P.J.M. Cluitmans

The potential value of three-dimensional accelerometry for detection of motor seizures in severe epilepsy

Seizure detection results based on the visual analysis of three-dimensional (3D) accelerometry (ACM) and video/EEG recordings are reported for 18 patients with severe epilepsy. They were monitored for 36 hours during which 897 seizures were detected. This was seven times higher than the number of seizures reported by nurses during the registration period. The results in this article demonstrate that 3D ACM is a valuable sensing method for seizure detection in this population. Four hundred twenty-eight (48%) seizures were detected by ACM. With 3D ACM alone it was possible to detect all the seizures in 10 of the 18 patients. Three-dimensional ACM also was complementary to EEG in our population. ACM patterns during seizures were stereotypical in 95% of the motor seizures. These characteristic patterns are a starting point for automated seizure detection.

A spatio-temporal dipole model of the readiness potential in humans. I. Finger movement

Readiness potentials (RP) have been recorded in 9 subjects who performed voluntary unilateral plantar flexions with the right or left foot. These show a paradoxical ipsilateral dominance. Spatio-temporal dipole models were obtained for these data, by iterative parameter estimation. The non-uniqueness of the inverse problem leads to several models which describe the data almost equally well, and which all pass orthogonality tests for the individual residuals and source waves. In these dipole models the ipsilateral preponderance is attributed to generators in the contralateral hemisphere, which agrees with results from MEG recording. According to these models the main generators of the RP are in the primary motor cortex, one bilaterally in its posterior wall and the other in the contralateral crown. This agrees with earlier results for finger RPs. However, for foot RPs, it was difficult to distinguish individual sub-components in both the observed scalp potentials and the estimated temporal activation patterns of the dipoles. Some of the presented models include a fronto-central dipole which possibly represents activity of the supplementary motor area. It is concluded that this finding is at best suggestive and needs further investigation.

Time-Frequency Analysis of Accelerometry Data for Detection of Myoclonic Seizures

Four time-frequency and time-scale methods are studied for their ability of detecting myoclonic seizures from accelerometric data. Methods that are used are: the short-time Fourier transform (STFT), the Wigner distribution (WD), the continuous wavelet transform (CWT) using a Daubechies wavelet, and a newly introduced model-based matched wavelet transform (MOD). Real patient data are analyzed using these four time-frequency and time-scale methods. To obtain quantitative results, all four methods are evaluated in a linear classification setup. Data from 15 patients are used for training and data from 21 patients for testing. Using features based on the CWT and MOD, the success rate of the classifier was 80%. Using STFT or WD-based features, the classification success is reduced. Analysis of the false positives revealed that they were either clonic seizures, the onset of tonic seizures, or sharp peaks in “normal” movements indicating that the patient was making a jerky movement. All these movements are considered clinically important to detect. Thus, the results show that both CWT and MOD are useful for the detection of myoclonic seizures. On top of that, MOD has the advantage that it consists of parameters that are related to seizure duration and intensity that are physiologically meaningful. Furthermore, in future work, the model can also be useful for the detection of other motor seizure types.

Sympathetic activity and hypothalamo-pituitary–adrenal axis activity during sleep in post-traumatic stress disorder : a study assessing polysomnography with simultaneous blood sampling

BACKGROUND Nightmares and insomnia in PTSD are hallmark symptoms, yet poorly understood in comparison to the advances toward a biological framework for the disorder. According to polysomnography (PSG), only minor changes in sleep architecture were described. This warrants alternative methods for assessing sleep regulation in PTSD. METHODS After screening for obstructive sleep apnea and period limb movement disorder, veterans with PTSD (n=13), trauma controls (TCs, n=17) and healthy controls (HCs, n=15) slept in our sleep laboratory on two consecutive nights with an IV catheter out of which blood was sampled every 20min from 22:00h to 08:00h. Nocturnal levels of plasma adrenocorticotropic hormone (ACTH), cortisol, melatonin were assessed in conjunction with PSG registration, as well as subjective sleep parameters. RESULTS PTSD patients showed a significant increase in awakenings during sleep in comparison to both control groups. These awakenings were correlated with ACTH levels during the night, and with the subjective perception of sleep depth. Also, heart rate (HR) was significantly increased in PTSD patients as compared with both control groups. The diurnal regulation of ACTH, cortisol and melatonin appeared undisturbed. PTSD patients exhibited lower cortisol levels at borderline significance (p=0.056) during the first half of the night. ACTH levels and cortisol levels during the first half of the night were inversely related to slow wave sleep (SWS). CONCLUSION This study suggests that hypothalamo-pituitary-adrenal (HPA) axis activity is related to sleep fragmentation in PTSD. Also, activity of the sympathetic nervous system (SNS) is increased during sleep in PTSD. Further research is necessary to explore the potential causal relationship between sleep problems and the activity of the HPA-axis and SNS in PTSD.

Detection of Subtle Nocturnal Motor Activity From 3-D Accelerometry Recordings in Epilepsy Patients

This paper presents a first step towards reliable detection of nocturnal epileptic seizures based on 3-D accelerometry (ACM) recordings. The main goal is to distinguish between data with and without subtle nocturnal motor activity, thus reducing the amount of data that needs further (more complex) analysis for seizure detection. From 15 ACM signals (measured on five positions on the body), two features are computed, the variance and the jerk. In the resulting 2-D feature space, a linear threshold function is used for classification. For training and testing, the algorithm ACM data along with video data is used from nocturnal registrations in seven mentally retarded patients with severe epilepsy. Per patient, the algorithm detected 100% of the periods of motor activity that are marked in video recordings and the ACM signals by experts. From all the detections, 43%-89% was correct (mean=65%). We were able to reduce the amount of data that need to be analyzed considerably. The results show that our approach can be used for detection of subtle nocturnal motor activity. Furthermore, our results indicate that our algorithm is robust for fluctuations across patients. Consequently, there is no need for training the algorithm for each new patient.

Event-related desynchronization related to the anticipation of a stimulus providing knowledge of results

In the present paper, event-related desynchronization (ERD) in the alpha and beta frequency bands is quantified in order to investigate the processes related to the anticipation of a knowledge of results (KR) stimulus. In a time estimation task, 10 subjects were instructed to press a button 4 s after the presentation of an auditory stimulus. Two seconds after the response they received auditory or visual feedback on the timing of their response. Preceding the button press, a centrally maximal ERD is found. Preceding the visual KR stimulus, an ERD is present that has an occipital maximum. Contrary to expectation, preceding the auditory KR stimulus there are no signs of a modality-specific ERD. Results are related to a thalamo-cortical gating model which predicts a correspondence between negative slow potentials and ERD during motor preparation and stimulus anticipation.

Resting-state networks and dissociation in psychogenic non-epileptic seizures

OBJECTIVE Psychogenic non-epileptic seizures (PNES) are epilepsy-like episodes which have an emotional rather than organic origin. Although PNES have often been related to the process of dissociation, the psychopathology is still poorly understood. To elucidate underlying mechanisms, the current study applied independent component analysis (ICA) on resting-state fMRI to investigate alterations within four relevant networks, associated with executive, fronto-parietal, sensorimotor, and default mode activation, and within a visual network to examine specificity of between-group differences. METHODS Twenty-one patients with PNES without psychiatric or neurologic comorbidities and twenty-seven healthy controls underwent resting-state functional MR imaging at 3.0T (Philips Achieva). Additional neuropsychological testing included Ravens Matrices test and dissociation questionnaires. ICA with dual regression was used to identify resting-state networks in all participants, and spatial maps of the networks of interest were compared between patients and healthy controls. RESULTS Patients displayed higher dissociation scores, lower cognitive performance and increased contribution of the orbitofrontal, insular and subcallosal cortex in the fronto-parietal network; the cingulate and insular cortex in the executive control network; the cingulate gyrus, superior parietal lobe, pre- and postcentral gyri and supplemental motor cortex in the sensorimotor network; and the precuneus and (para-) cingulate gyri in the default-mode network. The connectivity strengths within these regions of interest significantly correlated with dissociation scores. No between-group differences were found within the visual network, which was examined to determine specificity of between-group differences. CONCLUSIONS PNES patients displayed abnormalities in several resting-state networks that provide neuronal correlates for an underlying dissociation mechanism.

Non-linear algorithms for processing biological signals

This paper illustrates different approaches to the analysis of biological signals based on non-linear methods. The performance of such approaches, despite the greater methodological and computational complexity is, in many instances, more successful compared to linear approaches, in enhancing important parameters for both physiological studies and clinical protocols. The methods introduced employ median filters for pattern recognition, adaptive segmentation, data compression, prediction and data modelling as well as multivariate estimators in data clustering through median learning vector quantizers. Another approach described uses Wiener-Volterra kernel technique to obtain a satisfactory estimation and causality test among EEG recordings. Finally, methods for the assessment of non-linear dynamic behaviour are discussed and applied to the analysis of heart rate variability signal. In this way invariant parameters are studied which describe non-linear phenomena in the modelling of the physiological systems under investigation.

Neurophysiological correlates of dissociative symptoms

Objective Dissociation is a mental process with psychological and somatoform manifestations, which is closely related to hypnotic suggestibility and essentially shows the ability to obtain distance from reality. An increased tendency to dissociate is a frequently reported characteristic of patients with functional neurological symptoms and syndromes (FNSS), which account for a substantial part of all neurological admissions. This review aims to investigate what heart rate variability (HRV), EEG and neuroimaging data (MRI) reveal about the nature of dissociation and related conditions. Methods Studies reporting HRV, EEG and neuroimaging data related to hypnosis, dissociation and FNSS were identified by searching the electronic databases Pubmed and ScienceDirect. Results The majority of the identified studies concerned the physiological characteristics of hypnosis; relatively few investigations on dissociation related FNSS were identified. General findings were increased parasympathetic functioning during hypnosis (as measured by HRV), and lower HRV in patients with FNSS. The large variety of EEG and functional MRI investigations with diverse results challenges definite conclusions, but evidence suggests that subcortical as well as (pre)frontal regions serve emotion regulation in dissociative conditions. Functional connectivity analyses suggest the presence of altered brain networks in patients with FNSS, in which limbic areas have an increased influence on motor preparatory regions. Conclusions HRV, EEG and (functional) MRI are sensitive methods to detect physiological changes related to dissociation and dissociative disorders such as FNSS, and can possibly provide more information about their aetiology. The use of such measures could eventually provide biomarkers for earlier identification of patients at risk and appropriate treatment of dissociative conditions.

Event-related potential measures of information processing during general anesthesia

To investigate the incidence and manner of auditory information processing during a state of presumed unconsciousness event-related brain potentials (ERPs) were studied in 41 patients undergoing cardiac surgery with propofol/alfentanil anesthesia. The ERPs were recorded during auditory oddball tasks administered before and within several periods of the operation. Mean nasopharyngeal temperature and anesthetic concentrations were determined for each intraoperative ERP recording epoch. During anesthesia ERP waves could still be observed up to 500 ms after stimulus onset indicating that auditory information processing was not suppressed completely by the administered anesthetic agents. Relative to the preoperative recordings, the P1-N1-P2 complex was delayed and more positive going during anesthesia. Comparable changes in ERP morphology have been observed during Stage II-IV sleep, suggesting parallels in the mechanisms underlying early auditory processing in both states of reduced arousal level, possibly related to a selective reduction of a non-specific activity. N1 and P2 peak amplitudes were found to be larger for the deviant tones compared to the standard tones. These amplitude differences most likely reflect automatic detection of stimulus deviance, although it cannot be excluded entirely that they were due to differences in refractoriness. Anesthetic concentrations and nasopharyngeal temperature were found to be of minor significance for ERP control. It is suggested that ERPs could serve as intraoperative reference measures, providing the earliest evidence for auditory processing. This characteristic is important for validation of signals and techniques that are proposed to improve conventional monitoring of anesthesia with respect to detecting unintended awareness.