Dynamics of regional brain activity in epilepsy: a cross-disciplinary study on both intracranial and scalp-recorded epileptic seizures.

OBJECTIVE: Recent cross-disciplinary literature suggests a dynamical analogy between earthquakes and epileptic seizures. This study extends the focus of inquiry for the applicability of models for earthquake dynamics to examine both scalp-recorded and intracranial electroencephalogram recordings related to epileptic seizures. APPROACH: First, we provide an updated definition of the electric event in terms of magnitude and we focus on the applicability of (i) a model for earthquake dynamics, rooted in a nonextensive Tsallis framework, (ii) the traditional Gutenberg and Richter law and (iii) an alternative method for the magnitude-frequency relation for earthquakes. Second, we apply spatiotemporal analysis in terms of nonextensive statistical physics and we further examine the behavior of the parameters included in the nonextensive formula for both types of electroencephalogram recordings under study. MAIN RESULTS: We confirm the previously observed power-law distribution, showing that the nonextensive formula can adequately describe the sequences of electric events included in both types of electroencephalogram recordings. We also show the intermittent behavior of the epileptic seizure cycle which is analogous to the earthquake cycles and we provide evidence of self-affinity of the regional electroencephalogram epileptic seizure activity. SIGNIFICANCE: This study may provide a framework for the analysis and interpretation of epileptic brain activity and other biological phenomena with similar underlying dynamical mechanisms.

Multimodality GPU-based computer-assisted diagnosis of breast cancer using ultrasound and digital mammography images.

PURPOSE: To improve the computer-aided diagnosis of breast lesions, by designing a pattern recognition system (PR-system) on commercial graphics processing unit (GPU) cards using parallel programming and textural information from multimodality imaging. MATERIAL AND METHODS: Patients with histologically verified breast lesions underwent both ultrasound (US) and digital mammography (DM), lesions were outlined on the images by an experienced radiologist, and textural features were calculated. The PR-system was designed to provide highest possible precision by programming in parallel the multiprocessors of the NVIDIA's GPU cards, GeForce 8800GT or 580GTX, and using the CUDA programming framework and C++. The PR-system was built around the probabilistic neural network classifier, and its performance was evaluated by a re-substitution method, for estimating the system's highest accuracy, and by the external cross-validation method, for assessing the PR-system's unbiased accuracy to new, "unseen" by the system, data. RESULTS: Classification accuracies for discriminating malignant from benign lesions were as follows: 85.5 % using US-features alone, 82.3 % employing DM features alone, and 93.5 % combining US and DM features. Mean accuracy to new "unseen" data for the combined US and DM features was 81 %. Those classification accuracies were about 10 % higher than accuracies achieved on a single CPU, using sequential programming methods, and 150-fold faster. CONCLUSION: The proposed PR-system improves breast-lesion discrimination accuracy, it may be redesigned on site when new verified data are incorporated in its depository, and it may serve as a second opinion tool in a clinical environment.

Self-organised critical features in soil radon and MHz electromagnetic disturbances: results from environmental monitoring in Greece.

This paper addresses the issues of self-organised critical behaviour of soil-radon and MHz-electromagnetic disorders during intense seismic activity in SW Greece. A significant radon signal is re-analysed for environmental influences with Fast Fourier Transform and multivariate statistics. Self-organisation of signals is investigated via fractal evolving techniques and detrended fluctuation analysis. New lengthy radon data are presented and analysed accordingly. The data did not show self-similarities. Similar analysis applied to new important concurrent MHz-electromagnetic signals revealed analogous behaviour to radon. The signals precursory value is discussed.

Real time decision support system for diagnosis of rare cancers, trained in parallel, on a graphics processing unit.

In the present study a new strategy is introduced for designing and developing of an efficient dynamic Decision Support System (DSS) for supporting rare cancers decision making. The proposed DSS operates on a Graphics Processing Unit (GPU) and it is capable of adjusting its design in real time based on user-defined clinical questions in contrast to standard CPU implementations that are limited by processing and memory constrains. The core of the proposed DSS was a Probabilistic Neural Network classifier and was evaluated on 140 rare brain cancer cases, regarding its ability to predict tumors' malignancy, using a panel of 20 morphological and textural features Generalization was estimated using an external 10-fold cross-validation. The proposed GPU-based DSS achieved significantly higher training speed, outperforming the CPU-based system by a factor that ranged from 267 to 288 times. System design was optimized using a combination of 4 textural and morphological features with 78.6% overall accuracy, whereas system generalization was 73.8%±3.2%. By exploiting the inherently parallel architecture of a consumer level GPU, the proposed approach enables real time, optimal design of a DSS for any user-defined clinical question for improving diagnostic assessments, prognostic relevance and concordance rates for rare cancers in clinical practice.

Environmental monitoring of radon in soil during a very seismically active period occurred in South West Greece.

This paper focuses on the environmental monitoring of radon in soil as a potential trace gas in the search of earthquake precursors. The paper reports the following: (a) Pre-monitoring experiments. (b) Set-up of methods and devices. (c) Active and passive monitoring results concentrating on two extremely-strong radon anomalies (~ 500 kBq m(-3)). (e) Discussion regarding the employed ± 2σ technique for identifying radon disturbances. (f) Application of wavelet-power-spectrum fractal analysis for detecting power-law behaviour. The strong anomalies exhibited anti-persistent power-law-beta-values (b = (1.8 ± 0.2), b = (1.8 ± 0.3)) significantly higher than those of the baseline. Persistent b-values were also detected. The findings comply with a self-organised-critical pre-earthquake state. (h) Discussion on models that interpret the radon anomalies focusing on the recently-proposed asperity-model. (i) Application of a recent technique which showed that the two strong disturbances were proportional to the strain change. It was concluded that the strong radon disturbances may be linked to the strong earthquake of 8/6/2008, M = 6.5, occurred 29 km away from the installed instrumentation.