•Components and modalities of psychoeducation interventions vary widely.•Methods and quality of evaluation vary between studies.•There are indications that psychoeducation has benefits as an ...intervention in its own right.•Psychoeducation likely best placed as part of a stepped care model.
Cognitive behavioural therapy (CBT) and other psychological approaches have a growing evidence base for treating Non-Epileptic Seizures (NES). However, communication about the diagnosis can be difficult for clinicians and is not always well received. It is thought that Psychoeducation about NES may improve engagement with treatment such as CBT and may contribute to reductions in the frequency of seizures and improvements in health related quality of life. However, psychoeducational components of treatment are often not evaluated in isolation meaning the specific benefit to patients is not currently well understood.
The researchers aimed to examine the outcomes associated with psychoeducational interventions and the content of these programmes for NES.
A scoping review was undertaken across four databases with seventeen eligible studies identified which were charted to analyse the data.
Findings revealed that components and modalities of interventions varied as did methods of evaluating the interventions. A number of different outcome measures were used and not all studies reported the significance of findings. Results across studies were inconsistent; however, there was a general trend across the studies of symptom reduction and improvement in mental health.
Findings illustrate that psychoeducation has potential benefits as an intervention in its own right. However, it may be best placed as a second step in a stepped care model, between initial diagnosis and further psychological treatment. The present literature needs replication and more robust studies for more certain conclusions to be drawn.
The first and most important step in establishing diagnosis of epilepsy consists of careful history taking from patients and witnesses. The clinical evaluation of the event will lead the indication ...for further diagnostic tests including e.g. EEG and MRI. Hence, identifying the paroxysmal event as epileptic or non‐epileptic is the very first step in the diagnostic process. Paroxysmal events pose a clinical challenge, as these are unpredictable and do not usually occur in the doctor's office. History taking, hunting for witness reports and home‐video recordings are the main tools to conclude whether a paroxysmal event is a seizure or not. In this review, we describe the most common differential diagnoses of epileptic seizures, including syncope, psychogenic non‐epileptic seizures, as well as a variety of paroxysmal conditions and behaviours of all age groups. Misdiagnosis of non‐epileptic events as epilepsy may not only defer the correct diagnosis and treatment but also poses additional risk by prescribing antiepileptic drugs unnecessarily. Moreover, missing the diagnosis of epilepsy implies risk of additional seizures and therefore possibly injuries, sudden death in people with epilepsy, or status epilepticus. Studies have shown that patient and witness accounts are unreliable in a high percentage of cases. Therefore, the core competency of doctors and medical professionals assessing paroxysmal events is knowledge of the clinical features that help define the different aetiologies, thus empowering them to establish the most accurate appraisal of an event. Published with video sequences.
Background and objectives: Various factors facilitate seizures in patients with epilepsy. The relationship between the phases of the moon and neuropsychiatric conditions has been a matter of ...curiosity. The present study investigated whether patient presentations to the emergency department with epileptic seizures vary according to the phases of the moon, seasons, and daily air temperature. Materials and method: The study retrospectively included patients who presented to the emergency department with epileptic seizures over a one-year period. Patients with provoked seizures (head trauma, intracranial hemorrhage, etc.), pregnant women, and patients aged under 18 years were excluded. Patients’ age, gender, date and time of presentation to the emergency department were recorded. The effects of the phases of the moon and seasons on these presentations were investigated. Results:Total 255 patients (176 male, 79 female) met the inclusion criteria of the study. The majority of patients (67.1%) were aged 18-44 years. Majority of the patients (41.2%) did not previously used epileptic medication. The laboratory tests showed that the mean blood pH and lactate values were 7.31±0.11 and 4.59±4.12 mmol/L respectively. No statistically significant (p>0.05) relationship was observed regarding frequency of presentations of epileptic seizures and the season and phases of the moon. Conclusion: The results of this study showed that the phases of the moon, air temperature, and seasons did not affect the frequency of epileptic seizures. July 2024; Vol. 18(2):006 DOI: https://doi.org/10.55010/imcjms.18.018 *Correspondence: Erdal Yavuz, Department of Emergency Medicine, Adiyaman University Medical Faculty, Adiyaman, Turkey. Email: erdal_yavuz15@hotmail.com
Psychogenic Nonepileptic Seizures (PNES) superficially resemble epileptic seizures but are thought to have a psychological rather than epileptic basis. Patients with PNES vary widely in terms of ...background, personality profiles, comorbidities, response to treatment and outcomes. Previous accounts interpreting these seizures as the activation of dissociated material, a physical manifestation of emotional distress, hard-wired reflex responses, or learned behaviours cannot explain key features of the phenomenon. Drawing on a brief review of the literature on etiology, correlates and phenomenology of PNES, this paper integrates existing approaches and data within a novel explanatory framework that applies to all PNES patients with subjectively involuntary seizures. Following the Integrative Cognitive Model of medically unexplained symptoms, we suggest that the central feature of all PNES is the automatic activation of a mental representation of seizures (the “seizure scaffold”) in the context of a high level inhibitory processing dysfunction. This often arises in response to elevated autonomic arousal, and may disrupt the individual's awareness of distressing material, but can become divorced from abnormal autonomic and emotional activity. This model accounts both for existing findings and the heterogeneity of patients with PNES, whilst leading to a number of novel hypotheses against which it can be evaluated.
•Current models of PNES can account for some but not all of the available data.•Automatic activation of seizure representations in memory may be a unifying process.•Suppression of arousal and distress are typical maintaining factors for PNES.•Inhibitory dysfunction, often arising from chronic stress, is a key vulnerability.•Trauma exposure is common but neither necessary nor sufficient for PNES to occur.
•The use of prolactin could be a valuable adjunct to differentiate GTCS from PNES.•Prolactin is of limited use for differentiating FIAS or FAS from PNES.•A negative prolactin measure is not ...predictive of PNES.
Psychogenic non-epileptic seizures (PNES) are conversion disorders with functional neurological symptoms that can resemble epileptic seizures (ES). We conducted a systematic review to obtain an overview of the value of prolactin (PRL) levels in the differential diagnosis between PNES and ES.
We searched PubMed, EMBASE, and Cochrane Library databases for studies published up to June 4th, 2020. Published studies were included if they fulfilled the following criteria: original research on PRL changes after ES and PNES. By applying Bayes’ theorem, we calculated the predicted values of PRL with pretest probabilities of 90 % and 75 % in ES.
Sixteen studies were included in this review. All the studies showed that PRL levels increase after ES, especially 10–20 min after ES, when the elevation was most obvious. In studies where capillary PRL level measurements were included, the median sensitivity in the diagnosis of ES (all epileptic seizure types), generalized tonic clonic seizures (GTCS), focal impaired awareness seizures (FIAS), and focal aware seizures (FAS) was 67.3 %, 66.7 %, 33.9 %, and 11.1 %, respectively. The median specificity in the diagnosis of ES was 99.1 %. By using Bayes’ theorem, when we used the median specificity and sensitivity for predictive value calculation, assuming a pretest probability of 90 %, a positive PRL measure was highly predictive (99 %) of all types of ES, and negative predictive values were all below 30 %. When we used the lowest specificity and sensitivity for predictive value calculation, assuming a pretest probability of 75 %, ES and GTCS had positive predictive values of 77.2 % and 81.0 %, respectively; the negative predictive values of PRL in ES and GTCS were 26.2 % and 29.6 %, respectively.
The use of PRL could be a useful adjunct to differentiate GTCS from PNES. However, PRL levels are of limited use for differentiating FIAS or FAS from PNES, and a negative PRL measure is not predictive of PNES.
ABSTRACT
Psychogenic non‐epileptic seizures (PNES), also known as dissociative seizures, are paroxysms of altered subjective experience, involuntary movements and reduced self‐control that can ...resemble epileptic seizures, but have distinct clinical characteristics and a complex neuropsychiatric aetiology. They are common, accounting for over 10% of seizure emergencies and around 30% of cases in tertiary epilepsy units, but the diagnosis is often missed or delayed. The recently proposed “integrative cognitive model” accommodates current research on experiential, psychological and biological risk factors for the development of PNES, but in view of the considerable heterogeneity of presentations and medical context, it is not certain that a universal model can capture the full range of PNES manifestations. This narrative review addresses key learning objectives of the ILAE curriculum by describing the demographic profile, common risk factors (such as trauma or acute stress) and comorbid disorders (such as other dissociative and functional disorders, post‐traumatic stress disorder, depressive and anxiety disorders, personality disorders, comorbid epilepsy, head injury, cognitive and sleep problems, migraine, pain, and asthma). The clinical implications of demographic and aetiological factors for diagnosis and treatment planning are addressed.
•Epilepsy and psychogenic seizures are associated with elevated psychopathology.•Somatisation, and general and phobic anxiety are particular markers of psychogenic seizures.•Machine learning produced ...a SCL-90-R subset maximising PNES classification accuracy.•PNES classification accuracy was not sufficient for diagnostic use.
Similarities in clinical presentations between epileptic seizures (ES) and psychogenic non-epileptic seizures (PNES) produces a risk of misdiagnosis. Video-EEG monitoring (VEM) is the diagnostic gold standard, but involves significant cost and time commitment, suggesting a need for efficient screening tools.
628 patients were recruited from an inpatient VEM unit; 293 patients with ES, 158 with PNES, 31 both ES and PNES, and 146 non-diagnostic. Patients completed the SCL-90-R, a standardised 90-item psychopathology instrument. Bayesian linear models were computed to investigate whether SCL-90-R domain scores or the overall psychopathology factor p differed between groups. Receiver operating characteristic (ROC) curves were computed to investigate the PNES classification accuracy of each domain score and p. A machine learning algorithm was also used to determine which subset of SCL-90-R items produced the greatest classification accuracy.
Evidence was found for elevated scores in PNES compared to ES groups in the symptom domains of anxiety (b = 0.47, 95%HDI = 0.10, 0.80), phobic anxiety (b = 1.32, 95%HDI = 0.98, 1.69), somatisation (b = 0.84, 95%HDI = 0.49, 1.20), and the general psychopathology factor p (b = 1.35, 95%HDI = 0.86, 1.82). Of the SCL-90-R domain scores, somatisation produced the highest classification accuracy (AUC = 0.74, 95%CI = 0.69, 0.79). The genetic algorithm produced a 6-item subset from the SCL-90-R, which produced comparable classification accuracy to the somatisation scores (AUC = 0.73, 95%CI = 0.64, 0.82).
Compared to patients with ES, patients with PNES report greater symptoms of somatisation, general anxiety, and phobic anxiety against a background of generally elevated psychopathology. While self-reported psychopathology scores are not accurate enough for diagnosis in isolation, elevated psychopathology in these domains should raise the suspicion of PNES in clinical settings.
Psychological therapies are considered the treatment of choice for functional/dissociative seizures (FDSs). Although most previous studies have focused on seizure persistence or frequency, it has ...been argued that well‐being or health‐related quality of life outcomes may actually be more meaningful. This study contributes by summarizing and meta‐analyzing non‐seizure outcomes to quantify the effectiveness of psychological treatment in this patient group. A pre‐registered systematic search identified treatment studies (e.g., cohort studies, controlled trials) in FDSs. Data from these studies were synthesized using multi‐variate random‐effects meta‐analysis. Moderators of treatment effect were examined using treatment characteristics, sample characteristics, and risk of bias. A total of 171 non‐seizure outcomes across 32 studies with a pooled sample size of N = 898 yielded a pooled effect‐size of d = .51 (moderate effect size). The outcome domain assessed and the type of psychological treatment were significant moderators of reported outcomes. Greater rates of improvement were demonstrated for outcomes assessing general functioning. Behavioral treatments emerged as particularly effective interventions. Psychological interventions are associated with clinical improvements across a broad array of non‐seizure outcomes, over and above seizure frequency, in adults with FDSs.
•Automatic detection of epileptic seizures in EEG signals using new CADS based on artificial intelligence techniques.•The combining of fuzzy entropies for features extraction.•Autoencoder with ...proposed layers is used for dimension reduction of feature matrix.•The ANFIS classifier with BS optimizer is used for classification.
Epileptic seizures are one of the most crucial neurological disorders, and their early diagnosis will help the clinicians to provide accurate treatment for the patients. The electroencephalogram (EEG) signals are widely used for epileptic seizures detection, which provides specialists with substantial information about the functioning of the brain. In this paper, a novel diagnostic procedure using fuzzy theory and deep learning techniques is introduced. The proposed method is evaluated on the Bonn University dataset with six classification combinations and also on the Freiburg dataset. The tunable-Q wavelet transform (TQWT) is employed to decompose the EEG signals into different sub-bands. In the feature extraction step, 13 different fuzzy entropies are calculated from different sub-bands of TQWT, and their computational complexities are calculated to help researchers choose the best set for various tasks. In the following, an autoencoder (AE) with six layers is employed for dimensionality reduction. Finally, the standard adaptive neuro-fuzzy inference system (ANFIS), and also its variants with grasshopper optimization algorithm (ANFIS-GOA), particle swarm optimization (ANFIS-PSO), and breeding swarm optimization (ANFIS-BS) methods are used for classification. Using our proposed method, ANFIS-BS method has obtained an accuracy of 99.74% in classifying into two classes and an accuracy of 99.46% in ternary classification on the Bonn dataset and 99.28% on the Freiburg dataset, reaching state-of-the-art performances on both of them.
PDF
