Improving classification of epileptic and non-epileptic EEG events by feature selection

This is the Accepted Manuscript version of the following article: E. Pippa, et al, “Improving classification of epileptic and non-epileptic EEG events by feature selection”, Neurocomputing, Vol. 171: 576-585, July 2015. The final published version is available at: http://www.sciencedirect.com/science/article/pii/S0925231215009509?via%3Dihub Copyright © 2015 Elsevier B.V.Correctly diagnosing generalized epileptic from non-epileptic episodes, such as psychogenic non epileptic seizures (PNES) and vasovagal or vasodepressor syncope (VVS), despite its importance for the administration of appropriate treatment, life improvement of the patient, and cost reduction for patient and healthcare system, is rarely tackled in the literature. Usually clinicians differentiate between generalized epileptic seizures and PNES based on clinical features and video-EEG. In this work, we investigate the use of machine learning techniques for automatic classification of generalized epileptic and non-epileptic events based only on multi-channel EEG data. For this purpose, we extract the signal patterns in the time domain and in the frequency domain and then combine all features across channels to characterize the spatio-temporal manifestation of seizures. Several classification algorithms are explored and evaluated on EEG epochs from 11 subjects in an inter-subject cross-validation setting. Due to large number of features feature ranking and selection is performed prior to classification using the ReliefF ranking algorithm within two different voting strategies. The classification models using feature subsets, achieved higher accuracy compared to the models using all features reaching 95% (Bayesian Network), 89% (Random Committee) and 87% (Random Forest) for binary classification (epileptic versus non-epileptic). The results demonstrate the competitiveness of this approach as opposed to previous methods.Peer reviewe

Chimeric Stimuli-Responsive Liposomes as Nanocarriers for the Delivery of the Anti-Glioma Agent TRAM-34

Nanocarriers are delivery platforms of drugs, peptides, nucleic acids and other therapeutic molecules that are indicated for severe human diseases. Gliomas are the most frequent type of brain tumor, with glioblastoma being the most common and malignant type. The current state of glioma treatment requires innovative approaches that will lead to efficient and safe therapies. Advanced nanosystems and stimuli-responsive materials are available and well-studied technologies that may contribute to this effort. The present study deals with the development of functional chimeric nanocarriers composed of a phospholipid and a diblock copolymer, for the incorporation, delivery and pH-responsive release of the antiglioma agent TRAM-34 inside glioblastoma cells. Nanocarrier analysis included light scattering, protein incubation and electron microscopy, and fluorescence anisotropy and thermal analysis techniques were also applied. Biological assays were carried out in order to evaluate the nanocarrier nanotoxicity in vitro and in vivo, as well as to evaluate antiglioma activity. The nanosystems were able to successfully manifest functional properties under pH conditions, and their biocompatibility and cellular internalization were also evident. The chimeric nanoplatforms presented herein have shown promise for biomedical applications so far and should be further studied in terms of their ability to deliver TRAM-34 and other therapeutic molecules to glioblastoma cells

Dissecting the Shared Genetic Architecture of Suicide Attempt, Psychiatric Disorders, and Known Risk Factors

Background Suicide is a leading cause of death worldwide, and nonfatal suicide attempts, which occur far more frequently, are a major source of disability and social and economic burden. Both have substantial genetic etiology, which is partially shared and partially distinct from that of related psychiatric disorders. Methods We conducted a genome-wide association study (GWAS) of 29,782 suicide attempt (SA) cases and 519,961 controls in the International Suicide Genetics Consortium (ISGC). The GWAS of SA was conditioned on psychiatric disorders using GWAS summary statistics via multitrait-based conditional and joint analysis, to remove genetic effects on SA mediated by psychiatric disorders. We investigated the shared and divergent genetic architectures of SA, psychiatric disorders, and other known risk factors. Results Two loci reached genome-wide significance for SA: the major histocompatibility complex and an intergenic locus on chromosome 7, the latter of which remained associated with SA after conditioning on psychiatric disorders and replicated in an independent cohort from the Million Veteran Program. This locus has been implicated in risk-taking behavior, smoking, and insomnia. SA showed strong genetic correlation with psychiatric disorders, particularly major depression, and also with smoking, pain, risk-taking behavior, sleep disturbances, lower educational attainment, reproductive traits, lower socioeconomic status, and poorer general health. After conditioning on psychiatric disorders, the genetic correlations between SA and psychiatric disorders decreased, whereas those with nonpsychiatric traits remained largely unchanged. Conclusions Our results identify a risk locus that contributes more strongly to SA than other phenotypes and suggest a shared underlying biology between SA and known risk factors that is not mediated by psychiatric disorders.Peer reviewe

Data fusion for paroxysmal events' classification from EEG

International audienceSpatiotemporal analysis of electroencephalography is commonly used for classification of events since it allows capturing dependencies across channels. The significant increase of feature vector dimensionality however introduce noise and thus it does not allow the classification models to be trained using a limited number of samples usually available in clinical studies.Thus, we investigate the classification of epileptic and non-epileptic events based on temporal and spectral analysis through the application of three different fusion schemes for the combination of information across channels. We compare the commonly used early-integration (EI) scheme - in which features are fused from all channels prior to classification - with two late-integration (LI) schemes performing per channel classification when: (i) the temporal context varies significantly across channels, thus local spatial training models are required, and (ii) the spatial variations are negligible in comparison to the inter-subject variation, thus only the temporal variation is modeled using a single global spatial training model. Furthermore, we perform dimensionality reduction either by feature selection or by principal component analysis.The framework is applied on events that manifest across most channels, as generalized epileptic seizures, psychogenic non-epileptic seizures and vasovagal syncope. The three classification architectures were evaluated on EEG epochs from 11 subjects.Although direct comparison with other studies is difficult due to the different characteristics of each dataset, the achieved recognition accuracy of the LI fusion schemes outperforms the performance reported in the literature.The best scheme was the LI with global model which achieved 97% accuracy

EEG-based Classification of Epileptic and Non-epileptic Events using Multi-array Decomposition

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The Use of Crystalline Carbon-Based Nanomaterials (CBNs) in Various Biomedical Applications

This review study aims to present, in a condensed manner, the significance of the use of crystalline carbon-based nanomaterials in biomedical applications. Crystalline carbon-based nanomaterials, encompassing graphene, graphene oxide, reduced graphene oxide, carbon nanotubes, and graphene quantum dots, have emerged as promising materials for the development of medical devices in various biomedical applications. These materials possess inorganic semiconducting attributes combined with organic π-π stacking features, allowing them to efficiently interact with biomolecules and present enhanced light responses. By harnessing these unique properties, carbon-based nanomaterials offer promising opportunities for future advancements in biomedicine. Recent studies have focused on the development of these nanomaterials for targeted drug delivery, cancer treatment, and biosensors. The conjugation and modification of carbon-based nanomaterials have led to significant advancements in a plethora of therapies and have addressed limitations in preclinical biomedical applications. Furthermore, the wide-ranging therapeutic advantages of carbon nanotubes have been thoroughly examined in the context of biomedical applications

Marine-Originated Materials and Their Potential Use in Biomedicine

Aquatic habitats cover almost 70% of the Earth, containing several species contributing to marine biodiversity. Marine and aquatic organisms are rich in chemical compounds that can be widely used in biomedicine (dentistry, pharmacy, cosmetology, etc.) as alternative raw biomaterials or in food supplements. Their structural characteristics make them promising candidates for tissue engineering approaches in regenerative medicine. Thus, seaweeds, marine sponges, arthropods, cnidaria, mollusks, and the biomaterials provided by them, such as alginate, vitamins, laminarin, collagen, chitin, chitosan, gelatin, hydroxyapatite, biosilica, etc., are going to be discussed focusing on the biomedical applications of these marine-originated biomaterials. The ultimate goal is to highlight the sustainability of the use of these biomaterials instead of conventional ones, mainly due to the antimicrobial, anti-inflammatory, anti-aging and anticancer effect