Early seizure onset prediction in clinical recordings of EEG

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Seizure detection in subdural recordings using nonlinear decision functions

This dissertation proposed a new approach to seizure detection in intracranial EEG recordings using nonlinear decision functions. It implemented well-established features that were designed to deal with complex signals such as brain recordings, and proposed a 2-D domain of analysis. Since the features considered assume both the time and frequency domains, the analysis was carried out both temporally and as a function of different frequency ranges in order to ascertain those measures that were most suitable for seizure detection. In retrospect, this study established a generalized approach to seizure detection that works across several features and across patients. ^ Clinical experiments involved 8 patients with intractable seizures that were evaluated for potential surgical interventions. A total of 35 iEEG data files collected were used in a training phase to ascertain the reliability of the formulated features. The remaining 69 iEEG data files were then used in the testing phase. ^ The testing phase revealed that the correlation sum is the feature that performed best across all patients with a sensitivity of 92% and an accuracy of 99%. The second best feature was the gamma power with a sensitivity of 92% and an accuracy of 96%. In the frequency domain, all of the 5 other spectral bands considered, revealed mixed results in terms of low sensitivity in some frequency bands and low accuracy in other frequency bands, which is expected given that the dominant frequencies in iEEG are those of the gamma band. In the time domain, other features which included mobility, complexity, and activity, all performed very well with an average a sensitivity of 80.3% and an accuracy of 95%. ^ The computational requirement needed for these nonlinear decision functions to be generated in the training phase was extremely long. It was determined that when the duration dimension was rescaled, the results improved and the convergence rates of the nonlinear decision functions were reduced dramatically by more than a 100 fold. Through this rescaling, the sensitivity of the correlation sum improved to 100% and the sensitivity of the gamma power to 97%, which meant that there were even less false negatives and false positives detected. ^

Seizure burden and neurodevelopmental outcome in neonates with hypoxic-ischemic encephalopathy.

Aim: To examine the relationship between electrographic seizures and long-term outcome in neonates with hypoxic-ischemic encephalopathy (HIE). Method: Full-term neonates with HIE born in Cork University Maternity Hospital from 2003 to 2006 (pre-hypothermia era) and 2009 to 2012 (hypothermia era) were included in this observational study. All had early continuous electroencephalography monitoring. All electrographic seizures were annotated. The total seizure burden and hourly seizure burden were calculated. Outcome (normal/abnormal) was assessed at 24 to 48 months in surviving neonates using either the Bayley Scales of Infant and Toddler Development, Third Edition or the Griffiths Mental Development Scales; a diagnosis of cerebral palsy or epilepsy was also considered an abnormal outcome. Results: Continuous electroencephalography was recorded for a median of 57.1 hours (interquartile range 33.5-80.5h) in 47 neonates (31 males, 16 females); 29 out of 47 (62%) had electrographic seizures and 25 out of 47 (53%) had an abnormal outcome. The presence of seizures per se was not associated with abnormal outcome (p=0.126); however, the odds of an abnormal outcome increased over ninefold (odds ratio [OR] 9.56; 95% confidence interval [95% CI] 2.43-37.67) if a neonate had a total seizure burden of more than 40 minutes (p=0.001), and eightfold (OR: 8.00; 95% CI: 2.06-31.07) if a neonate had a maximum hourly seizure burden of more than 13 minutes per hour (p=0.003). Controlling for electrographic HIE grade or treatment with hypothermia did not change the direction of the relationship between seizure burden and outcome. Interpretation: In HIE, a high electrographic seizure burden is significantly associated with abnormal outcome, independent of HIE severity or treatment with hypothermia.

Electrophysiological effects of guanosine and MK-801 in a quinolinic acid-induced seizure model

TORRES, F ; FILHO, M.S. ; ANTUNES, C. ; KALININE, E. ; ANTONIOLLI, E. ; PORTELA, Luis Valmor ; SOUZA, Diogo Onofre ; TORT, A. B. L. . Electrophysiological effects of guanosine and MK-801 in a quinolinic acid-induced seizure model. Experimental Neurology , v. 221, p. 296-306, 2010

Phenobarbital reduces EEG amplitude and propagation of neonatal seizures but does not alter performance of automated seizure detection

Objective: Phenobarbital increases electroclinical uncoupling and our preliminary observations suggest it may also affect electrographic seizure morphology. This may alter the performance of a novel seizure detection algorithm (SDA) developed by our group. The objectives of this study were to compare the morphology of seizures before and after phenobarbital administration in neonates and to determine the effect of any changes on automated seizure detection rates. Methods: The EEGs of 18 term neonates with seizures both pre- and post-phenobarbital (524 seizures) administration were studied. Ten features of seizures were manually quantified and summary measures for each neonate were statistically compared between pre- and post-phenobarbital seizures. SDA seizure detection rates were also compared. Results: Post-phenobarbital seizures showed significantly lower amplitude (p < 0.001) and involved fewer EEG channels at the peak of seizure (p < 0.05). No other features or SDA detection rates showed a statistical difference. Conclusion: These findings show that phenobarbital reduces both the amplitude and propagation of seizures which may help to explain electroclinical uncoupling of seizures. The seizure detection rate of the algorithm was unaffected by these changes. Significance: The results suggest that users should not need to adjust the SDA sensitivity threshold after phenobarbital administration.

Electrophysiological effects of guanosine and MK-801 in a quinolinic acid-induced seizure model

TORRES, F ; FILHO, M.S. ; ANTUNES, C. ; KALININE, E. ; ANTONIOLLI, E. ; PORTELA, Luis Valmor ; SOUZA, Diogo Onofre ; TORT, A. B. L. . Electrophysiological effects of guanosine and MK-801 in a quinolinic acid-induced seizure model. Experimental Neurology , v. 221, p. 296-306, 2010

Seizure and Profound Hypokalemia: Unusual Presentation of Primary Hyperparathyroidism

A 68-year-old man was admitted because of tonic–clonic convulsion. He had been receiving 200 mg itraconazole for 10 days. He had hypokalaemia (2.2 mEq/l), hypercalcaemia (Cacorr 11.0 mg/dl) and elevated serum parathyroid hormone (PTH, 95 pg/ml). Ultrasound examination of the neck revealed a low echoic tumour. Cessation of itraconazole and fluid supplementation eradicated clinical symptoms and profound hypokalaemia, but serum potassium remained low normal (3.4 mEq/l) and the mild hypercalcaemia and elevated PTH were unchanged. To conclude, a small amount of itraconazole (200 mg) precipitated profound hypokalaemia and seizure in a patient with mild hyperparathyroidism and low normal serum potassium.

In-silico optimisation of two-dimensional high performance liquid chromatography for the determination of Australian methamphetamine seizure samples

In-silico optimisation of a two-dimensional high performance liquid chromatography (2D-HPLC) separation protocol has been developed for the interogation of methamphetamine samples including model, real world seizure, and laboratory synthesised samples. The protocol used Drylab® software to rapidly identify the optimum separation conditions from a library of chromatography columns. The optimum separation space was provided by the Phenomonex Kinetex PFP column (first dimension) and an Agilent Poroshell 120 EC-C18 column (second dimension). To facilitate a rapid 2D-HPLC analysis the particle packed C18 column was replaced with a Phenomenex Onyx Monolithic C18 withought sacrificing separation performance. The Drylab® optimised and experimental separations matched very closely, highlighting the robust nature of HPLC simulations. The chemical information gained from an intermediate methamphetamine sample was significant and complimented that generated from a pure seizure sample. The influence of the two-dimensional separation on the analytical figures of merit was also investigated. The limits of detection for key analytes in the second dimension determined for methamphetamine (4.59 × 10-⁴ M), pseudoephedrine (4.03 × 10-4 M), caffeine (5.16 × 10-⁴ M), aspirin (9.32 × 10-4 M), paracetamol (5.93 × 10-4 M) and procaine (2.02 × 10-3 M).

The Treatment of Neonatal Seizures: A Critical Review of the Evidence

In a critical review of the literature to assess the efficacy of monotherapy and subsequent combinant anticonvulsant therapy in the treatment of neonatal seizures, four studies were examined; three randomised control trials and one retrospective cohort study. Each study used phenobarbital for monotherapy with doses reaching a maximum of 40mg/kg. Anticonvulsant drugs used in conjunction with phenobarbitone for combinant therapy included midazolam, clonazepam, lorazepam, phenytoin and lignocaine. Each study used an electroencephalograph for seizure diagnosis and neonatal monitoring when determining therapy efficacy and final outcome assessments. Collectively the studies suggest neither monotherapy nor combinant therapy are entirely effective in seizure control. Monotherapy demonstrated a 29% - 50% success rate for complete seizure control whereas combinant therapy administered after the failure of monotherapy demonstrated a success rate of 43% - 100%. When these trials were combined the overall success for monotherapy was 44% (n = 34/78) and for combinant therapy 72% ( n = 56/78). Though the evidence was inconclusive, it would appear that combinant therapy is of greater benefit to infants unresponsive to monotherapy. Further research such as multi-site randomised controlled trials using standardised criteria and data collection are required within this specialised area.

Analysis of cardiac and epileptic signals using higher order spectra

The theory of nonlinear dyamic systems provides some new methods to handle complex systems. Chaos theory offers new concepts, algorithms and methods for processing, enhancing and analyzing the measured signals. In recent years, researchers are applying the concepts from this theory to bio-signal analysis. In this work, the complex dynamics of the bio-signals such as electrocardiogram (ECG) and electroencephalogram (EEG) are analyzed using the tools of nonlinear systems theory. In the modern industrialized countries every year several hundred thousands of people die due to sudden cardiac death. The Electrocardiogram (ECG) is an important biosignal representing the sum total of millions of cardiac cell depolarization potentials. It contains important insight into the state of health and nature of the disease afflicting the heart. Heart rate variability (HRV) refers to the regulation of the sinoatrial node, the natural pacemaker of the heart by the sympathetic and parasympathetic branches of the autonomic nervous system. Heart rate variability analysis is an important tool to observe the heart's ability to respond to normal regulatory impulses that affect its rhythm. A computerbased intelligent system for analysis of cardiac states is very useful in diagnostics and disease management. Like many bio-signals, HRV signals are non-linear in nature. Higher order spectral analysis (HOS) is known to be a good tool for the analysis of non-linear systems and provides good noise immunity. In this work, we studied the HOS of the HRV signals of normal heartbeat and four classes of arrhythmia. This thesis presents some general characteristics for each of these classes of HRV signals in the bispectrum and bicoherence plots. Several features were extracted from the HOS and subjected an Analysis of Variance (ANOVA) test. The results are very promising for cardiac arrhythmia classification with a number of features yielding a p-value < 0.02 in the ANOVA test. An automated intelligent system for the identification of cardiac health is very useful in healthcare technology. In this work, seven features were extracted from the heart rate signals using HOS and fed to a support vector machine (SVM) for classification. The performance evaluation protocol in this thesis uses 330 subjects consisting of five different kinds of cardiac disease conditions. The classifier achieved a sensitivity of 90% and a specificity of 89%. This system is ready to run on larger data sets. In EEG analysis, the search for hidden information for identification of seizures has a long history. Epilepsy is a pathological condition characterized by spontaneous and unforeseeable occurrence of seizures, during which the perception or behavior of patients is disturbed. An automatic early detection of the seizure onsets would help the patients and observers to take appropriate precautions. Various methods have been proposed to predict the onset of seizures based on EEG recordings. The use of nonlinear features motivated by the higher order spectra (HOS) has been reported to be a promising approach to differentiate between normal, background (pre-ictal) and epileptic EEG signals. In this work, these features are used to train both a Gaussian mixture model (GMM) classifier and a Support Vector Machine (SVM) classifier. Results show that the classifiers were able to achieve 93.11% and 92.67% classification accuracy, respectively, with selected HOS based features. About 2 hours of EEG recordings from 10 patients were used in this study. This thesis introduces unique bispectrum and bicoherence plots for various cardiac conditions and for normal, background and epileptic EEG signals. These plots reveal distinct patterns. The patterns are useful for visual interpretation by those without a deep understanding of spectral analysis such as medical practitioners. It includes original contributions in extracting features from HRV and EEG signals using HOS and entropy, in analyzing the statistical properties of such features on real data and in automated classification using these features with GMM and SVM classifiers.

Drug law enforcement : a review of the evaluation literature

This article describes the results of a systematic review of drug law enforcement evaluations. The authors describe the search procedures and document the results in five main categories: international/national interventions (e.g., interdiction and drug seizure), reactive/ directed interventions (e.g., crackdowns, raids, buy-busts, saturation patrol, etc.), proactive/ partnership interventions (e.g., third-party policing, problem-oriented policing, community policing, drug nuisance abatement, etc.), individualized interventions (e.g., arrest referral and diversion), or interventions that used a combination of reactive/directed and proactive/ partnership strategies. Results indicate that proactive interventions involving partnerships between the police and third parties and/or community entities appear to be more effective at reducing both drug and nondrug problems in drug problem places than are reactive/ directed approaches. But the general quality of research in drug law enforcement is poor, the range of interventions that have been evaluated is limited, and more high-quality research is needed across a greater variety of drug interventions.

Application of higher order spectra to identify epileptic EEG

Epilepsy is characterized by the spontaneous and seemingly unforeseeable occurrence of seizures, during which the perception or behavior of patients is disturbed. An automatic system that detects seizure onsets would allow patients or the people near them to take appropriate precautions, and could provide more insight into this phenomenon. Various methods have been proposed to predict the onset of seizures based on EEG recordings. The use of nonlinear features motivated by the higher order spectra (HOS) has been reported to be a promising approach to differentiate between normal, background (pre-ictal) and epileptic EEG signals. In this work, we made a comparative study of the performance of Gaussian mixture model (GMM) and Support Vector Machine (SVM) classifiers using the features derived from HOS and from the power spectrum. Results show that the selected HOS based features achieve 93.11% classification accuracy compared to 88.78% with features derived from the power spectrum for a GMM classifier. The SVM classifier achieves an improvement from 86.89% with features based on the power spectrum to 92.56% with features based on the bispectrum.

Myoinositol Abnormalities in Temporal Lobe Epilepsy

Purpose: This study used magnetic resonance spectroscopy (MRS) to examine metabolite abnormalities in the temporal and frontal lobe of patients with temporal lobe epilepsy (TLE) of differing severity. Methods: We investigated myoinositol in TLE by using short-echo MRS in 34 TLE patients [26 late onset (LO-TLE), eight hippocampal sclerosis (HS-TLE)], and 16 controls. Single-voxel short-echo (35 ms) MR spectra of temporal and frontal lobes were acquired at 1.5 T and analyzed by using LCModel. Results: The temporal lobe ipsilateral to seizure origin in HS-TLE, but not LO-TLE, had reduced N-acetylaspartate (NA) and elevated myoinositol (MI; HS-TLE NA, 7.8 ± 1.9 mM, control NA, 9.2 ± 1.3 mM; p < 0.05; HS-TLE MI, 6.1 ± 1.6 mM, control mI 4.9 ± 0.8 mM, p< 0.05). Frontal lobe MI was low in both patient groups (LO-TLE, 4.3 ± 0.8 mM; p < 0.05; HS-TLE, 3.6 ±.05 mM; p < 0.001; controls, 4.8 ± 0.5 mM). Ipsilateral frontal lobes had lower MI (3.8 ± 0.7 mM; p < 0.01) than contralateral frontal lobes (4.3 ± 0.8 mM; p < 0.05). Conclusions: MI changes may distinguish between the seizure focus, where MI is increased, and areas of seizure spread where MI is decreased.

Platinum-based chemotherapy in metastatic breast cancer : current status

Cisplatin and carboplatin are active in previously untreated patients with metastatic breast cancer (MBC) with mean response rates (RRs) of 50 and 32%, respectively. In pretreated patients the RR to cisplatin/carboplatin monotherapy declines markedly to <10%. Cisplatin and carboplatin have been combined with many other cytotoxics. In first-line setting high activity has been observed in combination with taxanes or vinorelbine (RRs consistently ∼60%). It appears that these newer combinations are superior to older regimens with etoposide (RRs 30 to 50%) or 5-fluorouracil (RRs 40 to 60%). Cisplatin-/carboplatin-based regimens with infusional 5-FU and epirubicin/paclitaxel/vinorelbine achieve high RRs of around 60 to 80%. However these regimens are difficult to administer in all patients because they require central venous access for continuous 5-FU infusion. In pretreated MBC the combinations of cisplatin-taxane/vinorelbine/gemcitabine or carboplatin-docetaxel/vinorelbine yield RRs of 40 to 50%, which are higher than those achieved with platinum-etoposide/5-FU. In locally advanced disease cisplatin-based regimens achieve very high RRs (>80%). This would suggest that in chemotherapy-naïve patients platinum-based therapy might have an important role to play. Additionally the synergy demonstrated between platinum compounds, taxanes and herceptin, in preclinical and clinical studies is of immense importance and the results of the two ongoing Breast Cancer International Research Group randomized phase III studies are eagerly awaited. These studies may help clarify the role of platinum compounds in the treatment of metastatic and possibly early breast cancer. © 2003 Elsevier Ltd. All rights reserved.

Survey : video forensic tools

Due to extension of using CCTVs and the other video security systems in all areas, these sorts of devices have been introduced as the most important digital evidences to search and seizure crimes. Video forensics tools are developed as a part of digital forensics tools to analyze digital evidences and clear vague points of them for presenting in the courts Existing video forensics tools have been facilitated the investigation process by providing different features based on various video editing techniques. In this paper, some of the most popular video forensics tools are discussed and the strengths and shortages of them are compared and consequently, an alternative framework which includes the strengths of existing popular tools is introduced.