Analysis of spike-wave discharges in rats using discrete wavelet transform

Side-Effects of Convulsive Seizures and Anti-Seizure Therapy on Bone in a Rat Model of Epilepsy

The severe sole effects of seizures on the cortical part of bone were reported in our previous study. However, the side effects of anti-epileptic drug therapy on bones has not been differentiated from the effects of the convulsive seizures, yet. This study provides the first report on differentiation of the effects of seizures and carbamazepine (a widely used antiepileptic drug) therapy on bones; 50 mg/kg/day drug was given to genetically induced absence epileptic rats for five weeks. Distinct bone regions including cortical, trabecular, and growth plate in each of tibia, femur, and spine tissues were studied using Fourier transform infrared (FT-IR) imaging and Vickers microhardness test. Blood levels of vitamin D and bone turnover biomarkers were also measured. According to the FT-IR imaging results, both seizure and carbamazepine-treated groups, more dominantly the drug-treated group, had lower mineral content with altered collagen crosslinks and higher crystallinity, implying reduced bone strength. Lower microhardness values also supported lower mechanical strength in bones. The most affected bone tissue and region from seizures and treatment was found as the spine and cortical, respectively. While there was a reduction in vitamin D and calcium levels in both seizure and carbamazepin-treated groups, significantly elevated PTH and bone turnover biomarkers were only seen in the drug-treated group.

Automatic Identification of Interictal Epileptiform Discharges in Secondary Generalized Epilepsy

Ictal epileptiform discharges (EDs) are characteristic signal patterns of scalp electroencephalogram (EEG) or intracranial EEG (iEEG) recorded from patients with epilepsy, which assist with the diagnosis and characterization of various types of epilepsy. The EEG signal, however, is often recorded from patients with epilepsy for a long period of time, and thus detection and identification of EDs have been a burden on medical doctors. This paper proposes a new method for automatic identification of two types of EDs, repeated sharp-waves (sharps), and runs of sharp-and-slow-waves (SSWs), which helps to pinpoint epileptogenic foci in secondary generalized epilepsy such as Lennox-Gastaut syndrome (LGS). In the experiments with iEEG data acquired from a patient with LGS, our proposed method detected EDs with an accuracy of 93.76% and classified three different signal patterns with a mean classification accuracy of 87.69%, which was significantly higher than that of a conventional wavelet-based method. Our study shows that it is possible to successfully detect and discriminate sharps and SSWs from background EEG activity using our proposed method.

TIME-FREQUENCY PROCESSING METHOD OF EPILEPTIC EEG SIGNALS

Wavelet transform decomposition of electroencephalogram (EEG) signals has been widely used for the analysis and detection of epileptic seizure of patients. However, the classification of EEG signals is still challenging because of high nonstationarity and high dimensionality. The aim of this work is an automatic classification of the EEG recordings by using statistical features extraction and support vector machine. From a real database, two sets of EEG signals are used: EEG recorded from a healthy person and from an epileptic person during epileptic seizures. Three important statistical features are computed at different sub-bands discrete wavelet and wavelet packet decomposition of EEG recordings. In this study, to select the best wavelet for our application, five wavelet basis functions are considered for processing EEG signals. After reducing the dimension of the obtained data by linear discriminant analysis and principal component analysis (PCA), feature vectors are used to model and to train the efficient support vector machine classifier. In order to show the efficiency of this approach, the statistical classification performances are evaluated, and a rate of 100% for the best classification accuracy is obtained and is compared with those obtained in other studies for the same dataset. However, this method is not meant to replace the clinician but can assist him for his diagnosis and reinforce his decision.

P50 sensory gating and smoking in the general population

P50 gating is a major functional biomarker in research on schizophrenia and other psychiatric conditions with high smoking prevalence. It is used as endophenotype for studying nicotinic systems genetics and as surrogate endpoint measure for drug development of nicotinic agonists. Surprisingly, little is known about P50 gating in the general population and the relationship to smoking-related characteristics. In this multicenter study at six academic institutions throughout Germany, n=907 never-smokers (NS<20 cigarettes/lifetime), n=463 light smokers (LS) with Fagerström Test for Nicotine Dependence (FTND)≥4 and n=353 heavy smokers (HS, FTND<4) were randomly selected from the general population. As part of a standardized protocol for investigating the genetics of nicotine dependence (ND), an auditory P50 paradigm was applied. The main outcome measure was P50-amplitude difference followed by time-frequency analyses and functional imaging (sLORETA). Reduced P50 gating was found in HS compared to NS with LS taking an intermediate position-correlating with the degree of ND. sLORETA and time-frequency analyses indicate that high-frequency oscillations in frontal brain regions are particularly affected. With growing age, P50 gating increased in (heavy) smokers. This is the first large-scale study (normative sample data) on P50 sensory gating and smoking in the general population. Diminished gating of P50 and associated high-frequency oscillations in the frontal brain region are indications of a deficient inhibitory cortical function in nicotine-dependent smokers. The suitability and application of sensory P50 gating as functional biomarker with regard to genetic and pharmacological studies is discussed.

An algorithm for real-time detection of spike-wave discharges in rodents

The automatic real-time detection of spike-wave discharges (SWDs), the electroencephalographic hallmark of absence seizures, would provide a complementary tool for rapid interference with electrical deep brain stimulation in both patients and animal models. This paper describes a real-time detection algorithm for SWDs based on continuous wavelet analyses in rodents. It has been implemented in a commercially available data acquisition system and its performance experimentally verified. ECoG recordings lasting 5-8h from rats (n=8) of the WAG/Rij strain were analyzed using the real-time SWD detection system. The results indicate that the algorithm is able to detect SWDs within 1s with 100% sensitivity and with a precision of 96.6% for the number of SWDs. Similar results are achieved for 24-h ECoG recordings of two rats. The dependence of accuracy and speed of detection on program settings and attributes of ECoG are discussed. It is concluded that the wavelet based real-time detecting algorithm is well suited for automatic, real-time detection of SWDs in rodents.

Quantifying time-varying multiunit neural activity using entropy based measures

Modern micro-electrode arrays make it possible to simultaneously record population neural activity. However, methods to analyze multiunit activity (MUA), which reflects the aggregate spiking activity of a population of neurons, have remained underdeveloped in comparison to those used for studying single unit activity (SUA). In scenarios where SUA is hard to record and maintain or is not representative of brains response, MUA is informative in deciphering the brains complex time-varying response to stimuli or to clinical insults. Here, we present two quantitative methods of analysis of the time-varying dynamics of MUA without spike detection. These methods are based on the multiresolution discrete wavelet transform (DWT) of an envelope of MUA followed by information theoretic measures: multiresolution entropy (MRE) and the multiresolution Kullback-Leibler distance (MRKLD). We test the proposed quantifiers on both simulated and experimental MUA recorded from rodent cortex in an experimental model of global hypoxic-ischemic brain injury. First, our results validate the use of the envelope of MUA as an alternative to detecting and analyzing transient and complex spike activity. Second, the MRE and MRKLD are shown to respond to dynamic changes due to the brains response to global injury and to identify the transient changes in the MUA.