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Zandi A.S.,University of British Columbia | Javidan M.,University of British Columbia | Javidan M.,Neurophysiology Laboratory | Dumont G.A.,University of British Columbia | Tafreshi R.,Texas A&M University at Qatar
IEEE Transactions on Biomedical Engineering | Year: 2010

A novel wavelet-based algorithm for real-time detection of epileptic seizures using scalp EEG is proposed. In a movingwindow analysis, the EEG from each channel is decomposed by wavelet packet transform. Using wavelet coefficients from seizure and nonseizure references, a patient-specific measure is developed to quantify the separation between seizure and nonseizure states for the frequency range of 1-30 Hz. Utilizing this measure, a frequency band representing themaximum separation between the two states is determined and employed to develop a normalized index, called combined seizure index (CSI). CSI is derived for each epoch of every EEG channel based on both rhythmicity and relative energy of that epoch as well as consistency among different channels. Increasing significantly during ictal states, CSI is inspected using one-sided cumulative sum test to generate proper channel alarms. Analyzing alarms from all channels, a seizure alarm is finally generated. The algorithm was tested on scalp EEG recordings from 14 patients, totaling ∼75.8 h with 63 seizures. Results revealed a high sensitivity of 90.5%, a false detection rate of 0.51 h-1 and a median detection delay of 7 s. The algorithm could also lateralize the focus side for patients with temporal lobe epilepsy. © 2006 IEEE. Source


Zandi A.S.,University of British Columbia | Tafreshi R.,University of British Columbia | Javidan M.,Neurophysiology Laboratory | Dumont G.A.,Texas A&M University at Qatar
2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10 | Year: 2010

A novel real-time patient-specific algorithm to predict epileptic seizures is proposed. The method is based on the analysis of the positive zero-crossing intervals in the scalp electroencephalogram (EEG), describing the brain dynamics. In a moving-window analysis, the histogram of these intervals in each EEG epoch is computed, and the distribution of the histogram value in specific bins, selected using interictal and preictal references, is estimated based on the values obtained from the current epoch and the epochs of the last 5 min. The resulting distribution for each selected bin is then compared to two reference distributions (interictal and preictal), and a seizure prediction index is developed. Comparing this index with a patient-specific threshold for all EEG channels, a seizure prediction alarm is finally generated. The algorithm was tested on ∼15.5 hours of multichannel scalp EEG recordings from three patients with temporal lobe epilepsy, including 14 seizures. 86% of seizures were predicted with an average prediction time of 20.8 min and a false prediction rate of 0.12/hr. © 2010 IEEE. Source


Zandi A.S.,University of British Columbia | Dumont G.A.,University of British Columbia | Javidan M.,University of British Columbia | Javidan M.,Neurophysiology Laboratory | Tafreshi R.,Texas A&M University at Qatar
Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS | Year: 2011

We propose a novel patient-specific method for predicting epileptic seizures by analysis of positive zero-crossing intervals in scalp electroencephalogram (EEG). In real-time analysis, the histogram of these intervals for the current EEG epoch is computed, and the values which correspond to the bins discriminating between interictal and preictal references are selected as an observation. Then, the set of observations from the last 5 min is compared with two reference sets of data points (interictal and preictal) using a variational Gaussian mixture model (GMM) of the data, and a combined index is computed. Comparing this index with a patient-specific threshold, an alarm sequence is produced for each channel. Finally, a seizure prediction alarm is generated according to channel-based information. The proposed method was evaluated using ∼40.3 h of scalp EEG recordings from 6 patients with total of 28 partial seizures. A high sensitivity of 95% was achieved with a false prediction rate of 0.134/h and an average prediction time of 22.8 min for the test dataset. © 2011 IEEE. Source


Pisani V.,University of Rome Tor Vergata | Pisani V.,Neurophysiology Laboratory | Madeo G.,University of Rome Tor Vergata | Madeo G.,Neurophysiology Laboratory | And 10 more authors.
Movement Disorders | Year: 2011

Endocannabinoids (eCBs) are endogenous lipids that bind principally type-1 and type-2 cannabinoid (CB1 and CB2) receptors. N-Arachidonoylethanolamine (AEA, anandamide) and 2-arachidonoylglycerol (2-AG) are the best characterized eCBs that are released from membrane phospholipid precursors through multiple biosynthetic pathways. Together with their receptors and metabolic enzymes, eCBs form the so-called 'eCB system'. The later has been involved in a wide variety of actions, including modulation of basal ganglia function. Consistently, both eCB levels and CB1 receptor expression are high in several basal ganglia regions, and more specifically in the striatum and in its target projection areas. In these regions, the eCB system establishes a close functional interaction with dopaminergic neurotransmission, supporting a relevant role for eCBs in the control of voluntary movements. Accordingly, compelling experimental and clinical evidence suggests that a profound rearrangement of the eCB system in the basal ganglia follows dopamine depletion, as it occurs in Parkinson's disease (PD). In this article, we provide a brief survey of the evidence that the eCB system changes in both animal models of, and patients suffering from, PD. A striking convergence of findings is observed between both rodent and primate models and PD patients, indicating that the eCB system undergoes dynamic, adaptive changes, aimed at restoring an apparent homeostasis within the basal ganglia network. © 2010 Movement Disorder Society. Source


Joensen P.,Neurophysiology Laboratory
Multiple Sclerosis Journal | Year: 2011

Epidemiological studies of multiple sclerosis (MS) conducted in the Faroe Islands identified 10 annual incidences per 100,000 in 1945 and 4.5 in the period 1986-2007. The aim of this study was to estimate the annual incidence of onset of MS in the Faroe Islands in the six decades from 1943 to 2002 and during the period 2003-7.All patients diagnosed with MS between 1943 and 2007 were documented. The incidence of MS before 1943 was around 0.2 per 100,000 annually. During the period 1943-62, an annual incidence rate of 4.4 [confidence interval (CI) 2.9-6.1] per 100,000 was observed. During the 20-year period 1963-82, the level declined significantly (p < 0.001) to around 0.6 (0.3-1.5). Thereafter, an increase was seen during the period 1983-2002, evidencing a significant (p < 0.001) sevenfold increase in the annual incidence to 4.6 (3.2-6.2) per 100,000. Subsequently, for the period 2003-7, there was again a decline to 2.4 (2.1-3.3) (p = 0.003). This study confirms that significant variation in the incidence of MS has occurred in the Faroes over time. Because the rate of genetic change within the Faroese population is relatively slow, the study suggests environmental factors as a contributing cause of MS. If only genetic factors for susceptibility were important, the incidence rate should not wax and wane over time, as is observed. © 2011 The Author(s). Source

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