Pjm Pierre Cluitmans

Long-term postoperative cognitive dysfunction in the elderly: ISPOCD1 study

BACKGROUND Long-term postoperative cognitive dysfunction may occur in the elderly. Age may be a risk factor and hypoxaemia and arterial hypotension causative factors. We investigated these hypotheses in an international multicentre study. METHODS 1218 patients aged at least 60 years completed neuropsychological tests before and 1 week and 3 months after major non-cardiac surgery. We measured oxygen saturation by continuous pulse oximetry before surgery and throughout the day of and the first 3 nights after surgery. We recorded blood pressure every 3 min by oscillometry during the operation and every 15-30 min for the rest of that day and night. We identified postoperative cognitive dysfunction with neuropsychological tests compared with controls recruited from the UK (n=176) and the same countries as study centres (n=145). FINDINGS Postoperative cognitive dysfunction was present in 266 (25.8% [95% CI 23.1-28.5]) of patients 1 week after surgery and in 94 (9.9% [8.1-12.0]) 3 months after surgery, compared with 3.4% and 2.8%, respectively, of UK controls (p<0.0001 and p=0.0037, respectively). Increasing age and duration of anaesthesia, little education, a second operation, postoperative infections, and respiratory complications were risk factors for early postoperative cognitive dysfunction, but only age was a risk factor for late postoperative cognitive dysfunction. Hypoxaemia and hypotension were not significant risk factors at any time. INTERPRETATION Our findings have implications for studies of the causes of cognitive decline and, in clinical practice, for the information given to patients before surgery.

A higher illuminance induces alertness even during office hours: Findings on subjective measures, task performance and heart rate measures

Nocturnal white light exposure has shown marked results on subjective and objective indicators of alertness, vitality and mood, yet effects of white light during daytime and under usual office work conditions have not been investigated extensively. The current study employed a mixed-group design (N=32), testing effects of two illuminance levels (200lx or 1000lx at eye level, 4000K) during one hour of morning versus afternoon exposure. In four repeated blocks, subjective reports, objective performance and physiological arousal were measured. Results showed effects of illuminance on subjective alertness and vitality, sustained attention in tasks, and heart rate and heart rate variability. Participants felt less sleepy and more energetic in the high versus the low lighting condition, had shorter reaction times on the psychomotor vigilance task and increased physiological arousal. Effects of illuminance on the subjective measures, as well as those on heart rate were not dependent on time of day or duration of exposure. Performance effects were most pronounced in the morning sessions and towards the end of the one-hour exposure period. The effect on heart rate variability was also most pronounced at the end of the one-hour exposure. The results demonstrate that even under normal, i.e., neither sleep nor light deprived conditions, more intense light can improve feelings of alertness and vitality, as well as objective performance and physiological arousal.

Clinical evaluation of a method for automatic detection and removal of artifacts in auditory evoked potential monitoring

Objective. The objective of our study was to evaluate the method for detection and removal of artifacts in evoked potential monitoring described earlier by Cluitmans and colleagues in a clinical setting.Methods. The method for detection and removal of artifacts by Cluitmans and colleagues is based on the assumption that a sweep of the recorded electroencephalogram (EEG) signal contains artifacts if one or more variables derived from the signal deviates strongly from the normal range of values. Once these normal ranges are defined, all future EEG recordings that are recorded under comparable circumstances can be automatically evaluated for artifacts by tracking when one or more signal variables falls outside the normal range. To assess the performance of this method in a clinical setting, recordings from a learning set were visually evaluated for artifacts. From the empirical distribution functions of the signal variables, the thresholds for automatic detection of artifacts were determined. The auditory evoked potential (AEP) waveforms resulting after automatic screening were compared with the waveforms obtained after visual evaluation of the raw signal combined with manual exclusion of signal periods containing artifacts.Results. The quality of the resulting waveform was improved by our method of automatic detection and removal of artifacts in 97% of partly contaminated recordings. In only 2% of the recordings, automatic screening slightly degraded the resulting waveform.Conclusions. We conclude that the described method of automatic detection and removal of artifacts in AEP recordings effectively improves the quality of the resulting AEP waveform, without excessive rejection of artifact-free signal periods. The signal variables used in this method seem appropriate for distinguishing artifact-free signal periods from periods containing artifacts for the types of artifact that were studied.

Context related artefact detection in prolonged EEG recordings

The need for reliable detection of artefacts in raw and processed EEG is widely acknowledged. Although different EEG analysis systems have been described, only few general applicable artefact recognition techniques have emerged. This paper tackles the problem of artefact detection in seven 24 h EEG recordings in the intensive care unit. ICU recordings have received less attention than, e.g. epilepsy monitoring, although recordings in this environment present an interesting application area. The EEG data used here was recorded during the difficult circumstances of an explorative ICU study. The data set includes a diverse set of EEG patterns, as well as EEG artefacts. The study investigates objective artefact detection methods based on statistical differences between signal parameters, using time-varying autoregressive modelling (AR) and Slope detection. In addition to matching the performance of artefact detection against two human observers, the study focuses on the optimal settings for context incorporation by testing the algorithms for different time windows and epoch lengths. Results indicate that a relatively short period (20-40 s) provides sufficient context information for the methods used. The combined AR and Slope detection parameters yielded good performance, detecting approximately 90% of the artefacts as indicated by the consensus score of the human observers.

An investigation of the phase locking index for measuring of interdependency of cortical source signals recorded in the EEG

The phase locking index (PLI) was introduced to quantify in a statistical sense the phase synchronization of two signals. It has been commonly used to process biosignals. In this article, we investigate the PLI for measuring the interdependency of cortical source signals (CSSs) recorded in the Electroencephalogram (EEG). To this end, we consider simple analytical models for the mapping of simulated CSSs into the EEG. For these models, the PLI is investigated analytically and through numerical simulations. An evaluation is made of the sensitivity of the PLI to the amount of crosstalk between the sources through biological tissues of the head. It is found that the PLI is a useful interdependency measure for CSSs, especially when the amount of crosstalk is small. Another common interdependency measure is the coherence. A direct comparison of both measures has not been made in the literature so far. We assess the performance of the PLI and coherence for estimation and detection purposes based on, respectively, a normalized variance and a novel statistical measure termed contrast. Based on these performance measures, it is found that the PLI is similar or better than the CM in most cases. This result is also confirmed through analysis of EEGs recorded from epileptic patients.

An algorithm for the automatic detection of seizures in neonatal amplitude‐integrated EEG

Aim: To develop and evaluate an algorithm for the automatic screening of electrographic neonatal seizures (ENS) in amplitude‐integrated electroencephalography (aEEG) signals.

Automated detection of tonic seizures using 3-D accelerometry

A first approach is presented for the detection of accelerometry (ACM) patterns associated with tonic seizures. First it is shown that during tonic seizures the typical ACM-pattern is mainly caused by change of position towards the field of gravity and that the acceleration caused by movement is negligible. To this end a mechanical model of the arm and physiological information about muscle contraction during tonic seizures are used. Then six features are computed that represent the main characteristics of ACM-patterns associated with tonic seizures. Linear discriminant analysis is used for classification. For training and evaluation ACM-data are used from mentally retarded patients with severe epilepsy. It was possible to detect tonic seizures with a success rate around 0.80 and with a positive predictive value (PPV) of 0.35. For off-line analysis this is acceptable, especially when 42 % of the false alarms are actually motor seizures of another type. The missed seizures, were not clearly visible in the ACM-signal. For these seizures additional ACM-sensors or a combination with other sensor types might be necessary. The results show that our approach is useful for the automated detection of tonic seizures and that it is a promising contribution in a complete multi-sensor seizure detection setup.

Higher light intensity induces modulations in brain activity even during regular daytime working hours

We investigated the effect of exposure to bright white light as compared to a commonly experienced illuminance (1000 lx vs. 200 lx at eye level, 4000 K) on electroencephalography spectral power density during daytime. Spectral power density was measured during one hour of exposure in the morning and in the afternoon. Results showed a lower relative power density in the theta range under bright light. In the morning, relative alpha power was also lower under exposure to 1000 lx. The current findings extend earlier results on the effect of illuminance on alertness and arousal in the late evening and at night. Moreover, they largely corroborate results on subjective experience and sustained attention during daytime, and together suggest higher alertness under brighter light even for daytime exposure in everyday situations.

Epileptic seizure detection on patients with mental retardation based on EEG features : a pilot study

Mental retardation (MR) is one of the most common secondary disabilities in people with Epilepsy. However, to our knowledge there are no reliable seizure detection methods specified for MR-patients. In this paper we performed a pilot study on a group of six patients with mental retardation to assess what EEG features potentially work well on this group. A group of EEG features on the time, frequency and spatio-temporal domain were extracted, the modified wrapper approach was then employed as an improved feature subset selection method. Results show high variance on obtained features subset across this group, meanwhile there exist some common features which characterize the high-frequency components of epileptic EEG signals.

Seizure detection using dynamic warping for patients with intellectual disability

Electroencephalography (EEG) is paramount for both retrospective analysis and real-time monitoring of epileptic seizures. Studies have shown that EEG-based seizure detection is very difficult for a specific epileptic population with intellectual disability due to the cerebral development disorders. In this work, a seizure detection method based on dynamic warping (DW) is proposed for patients with intellectual disability. It uses an EEG template of an individual subjects dominant seizure type, to extract the morphological features from EEG signals. A linear discriminant analysis (LDA) classifier is used to perform the seizure detection. Results show that the DW-based feature in the frequency domain is superior than that in the time domain, and the features extracted using wavelet transform method.