Abstract: To determine (1) whether heart rate variability (HRV) was a sensitive and reliable measure in mental effort tasks carried out by healthy seniors and (2) whether non-linear approaches to HRV analysis, in addition to traditional time and frequency domain approaches were useful to study such effects.
Abstract: Epilepsy is associated with imbalance of sympathetic and parasympathetic activity which may lead to sudden unexplained death in epilepsy (SUDEP). Well-controlled (WcE) and intractable epilepsy (IE) subjects may present different autonomic profiles, which can be helpful in explaining the predisposition of the latter to SUDEP.
Abstract: An attempt has been made to study the appropriateness of electrical impedance plethysmography (EIP) in detecting pulmonary health status. A feasibility study was conducted on normal, obstructive and restrictive subjects aged between 20 and 60 years. Quantitative assessment of pulmonary function was made by way of both EIP and pulmonary function tests (PFT). Amongst the various EIP parameters, a statistically significant difference was observed for the respiratory band power, between normal (129.7) and obstructive (35.8) subjects, indicating that this EIP variable can be used to distinguish between the two pulmonary function states. A significant positive correlation was observed between a spirometry parameter, peak expiratory flow rate (PEFR), and an EIP parameter, respiratory amplitude (r = 0.372, p < 0.05), thereby indicating that EIP information by way of respiratory amplitude is comparable to that provided by PEFR. Hence, both respiratory amplitude and respiratory band power were seen to provide useful information on pulmonary health status.
Abstract: The paper presents a novel computationally simple, easy-to-interpret compressed EEG display for multichannel intracranial EEG recordings. The compressed display is based on the level of sharp activity (relative sharpness index (RSI)) in the EEG, which profoundly increases during paroxysmal activities. RSI is graphically presented as a color-intensity plot that allows compressing several hours of EEG into a single display page. RSI display is a bird's-eye-view of the EEG that may reveal seizure evolution ('build-up'), seizure precursors, or sites associated with the seizures. We present examples from two patients to illustrate the method's ability to identify epileptogenic sites that may be difficult to observe in the conventional review process. RSI is compared with the color density spectral array (CDSA) and amplitude integrated EEG (aEEG) display. Examples demonstrate the RSI display to be simple, easy to interpret, computationally light and fast enough for online application.
Abstract: This paper presents a novel, unsupervised spike classification algorithm for intracranial EEG. The method combines template matching and principal component analysis (PCA) for building a dynamic patient-specific codebook without a priori knowledge of the spike waveforms. The problem of misclassification due to overlapping classes is resolved by identifying similar classes in the codebook using hierarchical clustering. Cluster quality is visually assessed by projecting inter- and intra-clusters onto a 3D plot. Intracranial EEG from 5 patients was utilized to optimize the algorithm. The resulting codebook retains 82.1% of the detected spikes in non-overlapping and disjoint clusters. Initial results suggest a definite role of this method for both rapid review and quantitation of interictal spikes that could enhance both clinical treatment and research studies on epileptic patients.
Abstract: Most of the automatic seizure detection schemes reported in the literature are complex for detecting seizures that are of (a) short duration, (b) minimal amplitude evolution, or (c) non-rhythmic mixed frequency epileptic activity. We present a novel morphology-based classifier to detect epileptic seizures for intracranial EEG recording. The method characterizes epileptic seizure by detecting continual presence of sharp half-waves in the EEG. Performance is evaluated on single channel intracranial EEG of seven patients, and compared to two previously developed methods for intracranial EEG recordings by our research group. The method detects seizure of varying types (rhythmic, non-rhythmic, short- and long- seizures) with a sensitivity of 100%, a false detection rate of 0.1/h and an average onset delay of 9.1 s. The method outperforms the two previously developed methods and is computationally simple for real-time application. Preliminary results on seven patients data are very promising.
Abstract: A patient-specific model-based seizure detection method using statistically optimal null filters (SONF) has been recently proposed to aid the review of long-term EEG [1, 2]. The method relies on the model of a priori known seizure (template pattern) for subsequent detection of similar seizures. Artifacts, non-epileptic EEG rhythms, and at times modeling errors lead to increased false or missed detections. In this paper, we present a new improved model-based seizure detection that introduces a pre-processing block for artifact rejection, an adaptive technique of modeling the template patterns, and a new evolution-based classifier. The proposed classifier tracks the temporal evolution of seizure to improve the classification accuracy. With the help of simulated EEG, we illustrate the significance and need for these modifications. Further, performance of the complete algorithm is tested on single channel depth EEG of seven patients, and compared with the previous approaches. In terms of sensitivity and specificity, the proposed method resulted in 84% and 100%, method of [1] 65% and 84%, and method of [2], 84% and 90% respectively. An overall performance improvement is seen as enhanced detection sensitivity and reduced false positives. This is preliminary result on seven patient data.
Abstract: In long-term monitoring of electroencephalogram (EEG) for epilepsy, it is crucial for the seizure detection systems to have high sensitivity and low false detections to reduce uninteresting and redundant data that may be stored for review by the medical experts. However, a large number of features and the complex decision boundaries for classification of seizures eventually lead to a trade-off between sensitivity and false detection rate (FDR). Thus, no single classifier can fulfill the requirements of high sensitivity with a low FDR and at the same time be a computationally efficient system suitable for real-time application. We present a novel, simple, computationally efficient seizure detection system to enhance the sensitivity with a low FDR by proposing a dual-stage classifier. This overall system consists of a pre-processing unit, a feature extraction unit and a novel dual-stage classifier. The first stage of the proposed classifier detects all true seizures, but also many false patterns, whereas the second stage of the proposed classifier minimizes false detections by rejecting patterns that may be artifacts. The performance of the novel seizure detection system has been evaluated on 300 hours of single-channel depth electroencephalogram (SEEG) recordings obtained from fifteen patients. An overall improvement has been observed in terms of sensitivity, specificity and FDR.
