Time-frequency mapping of the rhythmic limb movements distinguishes convulsive epileptic from psychogenic nonepileptic seizures

Classification of Epileptic and Psychogenic Nonepileptic Seizures via Time-Frequency Features of EEG Data

The majority of psychogenic nonepileptic seizures (PNESs) are brought on by psychogenic causes, but because their symptoms resemble those of epilepsy, they are frequently misdiagnosed. Although EEG signals are normal in PNES cases, electroencephalography (EEG) recordings alone are not sufficient to identify the illness. Hence, accurate diagnosis and effective treatment depend on long-term video EEG data and a complete patient history. Video EEG setup, however, is more expensive than using standard EEG equipment. To distinguish PNES signals from conventional epileptic seizure (ES) signals, it is crucial to develop methods solely based on EEG recordings. The proposed study presents a technique utilizing short-term EEG data for the classification of inter-PNES, PNES, and ES segments using time-frequency methods such as the Continuous Wavelet transform (CWT), Short-Time Fourier transform (STFT), CWT-based synchrosqueezed transform (WSST), and STFT-based SST (FSST), which provide high-resolution time-frequency representations (TFRs). TFRs of EEG segments are utilized to generate 13 joint TF (J-TF)-based features, four gray-level co-occurrence matrix (GLCM)-based features, and 16 higher-order joint TF moment (HOJ-Mom)-based features. These features are then employed in the classification procedure. Both three-class (inter-PNES versus PNES versus ES: ACC: 80.9%, SEN: 81.8%, and PRE: 84.7%) and two-class (Inter-PNES versus PNES: ACC: 88.2%, SEN: 87.2%, and PRE: 86.1%; PNES versus ES: ACC: 98.5%, SEN: 99.3%, and PRE: 98.9%) classification algorithms performed well, according to the experimental results. The STFT and FSST strategies surpass the CWT and WSST strategies in terms of classification accuracy, sensitivity, and precision. Moreover, the J-TF-based feature sets often perform better than the other two.

Entropy Measures of Electroencephalograms towards the Diagnosis of Psychogenic Non-Epileptic Seizures

Psychogenic non-epileptic seizures (PNES) may resemble epileptic seizures but are not caused by epileptic activity. However, the analysis of electroencephalogram (EEG) signals with entropy algorithms could help identify patterns that differentiate PNES and epilepsy. Furthermore, the use of machine learning could reduce the current diagnosis costs by automating classification. The current study extracted the approximate sample, spectral, singular value decomposition, and Renyi entropies from interictal EEGs and electrocardiograms (ECG)s of 48 PNES and 29 epilepsy subjects in the broad, delta, theta, alpha, beta, and gamma frequency bands. Each feature-band pair was classified by a support vector machine (SVM), k-nearest neighbour (kNN), random forest (RF), and gradient boosting machine (GBM). In most cases, the broad band returned higher accuracy, gamma returned the lowest, and combining the six bands together improved classifier performance. The Renyi entropy was the best feature and returned high accuracy in every band. The highest balanced accuracy, 95.03%, was obtained by the kNN with Renyi entropy and combining all bands except broad. This analysis showed that entropy measures can differentiate between interictal PNES and epilepsy with high accuracy, and improved performances indicate that combining bands is an effective improvement for diagnosing PNES from EEGs and ECGs.

Electroencephalogram Connectivity for the Diagnosis of Psychogenic Non-epileptic Seizures

Psychogenic non-epileptic seizures (PNES) are attacks that resemble epilepsy but are not associated with epileptic brain activity and are regularly misdiagnosed. The current gold standard method of diagnosis is expensive and complex. Electroencephalogram (EEG) analysis with machine learning could improve this. A k-nearest neighbours (kNN) and support vector machine (SVM) were used to classify EEG connectivity measures from 48 patients with PNES and 29 patients with epilepsy. The synchronisation method - correlation or coherence - and the binarisation threshold were defined through experimentation. Ten network parameters were extracted from the synchronisation matrix. The broad, delta, theta, alpha, beta, gamma, and combined 'all' frequency bands were compared along with three feature selection methods: the full feature set (no selection), light gradient boosting machine (LGBM) and k-Best. Coherence was the highest performing synchronisation method and 0.6 was the best coherence threshold. The highest balanced accuracy was 89.74%, produced by combining all six frequency bands and selecting features with LGBM, classified by the SVM. This method returned a comparatively high accuracy but at a high computation cost. Future research should focus on identifying specific frequency bands and network parameters to reduce this cost. Clinical relevance - This study found that EEG connectivity and machine learning methods can be used to differentiate PNES from epilepsy using interictal recordings to a high accuracy. Thus, this method could be an effective tool in assisting clinicians in PNES diagnosis without a video- EEG recording of a habitual seizure.

Wearable devices for seizure detection: Practical experiences and recommendations from the Wearables for Epilepsy And Research (WEAR) International Study Group

The Wearables for Epilepsy And Research (WEAR) International Study Group identified a set of methodology standards to guide research on wearable devices for seizure detection. We formed an international consortium of experts from clinical research, engineering, computer science, and data analytics at the beginning of 2020. The study protocols and practical experience acquired during the development of wearable research studies were discussed and analyzed during bi-weekly virtual meetings to highlight commonalities, strengths, and weaknesses, and to formulate recommendations. Seven major essential components of the experimental design were identified, and recommendations were formulated about: (1) description of study aims, (2) policies and agreements, (3) study population, (4) data collection and technical infrastructure, (5) devices, (6) reporting results, and (7) data sharing. Introducing a framework of methodology standards promotes optimal, accurate, and consistent data collection. It also guarantees that studies are generalizable and comparable, and that results can be replicated, validated, and shared.

[The problem of comorbidity of epilepsy and psychogenic paroxysms]

A review of publications over the last two decades is presented. Psychogenic paroxysms develop in approximately 12% of patients with epilepsy. The analysis of social and demographic data, history details, semiological features and results of electrophysiological and neuroimaging studies does not unequivocally support the comorbidity of epilepsy and psychogenic paroxysms. The pathogenetic mechanisms of the development of comorbidity are various and depend on the presence of pharmacoresistance, psychological traumas in the past, intellectual disability etc. Video-EEG-monitoring is the gold standard in the diagnosis of comorbidity of epilepsy and psychogenic paroxysms. Treatment of such cases includes anticonvulsants and cognitive-behavioral therapy.

EEG-based classification of epilepsy and PNES: EEG microstate and functional brain network features

Epilepsy and psychogenic non-epileptic seizures (PNES) often show over-lap in symptoms, especially at an early disease stage. During a PNES, the electrical activity of the brain remains normal but in case of an epileptic seizure the brain will show epileptiform discharges on the electroencephalogram (EEG). In many cases an accurate diagnosis can only be achieved after a long-term video monitoring combined with EEG recording which is quite expensive and time-consuming. In this paper using short-term EEG data, the classification of epilepsy and PNES subjects is analyzed based on signal, functional network and EEG microstate features. Our results showed that the beta-band is the most useful EEG frequency sub-band as it performs best for classifying subjects. Also the results depicted that when the coverage feature of the EEG microstate analysis is calculated in beta-band, the classification shows fairly high accuracy and precision. Hence, the beta-band and the coverage are the most important features for classification of epilepsy and PNES patients.

Feasibility of a Sensor-Based Technological Platform in Assessing Gait and Sleep of In-Hospital Stroke and Incomplete Spinal Cord Injury (iSCI) Patients

Recovery of the walking function is one of the most common rehabilitation goals of neurological patients. Sufficient and adequate sleep is a prerequisite for recovery or training. To objectively monitor patients’ progress, a combination of different sensors measuring continuously over time is needed. A sensor-based technological platform offers possibilities to monitor gait and sleep. Implementation in clinical practice is of utmost relevance and has scarcely been studied. Therefore, this study examined the feasibility of a sensor-based technological platform within the clinical setting. Participants (12 incomplete spinal cord injury (iSCI), 13 stroke) were asked to wear inertial measurement units (IMUs) around the ankles during daytime and the bed sensor was placed under their mattress for one week. Feasibility was established based on missing data, error cause, and user experience. Percentage of missing measurement days and nights was 14% and 4%, respectively. Main cause of lost measurement days was related to missing IMU sensor data. Participants were not impeded, did not experience any discomfort, and found the sensors easy to use. The sensor-based technological platform is feasible to use within the clinical rehabilitation setting for continuously monitoring gait and sleep of iSCI and stroke patients.

The utility of an automated and ambulatory device for detecting and differentiating epileptic and psychogenic non-epileptic seizures

OBJECTIVE

Accurate differentiation between epileptic seizures (ES) and psychogenic non-epileptic seizures (PNES) can be challenging based on history alone. Inpatient video EEG monitoring (VEM) is often needed for a definitive diagnosis. However, VEM is highly resource intensive, is of limited availability, and cannot be undertaken over long periods. Previous research has shown that time-frequency analysis of accelerometer data could be utilized to differentiate between ES and PNES. Using a seizure detection and classification algorithm, we sought to examine the diagnostic utility of an automated analysis with an ambulatory accelerometer.

METHODS

A wrist-worn device was used to collect accelerometer data from patients during VEM admission, for diagnostic evaluation of convulsive seizures. An automated process, that involved the use of K-means clustering and support vector machines, was used to detect and classify each seizure as ES or PNES. The results were compared with VEM diagnoses determined by epileptologists blinded to the accelerometer data.

RESULTS

Twenty-four convulsive seizures, consisting of at least 20 seconds of sustained continuous activity, recorded from 11 patients during inpatient VEM (13 PNES from five patients and 11 ES from six patients) were included for analysis. The automated system detected all convulsive seizures (ES, PNES) from >661 hours of recording with 67 false alarms (2.4 per 24 hours). The sensitivity and specificity for classifying ES from PNES were 72.7% and 100%, respectively. The positive and negative predictive values for classifying PNES were 81.3% and 100%, respectively. There was no significant difference between the classification results obtained from the automation process and the VEM diagnoses.

SIGNIFICANCE

This automated system can potentially provide a wearable out-of-hospital seizure diagnostic monitoring system.

An upper body garment with integrated sensors for people with neurological disorders - early development and evaluation

Background

In neurology and rehabilitation the primary interest for using wearables is to supplement traditional patient assessment and monitoring in hospital settings with continuous data collection at home and in community settings. The aim of this project was to develop a novel wearable garment with integrated sensors designed for continuous monitoring of physiological and movement related variables to evaluate progression, tailor treatments and improve diagnosis in epilepsy, Parkinson’s disease and stroke. In this paper the early development and evaluation of a prototype designed to monitor movements and heart rate is described. An iterative development process and evaluation of an upper body garment with integrated sensors included: identification of user needs, specification of technical and garment requirements, garment development and production as well as evaluation of garment design, functionality and usability. The project is a multidisciplinary collaboration with experts from medical, engineering, textile, and material science within the wearITmed consortium. The work was organized in regular meetings, task groups and hands-on workshops. User needs were identified using results from a mixed-methods systematic review, a focus group study and expert groups. Usability was evaluated in 19 individuals (13 controls, 6 patients with Parkinson’s disease) using semi-structured interviews and qualitative content analysis.

Results

The garment was well accepted by the users regarding design and comfort, although the users were cautious about the technology and suggested improvements. All electronic components passed a washability test. The most robust data was obtained from accelerometer and gyroscope sensors while the electrodes for heart rate registration were sensitive to motion artefacts. The algorithm development within the wearITmed consortium has shown promising results.

Conclusions

The prototype was accepted by the users. Technical improvements are needed, but preliminary data indicate that the garment has potential to be used as a tool for diagnosis and treatment selection and could provide added value for monitoring seizures in epilepsy, fluctuations in PD and activity levels in stroke. Future work aims to improve the prototype further, develop algorithms, and evaluate the functionality and usability in targeted patient groups. The potential of incorporating blood pressure and heart-rate variability monitoring will also be explored.

Novel features for capturing temporal variations of rhythmic limb movement to distinguish convulsive epileptic and psychogenic nonepileptic seizures

OBJECTIVE

To investigate the characteristics of motor manifestation during convulsive epileptic and psychogenic nonepileptic seizures (PNES), captured using a wrist-worn accelerometer (ACM) device. The main goal was to find quantitative ACM features that can differentiate between convulsive epileptic and convulsive PNES.

METHODS

In this study, motor data were recorded using wrist-worn ACM-based devices. A total of 83 clinical events were recorded: 39 generalized tonic-clonic seizures (GTCS) from 12 patients with epilepsy, and 44 convulsive PNES from 7 patients (one patient had both GTCS and PNES). The temporal variations in the ACM traces corresponding to 39 GTCS and 44 convulsive PNES events were extracted using Poincaré maps. Two new indices-tonic index (TI) and dispersion decay index (DDI)-were used to quantify the Poincaré-derived temporal variations for every GTCS and convulsive PNES event.

RESULTS

The TI and DDI of Poincaré-derived temporal variations for GTCS events were higher in comparison to convulsive PNES events (P < 0.001). The onset and the subsiding patterns captured by TI and DDI differentiated between epileptic and convulsive nonepileptic seizures. An automated classifier built using TI and DDI of Poincaré-derived temporal variations could correctly differentiate 42 (sensitivity: 95.45%) of 44 convulsive PNES events and 37 (specificity: 94.87%) of 39 GTCS events. A blinded review of the Poincaré-derived temporal variations in GTCS and convulsive PNES by epileptologists differentiated 26 (sensitivity: 70.27%) of 44 PNES events and 33 (specificity: 86.84%) of 39 GTCS events correctly.

SIGNIFICANCE

In addition to quantifying the motor manifestation mechanism of GTCS and convulsive PNES, the proposed approach also has diagnostic significance. The new ACM features incorporate clinical characteristics of GTCS and PNES, thus providing an accurate, low-cost, and practical alternative to differential diagnosis of PNES.

Dual diagnosis of epilepsy and psychogenic nonepileptic seizures: Systematic review and meta-analysis of frequency, correlates, and outcomes

Comorbid epilepsy and psychogenic nonepileptic seizures (PNES) represent a serious challenge for the clinicians. However, the frequency, associations, and outcomes of dual diagnosis of epilepsy and PNES are unclear. The aim of the review was to determine the frequency, correlates, and outcomes of a dual diagnosis. A systematic review of all published observational studies (from inception to Dec. 2016) was conducted to determine the frequency, correlates, and outcomes of dual diagnosis. We included studies of individuals of any age reporting a dual diagnosis of epilepsy and PNES. All observational study designs were included with the exception of case reports and case series with fewer than 10 participants. The mean frequency of epilepsy in patients with PNES across all studies was 22% (95% confidence intervals [CI] 20 to 25%, range: 0% to 90%) while the mean frequency of PNES in patients with epilepsy was 12% (95% CI 10 to 14%, range: 1% to 62%). High heterogeneity means that these pooled estimates should be viewed with caution. A number of correlates of dual diagnosis were reported. Some studies delineated differences in semiology of seizures in patients with dual diagnosis vs. PNES or epilepsy only. However, most of the correlates were inconclusive. Only a few studies examined outcome in patients with dual diagnosis. Dual diagnosis is common in clinical practice, especially among patients referred to specialized services, and requires careful diagnosis and management.

Improved Detection and Classification of Convulsive Epileptic and Psychogenic Non-epileptic Seizures Using FLDA and Bayesian Inference

A high number of patients with epileptic seizures (ES) are misdiagnosed due to prevalence of mimic conditions. The clinical characteristics of mimics are often similar to ES. The events mostly misdiagnosed are of psychogenic origin and are termed as psychogenic non-epileptic seizures (PNES). The gold standard for diagnosis of PNES is video-electroencephalography monitoring (VEM), which is a resource demanding process. Hence, need for a more object method of PNES diagnosis is created. Accelerometer sensors have been used previously for the diagnosis of ES. In this work, we present a new approach for detection and classification of PNES using wrist-worn accelerometer device. Various time, frequency and wavelet space features are extracted from the accelerometry signal. Feature compression is then performed using Fisher linear discriminant analysis (FLDA). A Bayesian classifier is then trained using kernel estimator method. The algorithm was trained and tested on data collected from 16 patients undergoing VEM. When tested, the algorithm detected all seizures with 20 false alarms and correctly classified 100% PNES and 75% ES, respectively of the detected seizures.

[Psychogenic non epileptic seizures : Differential diagnostic features]

Psychogenic nonepileptic seizures (PNES) are to be considered in the differential diagnosis of a transient loss of consciousness. Their discrimination from syncope, epileptic seizures or vascular events can be difficult and requires profound knowledge about the semiology and clinical presentation of PNES and their differential diagnoses. Erroneous diagnoses and the resulting therapies lead to elevated morbidity, elevated costs and a poorer outcome. The aim of the present article is to provide an overview on PNES and their delineation from the clinical pictures of epilepsy and syncope.

Wearable sensors for clinical applications in epilepsy, Parkinson's disease, and stroke: a mixed-methods systematic review

OBJECTIVES

Wearable technology is increasingly used to monitor neurological disorders. The purpose of this systematic review was to synthesize knowledge from quantitative and qualitative clinical researches using wearable sensors in epilepsy, Parkinson's disease (PD), and stroke.

METHODS

A systematic literature search was conducted in PubMed and Scopus spanning from 1995 to January 2017. A synthesis of the main findings, reported adherence to wearables and missing data from quantitative studies, is provided. Clinimetric properties of measures derived from wearables in laboratory, free activities in hospital, and free-living environment were also evaluated. Qualitative thematic synthesis was conducted to explore user experiences and acceptance of wearables.

RESULTS

In total, 56 studies (50 reporting quantitative and 6 reporting qualitative data) were included for data extraction and synthesis. Among studies reporting quantitative data, 5 were in epilepsy, 21 PD, and 24 studies in stroke. In epilepsy, wearables are used to detect and differentiate seizures in hospital settings. In PD, the focus is on quantification of cardinal motor symptoms and medication-evoked adverse symptoms in both laboratory and free-living environment. In stroke upper extremity activity, walking and physical activity have been studied in laboratory and during free activities. Three analytic themes emerged from thematic synthesis of studies reporting qualitative data: acceptable integration in daily life, lack of confidence in technology, and the need to consider individualization.

CONCLUSIONS

Wearables may provide information of clinical features of interest in epilepsy, PD and stroke, but knowledge regarding the clinical utility for supporting clinical decision making remains to be established.

Gaussian mixture model for the identification of psychogenic non-epileptic seizures using a wearable accelerometer sensor

Any abnormal hypersynchronus activity of neurons can be characterized as an epileptic seizure (ES). A broad class of non-epileptic seizures is comprised of Psychogenic non-epileptic seizures (PNES). PNES are paroxysmal events, which mimics epileptic seizures and pose a diagnostic challenge with epileptic seizures due to their clinical similarities. The diagnosis of PNES is done using video-electroencephalography (VEM) monitoring. VEM being a resource intensive process calls for alternative methods for detection of PNES. There is now an emerging interest in the use of accelerometer based devices for the detection of seizures. In this work, we present an algorithm based on Gaussian mixture model (GMM's) for the identification of PNES, ES and normal movements using a wrist-worn accelerometer device. Features in time, frequency and wavelet domain are extracted from the norm of accelerometry signal. All events are then classified into three classes i.e normal, PNES and ES using a parametric estimate of the multivariate normal probability density function. An algorithm based on GMM's allows us to accurately model the non-epileptic and epileptic movements, thus enhancing the overall predictive accuracy of the system. The new algorithm was tested on data collected from 16 patients and showed an overall detection accuracy of 91% with 25 false alarms.

Proposal for best practice in the use of video-EEG when psychogenic non-epileptic seizures are a possible diagnosis

The gold-standard for the diagnosis of psychogenic non-epileptic seizures (PNES) is capturing an attack with typical semiology and lack of epileptic ictal discharges on video-EEG. Despite the importance of this diagnostic test, lack of standardisation has resulted in a wide variety of protocols and reporting practices. The goal of this review is to provide an overview of research findings on the diagnostic video-EEG procedure, in both the adult and paediatric literature. We discuss how uncertainties about the ethical use of suggestion can be resolved, and consider what constitutes best clinical practice. We stress the importance of ictal observation and assessment and consider how diagnostically useful information is best obtained. We also discuss the optimal format of video-EEG reports; and of highlighting features with high sensitivity and specificity to reduce the risk of miscommunication. We suggest that over-interpretation of the interictal EEG, and the failure to recognise differences between typical epileptic and nonepileptic seizure manifestations are the greatest pitfalls in neurophysiological assessment of patients with PNES. Meanwhile, under-recognition of semiological pointers towards frontal lobe seizures and of the absence of epileptiform ictal EEG patterns during some epileptic seizure types (especially some seizures not associated with loss of awareness), may lead to erroneous PNES diagnoses. We propose that a standardised approach to the video-EEG examination and the subsequent written report will facilitate a clear communication of its import, improving diagnostic certainty and thereby promoting appropriate patient management.

Stereotypy of psychogenic nonepileptic seizures

Psychogenic nonepileptic seizures (PNES) are defined as paroxysmal episodes in which epileptic semiology features are manifested, without the characteristic concomitant electrical discharges seen in epileptic seizures. Although many studies have dealt with semiologic classification of PNES, most of the studies did not raise the question of consistency of PNES in the same patient. The aim of this study was to measure the degree of consistency of PNES among individual patients. We retrospectively reviewed medical records and video- EEG records of all adult patients who underwent monitoring in our center from August 1st 2013 to May 31st 2015. Those who were diagnosed with PNES with or without a background of epilepsy were selected for this study. In order to check consistency between seizures, we analyzed patients who had more than one recorded seizure during monitoring. In case of more than 2 recorded seizures, the first two seizures were analyzed. We found 53 patients who had PNES during this period, 29 of them had more than one seizure. All seizures in the same patient were in the same semiology category. In patients with either motor rhythmic or complex motor seizures, we found a main anatomical region involved. The main anatomical region involved was the same in 13 out of 14 patients. Movement frequency was highly similar between the seizures of the same patient, while duration of seizures was significantly different. Despite significant differences in duration between the first and second seizure in patients with PNES, all other aspects tested were highly similar. This shows that recurrent PNES in the same patient are stereotypic. This supports the hypothesis that PNES is probably a dissociative disorder.

Spectral Analysis of Acceleration Data for Detection of Generalized Tonic-Clonic Seizures

Generalized tonic-clonic seizures (GTCSs) can be underestimated and can also increase mortality rates. The monitoring devices used to detect GTCS events in daily life are very helpful for early intervention and precise estimation of seizure events. Several studies have introduced methods for GTCS detection using an accelerometer (ACM), electromyography, or electroencephalography. However, these studies need to be improved with respect to accuracy and user convenience. This study proposes the use of an ACM banded to the wrist and spectral analysis of ACM data to detect GTCS in daily life. The spectral weight function dependent on GTCS was used to compute a GTCS-correlated score that can effectively discriminate between GTCS and normal movement. Compared to the performance of the previous temporal method, which used a standard deviation method, the spectral analysis method resulted in better sensitivity and fewer false positive alerts. Finally, the spectral analysis method can be implemented in a GTCS monitoring device using an ACM and can provide early alerts to caregivers to prevent risks associated with GTCS.

Patient-centered design criteria for wearable seizure detection devices

INTRODUCTION

Epilepsy is a common neurological condition. Seizure diary reports and patient- or caregiver-reported seizure counts are often inaccurate and underestimated. Many caregivers express stress and anxiety about the patient with epilepsy having seizures when they are not present. Therefore, a need exists for the ability to recognize and/or detect a seizure in the home setting. However, few studies have inquired on detection device features that are important to patients and their caregivers.

METHODS

A survey instrument utilizing a population of patients and caregivers was created to obtain information on the design criteria most desired for patients with epilepsy in regard to wearable devices.

RESULTS

One thousand one hundred sixty-eight responses were collected. Respondents thought that sensors for muscle signal (61.4%) and heart rate (58.0%) would be most helpful followed by the O₂ sensor (41.4%). There was more interest in these three sensor types than for an accelerometer (25.5%). There was very little interest in a microphone (8.9%), galvanic skin response sensor (8.0%), or a barometer (4.9%). Based on a rating scale of 1-5 with 5 being the most important, respondents felt that "detecting all seizures" (4.73) is the most important device feature followed by "text/email alerts" (4.53), "comfort" (4.46), and "battery life" (4.43) as an equally important group of features. Respondents felt that "not knowing device is for seizures" (2.60) and "multiple uses" (2.57) were equally the least important device features. Average ratings differed significantly across age groups for the following features: button, multiuse, not knowing device is for seizures, alarm, style, and text ability. The p-values were all<0.002. Eighty-two point five percent of respondents [95% confidence interval: 80.0%, 84.7%] were willing to pay more than $100 for a wearable seizure detection device, and 42.8% of respondents [95% confidence interval: 39.8%, 45.9%] were willing to pay more than $200.

CONCLUSIONS

Our survey results demonstrated that patients and caregivers have design features that are important to them in regard to a wearable seizure detection device. Overall, the ability to detect all seizures rated highest among respondents which continues to be an unmet need in the community with epilepsy in regard to seizure detection. Additional uses for a wearable were not as important. Based on our results, it is important that an alert (via test and/or email) for events be a portion of the system. A reasonable price point appears to be around $200 to $300. An accelerometer was less important to those surveyed when compared with the use of heart rate, oxygen saturation, or muscle twitches/signals. As further products become developed for use in other health arenas, it will be important to consider patient and caregiver desires in order to meet the need and address the gap in devices that currently exist.

Generalized Models for the Classification of Abnormal Movements in Daily Life and its Applicability to Epilepsy Convulsion Recognition

The identification and the modeling of epilepsy convulsions during everyday life using wearable devices would enhance patient anamnesis and monitoring. The psychology of the epilepsy patient penalizes the use of user-driven modeling, which means that the probability of identifying convulsions is driven through generalized models. Focusing on clonic convulsions, this pre-clinical study proposes a method for generating a type of model that can evaluate the generalization capabilities. A realistic experimentation with healthy participants is performed, each with a single 3D accelerometer placed on the most affected wrist. Unlike similar studies reported in the literature, this proposal makes use of [Formula: see text] cross-validation scheme, in order to evaluate the generalization capabilities of the models. Event-based error measurements are proposed instead of classification-error measurements, to evaluate the generalization capabilities of the model, and Fuzzy Systems are proposed as the generalization modeling technique. Using this method, the experimentation compares the most common solutions in the literature, such as Support Vector Machines, [Formula: see text]-Nearest Neighbors, Decision Trees and Fuzzy Systems. The event-based error measurement system records the results, penalizing those models that raise false alarms. The results showed the good generalization capabilities of Fuzzy Systems.

Classification of convulsive psychogenic non-epileptic seizures using histogram of oriented motion of accelerometry signals

A seizure is caused due to sudden surge of electrical activity within the brain. There is another class of seizures called psychogenic non-epileptic seizure (PNES) that mimics epilepsy, but is caused due to underlying psychology. The diagnosis of PNES is done using video-electroencephalography monitoring (VEM), which is a resource intensive process. Recently, accelerometers have been shown to be effective in classification of epileptic and non-epileptic seizures. In this work, we propose a novel feature called histogram of oriented motion (HOOM) extracted from accelerometer signals for classification of convulsive PNES. An automated algorithm based on HOOM is proposed. The algorithm showed a high sensitivity of (93.33%) and an overall accuracy of (80%) in classifying convulsive PNES.

Classification of convulsive psychogenic non-epileptic seizures using muscle transforms obtained from accelerometry signal

Convulsive psychogenic non-epileptic seizure (PNES) can be characterized as events which mimics epileptic seizures but do not show any characteristic changes on electroencephalogram (EEG). Correct diagnosis requires video-electroencephalography monitoring (VEM) as the diagnosis of PNES is extremely difficult in primary health care. Recent work has demonstrated the usefulness of accelerometry signal taken during a seizure in classification of PNES. In this work, a new direction has been explored to understand the role of different muscles in PNES. This is achieved by modeling the muscle activity of ten different upper limb muscles as a resultant function of accelerometer signal. Using these models, the accelerometer signals recorded from convulsive epileptic patients were transformed into individual muscle components. Based on this, an automated algorithm for classification of convulsive PNES is proposed. The algorithm calculates four wavelet domain features based on signal power, approximate entropy, kurtosis and signal skewness. These features were then used to build a classification model using support vector machines (SVM) classifier. It was found that the transforms corresponding to anterior deltoid and brachioradialis results in good PNES classification accuracy. The algorithm showed a high sensitivity of 93.33% and an overall PNES classification accuracy of 89% with the transform corresponding to anterior deltoid.

Automatic Detection and Classification of Convulsive Psychogenic Nonepileptic Seizures Using a Wearable Device

Epilepsy is one of the most common neurological disorders and patients suffer from unprovoked seizures. In contrast, psychogenic nonepileptic seizures (PNES) are another class of seizures that are involuntary events not caused by abnormal electrical discharges but are a manifestation of psychological distress. The similarity of these two types of seizures poses diagnostic challenges that often leads in delayed diagnosis of PNES. Further, the diagnosis of PNES involves high-cost hospital admission and monitoring using video-electroencephalogram machines. A wearable device that can monitor the patient in natural setting is a desired solution for diagnosis of convulsive PNES. A wearable device with an accelerometer sensor is proposed as a new solution in the detection and diagnosis of PNES. The seizure detection algorithm and PNES classification algorithm are developed. The developed algorithms are tested on data collected from convulsive epileptic patients. A very high seizure detection rate is achieved with 100% sensitivity and few false alarms. A leave-one-out error of 6.67% is achieved in PNES classification, demonstrating the usefulness of wearable device in the diagnosis of PNES.

New insights into psychogenic nonepileptic seizures 2011-2014

PURPOSE

There has been a rapid increase in the rate of publications about psychogenic nonepileptic seizures (PNES). This review summarises insights from the 50 most important original articles about PNES published since 2011 and describes the advances made in the understanding of PNES over the last 3 years.

METHOD

We carried out a systematic literature search of all English language publications about PNES published between October 2011 and October 2014 on Scopus, Ovid Medline and Web of Knowledge, and inspected all abstracts. Having excluded all review articles, case reports, conference abstracts, articles exploring PNES in children, and articles not actually focussing on PNES, we considered 150 papers for inclusion in this review. We assessed the quality of the identified studies and used expert judgement to identify the 50 most important publications from the review period and composed a narrative review based on these original papers.

RESULTS

Almost one half of the studies initially identified only provided Class 4 evidence. Recent work has provided more support for a biopsychosocial account of PNES. It has illustrated the heterogeneity of PNES, identifying varying and distinct psychological profiles of individuals with this disorder. These findings suggest that intervention needs to be flexible or adaptive if it is appropriately to target the different mechanisms which may give rise to PNES. Several educational and psychotherapeutic interventions for PNES have been described, but sufficiently powered randomised controlled trials are yet to be undertaken. Recent research using social, economic and quality of life indicators has provided further evidence of the societal and individual burden of PNES.

CONCLUSION

The research into PNES published over the last 3 years has deepened our understanding of the condition as a biopsychosocial disorder which is neither a "physical" nor a "psychological" condition. A number of small studies have demonstrated the potential of educational and psychotherapeutic treatments, but rigorous and sufficiently large trials still need to be conducted to determine the effectiveness of these interventions.

Automated differentiation between epileptic and nonepileptic convulsive seizures

Our objective was the clinical validation of an automated algorithm based on surface electromyography (EMG) for differentiation between convulsive epileptic and psychogenic nonepileptic seizures (PNESs). Forty-four consecutive episodes with convulsive events were automatically analyzed with the algorithm: 25 generalized tonic-clonic seizures (GTCSs) from 11 patients, and 19 episodes of convulsive PNES from 13 patients. The gold standard was the interpretation of the video-electroencephalographic recordings by experts blinded to the EMG results. The algorithm correctly classified 24 GTCSs (96%) and 18 PNESs (95%). The overall diagnostic accuracy was 95%. This algorithm is useful for distinguishing between epileptic and psychogenic convulsive seizures.

Seizure detection, seizure prediction, and closed-loop warning systems in epilepsy

Nearly one-third of patients with epilepsy continue to have seizures despite optimal medication management. Systems employed to detect seizures may have the potential to improve outcomes in these patients by allowing more tailored therapies and might, additionally, have a role in accident and SUDEP prevention. Automated seizure detection and prediction require algorithms which employ feature computation and subsequent classification. Over the last few decades, methods have been developed to detect seizures utilizing scalp and intracranial EEG, electrocardiography, accelerometry and motion sensors, electrodermal activity, and audio/video captures. To date, it is unclear which combination of detection technologies yields the best results, and approaches may ultimately need to be individualized. This review presents an overview of seizure detection and related prediction methods and discusses their potential uses in closed-loop warning systems in epilepsy.