Clinical Neurophysiology Practice最新文献

Electroencephalography (EEG) is one of the main pillars used for the diagnosis and study of epilepsy, readily employed after a possible first seizure has occurred. The most established biomarker of epilepsy, in case seizures are not recorded, are interictal epileptiform discharges (IEDs). In clinical practice, however, IEDs are not always present and the EEG may appear completely normal despite an underlying epileptic disorder, often leading to difficulties in the diagnosis of the disease. Thus, finding other biomarkers that reliably predict whether an individual suffers from epilepsy even in the absence of evident epileptic activity would be extremely helpful, since they could allow shortening the period of diagnostic uncertainty and consequently decreasing the risk of seizure. To date only a few EEG features other than IEDs seem to be promising candidates able to distinguish between epilepsy, i.e. > 60 % risk of recurrent seizures, or other (pathological) conditions. The aim of this narrative review is to provide an overview of the EEG-based biomarker candidates for epilepsy and the techniques employed for their identification.

Objective

The prolonged video EEG monitoring is widely used for the diagnosis and management of epilepsy, especially during the presurgical evaluation. The routine practice in neurology is to order a prolonged recording like an overnight EEG when the initial routine EEG is normal or unrevealing. Only few studies have evaluated this sequential approach and we aimed in this study to evaluate the added diagnostic value of a relatively brief video EEG monitoring especially in developing nations where the history of seizure semiology may be harder to obtain, and the video EEG monitoring technology is scarce.

Methods

This study analyzed retrospectively 167 overnight video EEG records in one of the secondary healthcare facilities in Western Kenya between March 2018 and March 2021. The indications were mainly further diagnosis and seizure classification. All the patients had an unrevealing routine EEG and 162 of them were normal.

Results

Additional epileptiform discharges were recorded in 91 of those 162 with initial normal routine EEG. Further classification of seizure was achieved in 67 patients among 112 with initially unclassified seizure before the overnight recording. The improvement of 68% (97 out of 143 patients without a prior epilepsy diagnosis) for the diagnosis of epilepsy in those patients without initial final diagnosis is comparable to other similar studies but mostly with a longer duration of recording. The diagnosis was changed or at least improved in 142 (85%) patients out of the 167 patients who underwent the overnight video EEG. The treatment modification was immediately considered in 116 after the prolonged recording.

Conclusions

Adding an overnight video EEG to an unrevealing routine EEG can significantly increase the likelihood of detecting additional epileptiform discharges in patients with epilepsy, thereby improving diagnostic yield and aiding in treatment adjustment for all patients suspected of having epilepsy.

Significance

The sequential approach of adding a prolonged video EEG monitoring even as brief as an overnight video EEG to an unrevealing routine EEG has a very significant impact in further classification of seizure and diagnosis of epilepsy especially in a resource limited set up.

Objective

Misinterpretation of EEGs harms patients, yet few resources exist to help trainees practice interpreting EEGs. We therefore sought to evaluate a novel educational tool to teach trainees how to identify interictal epileptiform discharges (IEDs) on EEG.

Methods

We created a public EEG test within the iOS app DiagnosUs using a pool of 13,262 candidate IEDs. Users were shown a candidate IED on EEG and asked to rate it as epileptiform (IED) or not (non-IED). They were given immediate feedback based on a gold standard. Learning was analyzed using a parametric model. We additionally analyzed IED features that best correlated with expert ratings.

Results

Our analysis included 901 participants. Users achieved a mean improvement of 13% over 1,000 questions and an ending accuracy of 81%. Users and experts appeared to rely on a similar set of IED morphologic features when analyzing candidate IEDs. We additionally identified particular types of candidate EEGs that remained challenging for most users even after substantial practice.

Conclusions

Users improved in their ability to properly classify candidate IEDs through repeated exposure and immediate feedback.

Significance

This app-based learning activity has great potential to be an effective supplemental tool to teach neurology trainees how to accurately identify IEDs on EEG.

Objective

We evaluated the resistance to externally induced wrist extension in chronic stroke patients. We aimed to objectively measure and distinguish passive (muscle and soft tissue stiffness) and active (spasticity and spastic dystonia) components of the resistance.

Methods

We used a hand-held dynamometer, which measures torque, joint movement and electromyography (EMG) simultaneously, to assess the resistance to externally induced wrist extension. Slow and fast stretches were applied to the affected and unaffected wrists in 57 chronic stroke patients (57 ± 11 years). We extracted from the data parameters that represent passive and muscle activity components and assessed the validity, test–retest reliability and the clinical utility of the measurement.

Results

The analysis showed (1) a significant difference in the passive and muscle activity components between the affected and unaffected sides; (2) a significant correlation between passive and muscle activity components and the modified Ashworth scale (MAS); (3) a significant difference between the subgroups of patients stratified by the MAS; (4) an excellent intra-rater reliability on each of the passive and muscle activity components with intra-class coefficients between 0.92 and 0.99; (5) and small measurement error.

Conclusions

Using a hand-held dynamometer, we were able to objectively measure the resistance to muscle stretch in the wrist joint in chronic stroke patients and discriminate muscle overactivity components from muscle and soft tissue stiffness. We demonstrated validity, test–retest reliability and the clinical utility of the measurement.

Significance

Quantification of the different components of resistance to externally induced movement enables the objective evaluation of neurorehabilitation effects in chronic stroke patients.

Objective

We aimed to evaluate differences in ultrasonographic nerve enlargement sites among typical chronic inflammatory demyelinating polyradiculoneuropathy (CIDP), distal CIDP, multifocal CIDP and multifocal motor neuropathy (MMN) in a Japanese population.

Methods

We retrospectively reviewed medical records and selected 39 patients (14 with typical CIDP, 7 with multifocal CIDP, 4 with distal CIDP, and 14 with MMN) who underwent ultrasonography. Median and ulnar nerve cross-sectional areas (CSAs) were measured at the wrist, forearm, elbow, and upper arm. CSA ratios for each nerve were calculated as: wrist-to-forearm index (WFI) = wrist CSA/forearm CSA; elbow-to-upper arm index (EUI) = elbow CSA/upper arm CSA; and intranerve CSA variability (INCV) = maximal CSA/minimal CSA.

Results

Significant differences were observed among typical CIDP, multifocal CIDP, distal CIDP, and MMN in CSA at the forearm and upper arm in the median nerves (p < 0.05). Patients with multifocal CIDP had lower WFI and EUI and higher INCV than the other groups (p < 0.05).

Conclusions

Regardless of the untreated period, compared with other CIDP subtypes and MMN, multifocal CIDP showed a focal and marked nerve enlargement in the Japanese population.

Significance

Differences in nerve enlargement site may be an underlying feature of multifocal CIDP.

Objective

To compare the utility of MUNIX (motor unit number index) with needle EMG in characterizing motor unit (MU) properties in the biceps brachii (BB) muscle in subjects with remote polio.

Methods

Thirty subjects suffering from remote polio were investigated with MUNIX and needle EMG, all with Macro EMG and 16 of these subjects with concentric needle EMG.

Results

Both MUNIX and the needle EMG methods showed abnormal results. Fiber density (FD) was the most sensitive parameter for showing signs of reinnervation. At a group level, the methods showed neurogenic findings, but there was no correlation between the results of the MUNIX and needle EMG investigations.

Conclusions

Both MUNIX and needle EMG are valuable methods for measuring neurogenic involvement in the BB muscle. However, there was a lack of correlation between the MUNIX and needle EMG findings. The cause for this missing correlation may be multifactorial as there are several differences between the methods.

Significance

The reason for the lack of correlation between the MUNIX and needle EMG results is discussed. By combining the needle and surface recorded methods one can obtain more information on the denervation and reinnervation process compared to using just one of the methods alone.

Objective

To establish the intrasession relative and absolute reliability of Short (SAI) and Long-Latency Afferent Inhibition (LAI). These findings will allow us to guide future explorations of changes to these measures.

Methods

31 healthy individuals (21.06 ± 2.85 years) had SAI and LAI obtained thrice at 30-minute intervals in one session. To identify the minimum number of trials required to reliably elicit SAI and LAI, relative reliability was assessed at running intervals of 5 trials.

Results

SAI had moderate–high, and LAI had high-excellent relative reliability. Both SAI and LAI had high amounts of measurement error. LAI had high relative reliability when only 5 frames of data were included, whereas SAI required ∼20–30 frames of data for the same. For both SAI and LAI, individual smallest detectable change was large but was reduced at the group level.

Conclusions

SAI and LAI can be used for both diagnostic purposes and to assess group level change but have limited utility in assessing within-individual changes.

Significance

These results can be used to inform future work regarding the utility of SAI and LAI, particularly in terms of their ability to identify particularly high or low values of afferent inhibition.

Objective

Emergency diagnostics, such as acquisition of an electroencephalogram (EEG), are of great diagnostic importance, but there is often a lack of experienced personnel. Wet active electrode sponge-based electroencephalogram (sp-EEG) systems can be applied rapidly and by inexperienced personnel. This makes them an attractive alternative to routine EEG (r-EEG) systems in these settings. Here, we examined the feasibility and signal quality of sp-EEG compared to r-EEG.

Methods

In this case-control, single-blind, non-randomized study, EEG recordings using a sp- and a r-EEG system were performed in 18 individuals with a variety of epileptiform discharges and 11 healthy control subjects. The time was stopped until all electrodes in both systems displayed adequate skin-electrode impedances. The resulting 58 EEGs were visually inspected by 7 experienced, blinded neurologists. Raters were asked to score physiological and pathological graphoelements, and to distinguish between the different systems by visual inspection of the EEGs.

Results

Time to signal acquisition for sp-EEG was significantly faster (4.8 min (SD 2.01) vs. r-EEG 13.3 min (SD 2.72), p < 0.001). All physiological and pathological graphoelements of all 58 EEGs could be identified. Raters were unable to distinguish between sp-EEG or r-EEG based on visual inspection of the EEGs alone.

Conclusions

Sp-EEG represents a feasible alternative to r-EEG in emergency diagnostics or resource-limited settings.

Significance

Given shortage of trained personnel or resources, the easy implementation and comparable quality of a novel sp-EEG system may increase general availability of EEG and thus improve patient care.

Objective

Convolutional Neural Networks (CNNs) are promising for artifact detection in electroencephalography (EEG) data, but require large amounts of data. Despite increasing use of dry electrodes for EEG data acquisition, dry electrode EEG datasets are sparse. We aim to develop an algorithm for clean versus artifact dry electrode EEG data classification using transfer learning.

Methods

Dry electrode EEG data were acquired in 13 subjects while physiological and technical artifacts were induced. Data were per 2-second segment labeled as clean or artifact and split in an 80% train and 20% test set. With the train set, we fine-tuned a pre-trained CNN for clean versus artifact wet electrode EEG data classification using 3-fold cross validation. The three fine-tuned CNNs were combined in one final clean versus artifact classification algorithm, in which the majority vote was used for classification. We calculated accuracy, F1-score, precision, and recall of the pre-trained CNN and fine-tuned algorithm when applied to unseen test data.

Results

The algorithm was trained on 0.40 million and tested on 0.17 million overlapping EEG segments. The pre-trained CNN had a test accuracy of 65.6%. The fine-tuned clean versus artifact classification algorithm had an improved test accuracy of 90.7%, F1-score of 90.2%, precision of 89.1% and recall of 91.2%.

Conclusions

Despite a relatively small dry electrode EEG dataset, transfer learning enabled development of a high performing CNN-based algorithm for clean versus artifact classification.

Significance

Development of CNNs for classification of dry electrode EEG data is challenging as dry electrode EEG datasets are sparse. Here, we show that transfer learning can be used to overcome this problem.