Rating scale for psychogenic nonepileptic seizures: Scale development and clinimetric testing

Cianci, Vittoria; Ferlazzo, Edoardo; Condino, Francesca; Mauvais, Hélène Somma; Farnarier, G; Labate, Angelo; Latella, Maria Adele; Gasparini, Sara; Branca, Damiano; Pucci, Franco; Vazzana, Francesco; Gambardella, Antonio; Aguglia, Umberto

doi:10.1016/j.yebeh.2011.03.025

Cited by 22 publications

(19 citation statements)

References 22 publications

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“…Criterion validity for new measures of FND symptoms is difficult to assess due to a lack of validated gold standards. However, moderate–strong correlations (r>0.3) with measures of similar or related constructs were noted for some scales, suggesting acceptable convergent validity 32–34 36. The CDS and CDS-R also differentiated patients with FND from healthy controls 35 36…”

Section: Resultsmentioning

confidence: 96%

“…Five articles were eligible (online supplementary appendix 1 for the PRISMA diagram), which are summarised in table 1. Three articles described clinician-rated scales, two assessing functional movement disorder (FMD) symptoms: Psychogenic Movement Disorder Rating Scale (PMDRS),32 Simplified Functional Movement Disorder Rating Scale (S-FMDRS)33 and one clinician-rated measure for functional (ie, psychogenic non-epileptic/dissociative) seizures 34. Two patient-rated measures assessed a range of FND symptoms in children (Conversion Disorder Scale (CDS) and Conversion Disorder Scale—Revised (CDS-R)) 35 36.…”

Section: Resultsmentioning

confidence: 99%

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Outcome measurement in functional neurological disorder: a systematic review and recommendations

Pick

Anderson

Asadi‐Pooya

et al. 2020

J Neurol Neurosurg Psychiatry

100

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ObjectivesWe aimed to identify existing outcome measures for functional neurological disorder (FND), to inform the development of recommendations and to guide future research on FND outcomes.MethodsA systematic review was conducted to identify existing FND-specific outcome measures and the most common measurement domains and measures in previous treatment studies. Searches of Embase, MEDLINE and PsycINFO were conducted between January 1965 and June 2019. The findings were discussed during two international meetings of the FND-Core Outcome Measures group.ResultsFive FND-specific measures were identified—three clinician-rated and two patient-rated—but their measurement properties have not been rigorously evaluated. No single measure was identified for use across the range of FND symptoms in adults. Across randomised controlled trials (k=40) and observational treatment studies (k=40), outcome measures most often assessed core FND symptom change. Other domains measured commonly were additional physical and psychological symptoms, life impact (ie, quality of life, disability and general functioning) and health economics/cost–utility (eg, healthcare resource use and quality-adjusted life years).ConclusionsThere are few well-validated FND-specific outcome measures. Thus, at present, we recommend that existing outcome measures, known to be reliable, valid and responsive in FND or closely related populations, are used to capture key outcome domains. Increased consistency in outcome measurement will facilitate comparison of treatment effects across FND symptom types and treatment modalities. Future work needs to more rigorously validate outcome measures used in this population.

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Section: Resultsmentioning

confidence: 96%

Section: Resultsmentioning

confidence: 99%

Outcome measurement in functional neurological disorder: a systematic review and recommendations

Pick

Anderson

Asadi‐Pooya

et al. 2020

J Neurol Neurosurg Psychiatry

100

View full text Add to dashboard Cite

show abstract

“…Many aspects of PNES were highlighted over the years, such as pathogenesis [7,8], psychiatric comorbidities [7,9], benefits and harms of provocative techniques [10,11], usefulness of clinimetric scale [12], QoL [13], and treatment challenges [7,14]. As regards pathogenesis, it is now recognized that there may be an organic basis in patients with PNES.…”

Section: To the Editormentioning

confidence: 99%

About some behavioral and psychosocial aspects related to epilepsy

Ferlazzo

Aguglia

2014

Epilepsy & Behavior

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“…Psychogenic nonepileptic seizures (PNES) are sudden behavioral changes mimicking epileptic seizures without ictal electroencephalography (EEG) changes [1,2]. PNES have been linked to dysfunction in the processing of psychological or social distress, abuse during childhood, or severe traumatic events [3,4].…”

Section: Introductionmentioning

confidence: 99%

“…Misdiagnosis of epilepsy may lead to treatments with antiepileptic drugs (AEDs), posing the risk of iatrogenic morbidity and elevated cost for the healthcare system [9]. The gold standard for PNES diagnosis is the visual examination of clinical events [2,3,10] captured during video EEG, either occurring spontaneously or provoked by suggestion techniques. These methods are time-consuming and ethically disputable [11].…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Machine-Learning-Based Software Pipeline to Classify EEG Signals: A Case Study on PNES vs. Control Subjects

Varone

Gasparini

Ferlazzo

et al. 2020

Sensors

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The diagnosis of psychogenic nonepileptic seizures (PNES) by means of electroencephalography (EEG) is not a trivial task during clinical practice for neurologists. No clear PNES electrophysiological biomarker has yet been found, and the only tool available for diagnosis is video EEG monitoring with recording of a typical episode and clinical history of the subject. In this paper, a data-driven machine learning (ML) pipeline for classifying EEG segments (i.e., epochs) of PNES and healthy controls (CNT) is introduced. This software pipeline consists of a semiautomatic signal processing technique and a supervised ML classifier to aid clinical discriminative diagnosis of PNES by means of an EEG time series. In our ML pipeline, statistical features like the mean, standard deviation, kurtosis, and skewness are extracted in a power spectral density (PSD) map split up in five conventional EEG rhythms (delta, theta, alpha, beta, and the whole band, i.e., 1–32 Hz). Then, the feature vector is fed into three different supervised ML algorithms, namely, the support vector machine (SVM), linear discriminant analysis (LDA), and Bayesian network (BN), to perform EEG segment classification tasks for CNT vs. PNES. The performance of the pipeline algorithm was evaluated on a dataset of 20 EEG signals (10 PNES and 10 CNT) that was recorded in eyes-closed resting condition at the Regional Epilepsy Centre, Great Metropolitan Hospital of Reggio Calabria, University of Catanzaro, Italy. The experimental results showed that PNES vs. CNT discrimination tasks performed via the ML algorithm and validated with random split (RS) achieved an average accuracy of 0.97 ± 0.013 (RS-SVM), 0.99 ± 0.02 (RS-LDA), and 0.82 ± 0.109 (RS-BN). Meanwhile, with leave-one-out (LOO) validation, an average accuracy of 0.98 ± 0.0233 (LOO-SVM), 0.98 ± 0.124 (LOO-LDA), and 0.81 ± 0.109 (LOO-BN) was achieved. Our findings showed that BN was outperformed by SVM and LDA. The promising results of the proposed software pipeline suggest that it may be a valuable tool to support existing clinical diagnosis.

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Rating scale for psychogenic nonepileptic seizures: Scale development and clinimetric testing

Cited by 22 publications

References 22 publications

Outcome measurement in functional neurological disorder: a systematic review and recommendations

Outcome measurement in functional neurological disorder: a systematic review and recommendations

About some behavioral and psychosocial aspects related to epilepsy

A Comprehensive Machine-Learning-Based Software Pipeline to Classify EEG Signals: A Case Study on PNES vs. Control Subjects

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