Gerhard H. Visser

Automated neonatal seizure detection mimicking a human observer reading EEG

OBJECTIVE The description and evaluation of a novel patient-independent seizure detection for the EEG of the newborn term infant. METHODS We identified characteristics of neonatal seizures by which a human observer is able to detect them. Neonatal seizures were divided into two types. For each type, a fully automated detection algorithm was developed based on the identified human observer characteristics. The first algorithm analyzes the correlation between high-energetic segments of the EEG. The second detects increases in low-frequency activity (<8 Hz) with high autocorrelation. RESULTS The complete algorithm was tested on multi-channel EEG recordings of 21 patients with and 5 patients without electrographic seizures, totaling 217 h of EEG. Sensitivity of the combined algorithms was found to be 88%, Positive Predictive Value (PPV) 75% and the false positive rate 0.66 per hour. CONCLUSIONS Our approach to separate neonatal seizures into two types yields a high sensitivity combined with a good PPV and much lower false positive rate than previously published algorithms. SIGNIFICANCE The proposed algorithm significantly improves neonatal seizure detection and monitoring.

Pain in Guillain-Barre syndrome: a long-term follow-up study.

Background: Pain in Guillain-Barré syndrome (GBS) may be pronounced and is often overlooked. Objectives: To obtain detailed information about pain in GBS and its clinical variants. Methods: This was a prospective cohort study in 156 patients with GBS (including 18 patients with Miller Fisher syndrome [MFS]). We assessed the location, type, and intensity of pain using questionnaires at standard time points during a 1-year follow-up. Pain data were compared to other clinical features and serology. Results: Pain was reported in the 2 weeks preceding weakness in 36% of patients, 66% reported pain in the acute phase (first 3 weeks after inclusion), and 38% reported pain after 1 year. In the majority of patients, the intensity of pain was moderate to severe. Longitudinal analysis showed high mean pain intensity scores during the entire follow-up. Pain occurred in the whole spectrum of GBS. The mean pain intensity was predominantly high in patients with GBS (non-MFS), patients with sensory disturbances, and severely affected patients. Only during later stages of disease, severity of weakness and disability were significantly correlated with intensity of pain. Conclusions: Pain is a common and often severe symptom in the whole spectrum of GBS (including MFS, mildly affected, and pure motor patients). As it frequently occurs as the first symptom, but may even last for at least 1 year, pain in GBS requires full attention. It is likely that sensory nerve fiber involvement results in more severe pain.

Automated artifact removal as preprocessing refines neonatal seizure detection

OBJECTIVE The description and evaluation of algorithms using Independent Component Analysis (ICA) for automatic removal of ECG, pulsation and respiration artifacts in neonatal EEG before automated seizure detection. METHODS The developed algorithms decompose the EEG using ICA into its underlying sources. The artifact source was identified using the simultaneously recorded polygraphy signals after preprocessing. The EEG was reconstructed without the corrupting source, leading to a clean EEG. The impact of the artifact removal was measured by comparing the performance of a previously developed seizure detector before and after the artifact removal in 13 selected patients (9 having artifact-contaminated and 4 having artifact-free EEGs). RESULTS A significant decrease in false alarms (p=0.01) was found while the Good Detection Rate (GDR) for seizures was not altered (p=0.50). CONCLUSIONS The techniques reduced the number of false positive detections without lowering sensitivity and are beneficial in long term EEG seizure monitoring in the presence of disturbing biological artifacts. SIGNIFICANCE The proposed algorithms improve neonatal seizure monitoring.

Standardized computer-based organized reporting of EEG: SCORE.

The electroencephalography (EEG) signal has a high complexity, and the process of extracting clinically relevant features is achieved by visual analysis of the recordings. The interobserver agreement in EEG interpretation is only moderate. This is partly due to the method of reporting the findings in free‐text format. The purpose of our endeavor was to create a computer‐based system for EEG assessment and reporting, where the physicians would construct the reports by choosing from predefined elements for each relevant EEG feature, as well as the clinical phenomena (for video‐EEG recordings). A working group of EEG experts took part in consensus workshops in Dianalund, Denmark, in 2010 and 2011. The faculty was approved by the Commission on European Affairs of the International League Against Epilepsy (ILAE). The working group produced a consensus proposal that went through a pan‐European review process, organized by the European Chapter of the International Federation of Clinical Neurophysiology. The Standardised Computer‐based Organised Reporting of EEG (SCORE) software was constructed based on the terms and features of the consensus statement and it was tested in the clinical practice. The main elements of SCORE are the following: personal data of the patient, referral data, recording conditions, modulators, background activity, drowsiness and sleep, interictal findings, “episodes” (clinical or subclinical events), physiologic patterns, patterns of uncertain significance, artifacts, polygraphic channels, and diagnostic significance. The following specific aspects of the neonatal EEGs are scored: alertness, temporal organization, and spatial organization. For each EEG finding, relevant features are scored using predefined terms. Definitions are provided for all EEG terms and features. SCORE can potentially improve the quality of EEG assessment and reporting; it will help incorporate the results of computer‐assisted analysis into the report, it will make possible the build‐up of a multinational database, and it will help in training young neurophysiologists.

Validation of a new automated neonatal seizure detection system: A clinician’s perspective

OBJECTIVE To validate an improved automated electroencephalography (EEG)-based neonatal seizure detection algorithm (NeoGuard) in an independent data set. METHODS EEG background was classified into eight grades based on the evolution of discontinuity and presence of sleep-wake cycles. Patients were further sub-classified into two groups; gpI: mild to moderate (grades 1-5) and gpII: severe (grades 6-8) EEG background abnormalities. Seizures were categorised as definite and dubious. Seizure characteristics were compared between gpI and gpII. The algorithm was tested on 756 h of EEG data from 24 consecutive neonates (median 25 h per patient) with encephalopathy and recorded seizures during continuous monitoring (cEEG). No selection was made regarding the quality of EEG or presence of artefacts. RESULTS Seizure amplitudes significantly decreased with worsening EEG background. Seizures were detected with a total sensitivity of 61.9% (1285/2077). The detected seizure burden was 66,244/97,574 s (67.9%). Sensitivity per patient was 65.9%, with a mean positive predictive value (PPV) of 73.7%. After excluding four patients with severely abnormal EEG background, and predominantly having dubious seizures, the algorithm showed a median sensitivity per patient of 86.9%, PPV of 89.5% and false positive rate of 0.28 h(-1). Sensitivity tended to be better for patients in gpI. CONCLUSIONS The algorithm detects neonatal seizures well, has a good PPV and is suited for cEEG monitoring. Changes in electrographic characteristics such as amplitude, duration and rhythmicity in relation to deteriorating EEG background tend to worsen the performance of automated seizure detection. SIGNIFICANCE cEEG monitoring is important for detecting seizures in the neonatal intensive care unit (NICU). Our automated algorithm reliably detects neonatal seizures that are likely to be clinically most relevant, as reflected by the associated EEG background abnormality.

Guillain–Barré syndrome subtypes related to Campylobacter infection

Background In Guillain–Barré syndrome (GBS), the diversity in electrophysiological subtypes is unexplained but may be determined by geographical factors and preceding infections. Acute motor axonal neuropathy (AMAN) is a frequent GBS variant in Japan and one study proposed that in Japan, Campylobacter jejuni infections exclusively elicit AMAN. In The Netherlands C jejuni is the predominant type of preceding infection yet AMAN is rare. This may indicate that not all Dutch GBS patients with C jejuni infections have AMAN. Objective To determine if GBS patients with a preceding C jejuni infection in The Netherlands exclusively have AMAN. Methods Retrospective analysis of preceding infections in relation to serial electrophysiology and clinical data from 123 GBS patients. C jejuni related cases were defined as having preceding diarrhoea and positive C jejuni serology. Electrophysiological characteristics in C jejuni related cases were compared with those in viral related GBS patients. In addition, eight GBS patients from another cohort with positive stool cultures for C jejuni were analysed. Results 17 (14%) of 123 patients had C jejuni related GBS. C jejuni patients had lower motor and higher sensory action potentials compared with viral related cases. Nine (53%) C jejuni patients had either AMAN or inexcitable nerves. However, three (18%) patients fulfilled the criteria for acute inflammatory demyelinating polyneuropathy (AIDP). Also, two (25%) of eight additional patients with a C jejuni positive stool sample had AIDP. Conclusion In The Netherlands, C jejuni infections are strongly, but not exclusively, associated with axonal GBS. Some patients with these infections fulfil current criteria for demyelination.

Neonatal seizure localization using PARAFAC decomposition

OBJECTIVE The description and evaluation of two EEG-based algorithms for automatic and objective determination of the seizure location in the neonatal brain as it is reflected on the scalp. METHODS Each algorithm extracts the electrical potential distribution of the seizure over the scalp using the higher-order canonical decomposition or Parallel Factor Analysis (PARAFAC), also referred to as the CP model. This model decomposes a tensor in a sum of rank-1 components. The two algorithms differ in the way the tensor is constructed and in the type of activity they are able to extract. While the first method extracts oscillatory seizure activity, the second extracts spike train activity. RESULTS We compared the seizure localization results of 21 seizures from 6 neonates with post-asphyxial hypoxic ischemic encephalopathy, with that based on the visual analysis of the EEG by a clinical neurophysiologist. There was a good agreement between the two methods in the localization of seizure onset in all. CONCLUSION The techniques presented in this paper are robust, objective methods to determine neonatal seizure localization. They can be a useful tool for neonatal EEG analysis and for continuous brain function monitoring. SIGNIFICANCE The proposed algorithms significantly improve neonatal seizure localization and monitoring.

The electrophysiological muscle scan.

This study aims to assess the potential of the electrophysiological muscle scan or stimulus–response curve as a diagnostic instrument. If stimulus intensity is gradually increased from subthreshold to supramaximal values, all motor units in a muscle are successively activated. Thus, by plotting response size versus stimulus intensity, an impression (scan) of the entire muscle can be obtained. We recorded 54 detailed scans from 34 patients and 11 healthy subjects, and analyzed them visually and quantitatively. The scan summarized much diagnostic information in a single picture. Specific patterns in or properties of the scan (steps, maximum, variability, decrements, stimulus intensities used) provide clinically relevant information regarding motor unit number, size, and stability, and neuromuscular transmission and axonal excitability. The scan can be recorded noninvasively in about 5 minutes and is fairly easy to interpret. Because it is built up from contributions of all functioning motor units, the scan shows if and how many large motor units are present. There is no sample bias. For these reasons, further exploration and exploitation of this tool in the clinical setting are warranted. Muscle Nerve, 2007

Motor unit tracking with high-density surface EMG

Following (tracking) individual motor units over time can provide important new insights, both into the relationships among various motor unit (MU) morphological and functional properties and into how these properties are influenced by neuromuscular disorders or interventions. The present study aimed to determine whether high-density surface EMG (HD-sEMG) recordings, which use an array of surface electrodes over a muscle, can increase the yield of MU tracking studies in terms of the number of MUs that can be tracked. For that purpose, four HD-sEMG recording sessions were performed on the thenar muscles of ten healthy subjects. Decomposition of the recorded composite responses yielded a study total of 2849 motor unit action potentials (MUAPs). MUAPs that were found in both of the first two sessions, performed on the same day, were defined as trackable MUAPs. Our results show that 22 (median value; range, 13-34) MUAPs per nerve were trackable, which represented approximately 5% of the total MU population. Of these trackable MUAPs, 16 (11-26) could also be found in one or both of the third and fourth sessions, which were performed between 1 and 13 weeks after the initial studies. Nine (4-18) MUAPs were found in all four sessions. Many of the characteristic MUAP shapes matched well between sessions, even when these sessions were several weeks apart. However, some MUAPs seem very sensitive to changes in arm position or in the muscles morphology (e.g., to changes in muscle fiber length due to variable degrees of thumb flexion or extension), particularly those from larger and/or superficial MUs. Standardization is, therefore, essential to detect even small MUAP changes, as may occur with pathology or interventions. If this is accomplished, MU tracking with HD-sEMG may prove to be a powerful tool for a promising type of neurophysiological investigation.

Statistical motor unit number estimation assuming a binomial distribution

The statistical method of motor unit number estimation (MUNE) uses the natural stochastic variation in a muscles compound response to electrical stimulation to obtain an estimate of the number of recruitable motor units. The current method assumes that this variation follows a Poisson distribution. We present an alternative that instead assumes a binomial distribution. Results of computer simulations and of a pilot study on 19 healthy subjects showed that the binomial MUNE values are considerably higher than those of the Poisson method, and in better agreement with the results of other MUNE techniques. In addition, simulation results predict that the performance in patients with severe motor unit loss will be better for the binomial than Poisson method. The adapted method remains closer to physiology, because it can accommodate the increase in activation probability that results from rising stimulus intensity. It does not need recording windows as used with the Poisson method, and is therefore less user‐dependent and more objective and quicker in its operation. For these reasons, we believe that the proposed modifications may lead to significant improvements in the statistical MUNE technique. Muscle Nerve, 2005