days a week). The psychophysiological approach before and after PCI was assessed by measurements of the auditory ERPs during a short memory performance using the digit-span Wechsler test. Components of ERP were recorded from 15 scalp electrodes. Additionally, symptomatology of depression and anxiety were assessed using Zung Self-Rating Depression Scale and Spielberger Anxiety Inventory, respectively, for pre- and post-PCI. RESULTS: No significant difference was noticed pre- and post-radiotherapy of all particular level of psychophysiological analysis concerning both the latencies and the amplitudes of ERPs auditory components P50, N100, P300 and N400 (P > 0.05, Wilcoxon signed test). Additionally, no changes were found with regard to behavioral performance (memory recall), depression symptomatology and state anxiety, according to pre- and post-radiation measurements. However, the self-reported depression symptomatology showed that the patients presented moderate depression. CONCLUSION: No short-term psychophysiological neurotoxicity was detected with this PCI schedule using these instruments, lending additional support to evidence suggesting the benefit of this certain PCI schedule for patients with SCLC.

Inversion of simulated evoked potentials to charge distribution inside the human brain using an algebraic reconstruction technique.

An analytic method is presented to estimate the evolution of electrical charge distribution inside the human brain related to the evoked potentials observed on the head surface. A three-layer concentric spherical human head model is adopted to express the relation between the observed potentials on the head surface and the spatial charge distribution inside the brain. An integral equation associated with the three-layer concentric head model Green's function is employed. Assuming the electric potentials are measured on the head surface, the charge distributions inside the human brain are computed by solving an inverse problem. The Green's function integral equation is inverted by using an algebraic reconstruction technique widely employed in X-ray tomography imaging. The accuracy of the proposed technique is examined by employing computer simulations and by checking the self-consistency of the algorithm.