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Objective: Electroencephalography (EEG) interpretations through visual (by human raters) and automated (by computer technology) analysis were still not reliable for the diagnosis of nonconvulsive status epilepticus (NCSE). This study aimed to identify typical pitfalls in the EEG analysis and make suggestions as to how those pitfalls might be avoided. Methods: We analyzed the EEG recordings of individuals who had clinically confirmed or suspected NCSE. Epileptiform EEG activity during seizures (ictal discharges) was visually analyzed by 2 independent raters. We investigated whether unreliable EEG visual interpretations quantified by low interrater agreement can be predicted by the characteristics of ictal discharges and individuals' clinical data. In addition, the EEG recordings were automatically analyzed by in-house algorithms. To further explore the causes of unreliable EEG interpretations, 2 epileptologists analyzed EEG patterns most likely misinterpreted as ictal discharges based on the differences between the EEG interpretations through the visual and automated analysis. Results: Short ictal discharges with a gradual onset (developing over 3 s in length) were liable to be misinterpreted. An extra 2 min of ictal discharges contributed to an increase in the kappa statistics of >0.1. Other problems were the misinterpretation of abnormal background activity (slow-wave activities, other abnormal brain activity, and the ictal-like movement artifacts), continuous interictal discharges, and continuous short ictal discharges. Conclusion: A longer duration criterion for NCSE-EEGs than 10 s that is commonly used in NCSE working criteria is recommended. Using knowledge of historical EEGs, individualized algorithms, and context-dependent alarm thresholds may also avoid the pitfalls.

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OBJECTIVE: To assess the performance of a multimodal seizure detection device, first tested in adults (sensitivity 86%, PPV 49%), in a pediatric cohort living at home or residential care. METHODS: In this multicenter, prospective, video-controlled cohort-study, nocturnal seizures were detected by heartrate and movement changes in children with epilepsy and intellectual disability. Participants with a history of >1 monthly major motor seizure wore Nightwatch bracelet at night for 3 months. Major seizures were defined as tonic-clonic, generalized tonic >30 s, hyperkinetic, or clusters (>30 min) of short myoclonic or tonic seizures. The video of all events (alarms and nurse diaries) and about 10% of whole nights were reviewed to classify major seizures, and minor or no seizures. RESULTS: Twenty-three participants with focal or generalized epilepsy and nightly motor seizures were evaluated during 1511 nights, with 1710 major seizures. First 1014 nights, 4189 alarms occurred with average of 1.44/h, showing average sensitivity of 79.9% (median 75.4%) with mean PPV of 26.7% (median 11.1%) and false alarm rate of 0.2/hour. Over 90% of false alarms in children was due to heart rate (HR) part of the detection algorithm. To improve this rate, an adaptation was made such that the alarm was only triggered when the wearer was in horizontal position. For the remaining 497 nights, this was tested prospectively, 384 major seizures occurred. This resulted in mean PPV of 55.5% (median 58.1%) and a false alarm rate 0.08/h while maintaining a comparable mean sensitivity of 79.4% (median 93.2%). SIGNIFICANCE: Seizure detection devices that are used in bed which depend on heartrate and movement show similar sensitivity in children and adults. However, children do show general higher false alarm rate, mostly triggered while awake. By correcting for body position, the false alarms can be limited to a level that comes close to that in adults.

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OBJECTIVE: Frequent false alarms from computer-assisted monitoring systems may harm the safety of patients with non-convulsive status epilepticus (NCSE). In this study, we aimed at reducing false alarms in the NCSE detection based on preventing from three common errors: over-interpretation of abnormal background activity, dense short ictal discharges and continuous interictal discharges as ictal discharges. APPROACH: We analyzed 10 participants' hospital-archived 127-hour electroencephalography (EEG) recordings with 310 ictal discharges. To reduce the false alarms caused by abnormal background activity, we used morphological features extracted by visibility graph methods in addition to time-frequency features. To reduce the false alarms caused by over-interpreting short ictal discharges and interictal discharges, we created two synthetic classes-'Suspected Non-ictal' and 'Suspected Ictal'-based on the misclassified categories and constructed a synthetic 4-class dataset combining the standard two classes-'Non-ictal' and 'Ictal'-to train a 4-class classifier. Precision-recall curves were used to compare our proposed 4-class classification model and the standard 2-class classification model with or without the morphological features in the leave-one-out cross validation stage. The sensitivity and precision were primarily used as performance metrics for the detection of a seizure event. MAIN RESULTS: The 4-class classification model improved the performance of the standard 2-class model, in particular increasing the precision by 15% at an 80% sensitivity level when only time-frequency features were used. Using the morphological features, the 4-class classification model achieved the best performances: a sensitivity of 93% ± 12% and a precision of 55% ± 30% in the group level. 100% accuracy was reached in a participant's 4.3-hour recording with 5 ictal discharges. SIGNIFICANCE: False alarms in the NCSE detection were remarkably reduced using the morphological features and the proposed 4-class classification model.

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OBJECTIVE: To determine the incidence of clinically relevant arrhythmias in refractory focal epilepsy and to assess the potential of postictal arrhythmias as risk markers for sudden unexpected death in epilepsy (SUDEP). METHODS: We recruited people with refractory focal epilepsy without signs of ictal asystole and who had at least one focal seizure per month and implanted a loop recorder with 2-year follow-up. The devices automatically record arrhythmias. Subjects and caregivers were instructed to make additional peri-ictal recordings. Clinically relevant arrhythmias were defined as asystole ≥ 6 seconds; atrial fibrillation < 55 beats per minute (bpm), or > 200 bpm and duration > 30 seconds; persistent sinus bradycardia < 40 bpm while awake; and second- or third-degree atrioventricular block and ventricular tachycardia/fibrillation. We performed 12-lead electrocardiography (ECG) and tilt table testing to identify non-seizure-related causes of asystole. RESULTS: We included 49 people and accumulated 1060 months of monitoring. A total of 16 474 seizures were reported, of which 4679 were captured on ECG. No clinically relevant arrhythmias were identified. Three people had a total of 18 short-lasting (<6 seconds) periods of asystole, resulting in an incidence of 2.91 events per 1000 patient-months. None of these coincided with a reported seizure; one was explained by micturition syncope. Other non-clinically relevant arrhythmias included paroxysmal atrial fibrillation (n = 2), supraventricular tachycardia (n = 1), and sinus tachycardia with a right bundle branch block configuration (n = 1). SIGNIFICANCE: We found no clinically relevant arrhythmias in people with refractory focal epilepsy during long-term follow-up. The absence of postictal arrhythmias does not support the use of loop recorders in people at high SUDEP risk.

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To improve the diagnostic accuracy of electroencephalography (EEG) criteria for nonconvulsive status epilepticus (NCSE), external validation of the recently proposed Salzburg criteria is paramount. We performed an external, retrospective, diagnostic accuracy study of the Salzburg criteria, using EEG recordings from patients with and without a clinical suspicion of having NCSE. Of the 191 EEG recordings, 12 (12%) was classified as an NCSE according to the reference standard. In the validation cohort, sensitivity was 67% and specificity was 89%. The positive predictive value was 47% and the negative predictive value was 95%. Ten patients in the control group (n = 93) were false positive, resulting in a specificity of 89.2%. The interrater agreement between the reference standards and between the scorers of the Salzburg criteria was moderate; disagreement occurred mainly in patients with an epileptic encephalopathy. The Salzburg criteria showed a lower diagnostic accuracy in our external validation study than in the original design, suggesting that they cannot replace the current practice of careful weighing of both clinical and EEG information on an individual basis.

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BACKGROUND: At many outpatient departments for psychiatry worldwide, standardized monitoring of the safety of prescribed psychotropic drugs is not routinely performed in daily clinical practice. Therefore it is unclear to which extent the drugs used by psychiatric outpatients are prescribed effectively and safely. These issues warrant structured monitoring of medication use, (pre-existing) co-morbidities, effectiveness and side effects during psychiatric outpatient treatment. Improvement of monitoring practices provides an opportunity to ensure that somatic complications and adverse drug effects are detected and dealt with in a timely manner. Structural support for data collection and follow-up tests seems essential for improvement of monitoring practices in psychiatric outpatients. The implementation of a structured somatic monitoring program as part of routine clinical practice, as we describe in this study protocol, may be a solution. METHODS: In order to address these issues, we developed the innovative program 'Monitoring Outcomes of Psychiatric Pharmacotherapy (MOPHAR)'. MOPHAR is an infrastructure for implementation of standardized routine outcome monitoring (ROM; including standardized monitoring of treatment effect), monitoring of adverse psychotropic medication effects in psychiatric outpatients, encompassing both somatic adverse effects (e.g. metabolic disturbances) and subjective adverse effects (e.g. sedation or sexual side effects) and medication reconciliation. DISCUSSION: In the MOPHAR monitoring program, a nurse performs general and psychotropic drug-specific somatic screenings and provides the treating mental health care providers with more and better information on somatic monitoring for treatment decisions. Given our experience regarding implementation of the MOPHAR program, we expect that the MOPHAR program is feasible and beneficial for patients in any MHS organisation. This paper describes the objectives, target population, setting and the composition and roles of the treatment team. It also indicates what measurements are performed at which time points during outpatient treatment in the MOPHAR monitoring program, as well as the research aspects of this project. TRIAL REGISTRATION: MOPHAR research has been prospectively registered with the Netherlands Trial Register on 19th of November 2014. ( NL4779 ).

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INTRODUCTION: Heart rate decrease during epileptic seizures is rare and should be considered in patients with unusual or refractory episodes of syncope or in patients with a history suggestive of both epilepsy and syncope. We systematically reviewed the literature to better understand the clinical signs and risk factors of ictal heart rate decreases. MATERIAL AND METHODS: We performed a literature-search on "ictal bradycardia" and "ictal asystole" in Pubmed and added papers from the references and personal archives. Articles relating to animal studies, seizures without ictal decrease of heart rate, cases without simultaneous electroencephalography (EEG) and electrocardiography (ECG), convulsive syncopes, or cases with bradycardia before seizure onset and articles written in other languages than English, Dutch, German, French, or Spanish were excluded. Full texts of the remaining articles were screened for cases of ictal bradycardia or ictal asystole. Cases were selected on the basis of a self-designed quality score. The relationship of RR wave interval of at least 5 s, signs of syncope, and EEG signs of ischemia were analyzed with chi-square test and identifying 95% confidence intervals. RESULTS: Ictal bradycardia and ictal asystole predominantly occurred during focal seizures with loss of awareness (proportion in the combined group of bradycardia and asystole (p1 + 2) = 0.85) in people with mainly left lateralized (p1 + 2 = 0.64; p = 0.001) temporal lobe seizures (p1 + 2 = 0.91). Seizures with ictal asystole typically started with a heart rate decrease. During ictal asystole in the majority of cases, not only the clinical signs of syncope occurred (change of proportion (Δp) = 0.67; 95% CI: 0.48-0.86; p < 0.0001), i.e., interrupting the seizure semiology, but also the characteristic EEG signs of ischemia (Δp = 0.50; 95% CI: 0.26-0.74; p < 0.001). We found a statistically significant relation between signs of syncope and EEG signs of ischemia (Δp = -0.37; 95% CI: (-0.64)-(-0.10); p < 0.01) but not between duration of asystole (5 s) and either signs of syncope (Δp = -0.36; 95% CI: (-0.77)-0.05; p = 0.03) or EEG signs of ischemia (Δp = -0.37; 95% CI: (-1.07)-0.33; p = 0.16). CONCLUSION: In the ictal bradycardia syndrome, signs of syncope disrupt the semiology of ongoing seizures and are associated with EEG signs of brain ischemia and the duration of the cardiac arrhythmia.

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Psychotic disorders often have been linked with violence. However, studies have shown that people with a psychotic disorder are more often victim than perpetrator of violence. The objective of this meta-analysis was to review prevalence rates for different types of victimization and to identify risk factors associated with victimization. Based on a search in MEDLINE, PsycINFO, and Web of Science, 27 studies were found with samples consisting of adults with a psychotic disorder and possible victimization occurring during adulthood and data on "violent victimization," "sexual victimization," "non-violent victimization," and/or "victimization not otherwise specified." The median prevalence rate for violent victimization was 20%, for sexual victimization 20%, nonviolent victimization 19%, and for victimization not otherwise specified 19%. Victimization rates were approximately 4-6 times higher than in the general community. Meta-analyses showed the following significant risk factors: delusion (OR = 1.69), hallucinations (OR = 1.70), manic symptoms (OR = 1.66), drugs (OR = 1.90) or alcohol abuse (OR = 2.05), perpetration of a crime (OR = 4.33), unemployment (OR = 1.31), and homelessness (OR = 2.49). Other risk factors like previous victimization, impaired social functioning, personality disorder, and living in a disadvantaged neighborhood were found only in 1 or 2 studies. Based on the results, we conclude that, depending on the examined time period, 1 in 5 (assessment period ≤3 y) or 1 in 3 (assessment period entire adulthood) people with a psychotic disorder was victim of a crime. Clinical, behavioral, and sociodemographic factors were significantly associated with victimization, as well as previous victimization. Prospective research into risk factors is needed to capture causal trajectories of victimization.

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BACKGROUND: People with a psychotic disorder have an increased risk of becoming the victim of a crime. To prevent victimization a body-oriented resilience therapy using kickboxing exercises was developed. This study aims to explore the feasibility of the therapy, to improve the therapy protocol and to explore suitable outcomes for a RCT. METHODS: Twenty-four adults with a psychotic disorder received 20 weekly group sessions in which potential risk factors for victimization and strategies for dealing with them were addressed. Sessions were evaluated weekly. During pre and post assessment participants completed questionnaires on, among other, victimization, aggression regulation and social functioning. RESULTS: The short recruitment period indicates the interest in such an intervention and the willingness of clients to participate. Mean attendance was 85.3 and 88% of the participants completed fifteen or more sessions. The therapy protocol was assessed as adequate and exercises as relevant with some small improvements to be made. The victimization and aggression regulation questionnaires were found to be suitable outcome measurements for a subsequent RCT. CONCLUSION: The results support the feasibility of the BEATVIC therapy. Participants subjectively evaluated the intervention as helpful in their attempt to gain more self-esteem and assertiveness. With some minor changes in the protocol the effects of BEATVIC can be tested in a RCT. TRIAL REGISTRATION: The trial registration number (TRN) is 35949 (date submitted 09/11/2018). Retrospectively registered.

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OBJECTIVE: To develop and prospectively evaluate a method of epileptic seizure detection combining heart rate and movement. METHODS: In this multicenter, in-home, prospective, video-controlled cohort study, nocturnal seizures were detected by heart rate (photoplethysmography) or movement (3-D accelerometry) in persons with epilepsy and intellectual disability. Participants with >1 monthly major seizure wore a bracelet (Nightwatch) on the upper arm at night for 2 to 3 months. Major seizures were tonic-clonic, generalized tonic >30 seconds, hyperkinetic, or others, including clusters (>30 minutes) of short myoclonic/tonic seizures. The video of all events (alarms, nurse diaries) and 10% completely screened nights were reviewed to classify major (needing an alarm), minor (needing no alarm), or no seizure. Reliability was tested by interobserver agreement. We determined device performance, compared it to a bed sensor (Emfit), and evaluated the caregivers' user experience. RESULTS: Twenty-eight of 34 admitted participants (1,826 nights, 809 major seizures) completed the study. Interobserver agreement (major/no major seizures) was 0.77 (95% confidence interval [CI] 0.65-0.89). Median sensitivity per participant amounted to 86% (95% CI 77%-93%); the false-negative alarm rate was 0.03 per night (95% CI 0.01-0.05); and the positive predictive value was 49% (95% CI 33%-64%). The multimodal sensor showed a better sensitivity than the bed sensor (n = 14, median difference 58%, 95% CI 39%-80%, p < 0.001). The caregivers' questionnaire (n = 33) indicated good sensor acceptance and usability according to 28 and 27 participants, respectively. CONCLUSION: Combining heart rate and movement resulted in reliable detection of a broad range of nocturnal seizures.

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People with epilepsy need assistance and are at risk of sudden death when having convulsive seizures (CS). Automated real-time seizure detection systems can help alert caregivers, but wearable sensors are not always tolerated. We determined algorithm settings and investigated detection performance of a video algorithm to detect CS in a residential care setting. The algorithm calculates power in the 2-6 Hz range relative to 0.5-12.5 Hz range in group velocity signals derived from video-sequence optical flow. A detection threshold was found using a training set consisting of video-electroencephalogaphy (EEG) recordings of 72 CS. A test set consisting of 24 full nights of 12 new subjects in residential care and additional recordings of 50 CS selected randomly was used to estimate performance. All data were analyzed retrospectively. The start and end of CS (generalized clonic and tonic-clonic seizures) and other seizures considered desirable to detect (long generalized tonic, hyperkinetic, and other major seizures) were annotated. The detection threshold was set to the value that obtained 97% sensitivity in the training set. Sensitivity, latency, and false detection rate (FDR) per night were calculated in the test set. A seizure was detected when the algorithm output exceeded the threshold continuously for 2 seconds. With the detection threshold determined in the training set, all CS were detected in the test set (100% sensitivity). Latency was ≤10 seconds in 78% of detections. Three/five hyperkinetic and 6/9 other major seizures were detected. Median FDR was 0.78 per night and no false detections occurred in 9/24 nights. Our algorithm could improve safety unobtrusively by automated real-time detection of CS in video registrations, with an acceptable latency and FDR. The algorithm can also detect some other motor seizures requiring assistance.

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This is a critical review and comment on the use of movement detection in epileptic seizures. The detection of rhythmic movement components, such as the clonic part of tonic-clonic seizures, is essential in all seizure detection based on movement sensors. Of the many available movement sensor types, accelerometric sensors are used most often. Eleven video-electroencephalographic (EEG) and 1 field study have been carried out. The results of these clinical trials depend on the population, study design, and seizure evolution. In video-EEG monitoring units, sensitivity for tonic-clonic seizures varied from 31% to 95%, and positive predictive value from 4% to 60%. In a field trial in a residential adult population with intellectual disability, sensitivity was 14% and positive predictive value was 82%, whereas in patients admitted to an epilepsy clinic, a bed sensor had a sensitivity of 84% (no positive predictive value was given). The algorithms using the "rhythmic movement" component at the end of a tonic-clonic seizure are reliable (few false-positive alarms) but miss less typical seizure patterns that are mostly present in people with associated brain development disturbances. Other modalities (heart rate and electromyography) are needed to increase the detection performance. Advanced accelerometric techniques allow us to gain greater insight into seizure evolution patterns, possibilities for neuromodulation, and the influence of antiepileptic drugs on specific seizure components.

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BACKGROUND: Patients with psychotic disorders are at risk of developing mental health and social problems, and physical disorders. To monitor and treat these problems when indicated, an annual routine outcome monitoring program, Pharmacotherapy Monitoring and Outcome Survey (PHAMOUS), was developed. This paper presents the background and content of PHAMOUS, implementation of PHAMOUS, characteristics of the patients screened in 2015, and the outcome of patients with three annual screenings between 2011 and 2015. METHODS: PHAMOUS was implemented in four mental health institutions in the Northern Netherlands in 2006. During the PHAMOUS screening, patients are assessed on socio-demographics, psychiatric symptoms, medication, physical parameters, lifestyle, (psycho)social functioning and quality of life, using internationally validated instruments. RESULTS: In 2015, 1955 patients with psychotic disorders were enrolled in the PHAMOUS screening. The majority (72%) was receiving mental healthcare for ten years or longer. A small group was hospitalized (10%) in the past year. Half of the patients were in symptomatic remission. Less than 10% had a paid job. More than half of the patients fulfilled the criteria for metabolic syndrome (54%). The subsample with three annual screenings from 2011 to 2015 (N = 1230) was stable, except the increasing prevalence of high glucose levels and satisfaction with social relationships (Cochran's Q = 16.33, p = .001 resp. Q = 14.79, p = .001). CONCLUSION: The annual PHAMOUS screening enables to follow the mental, physical and social health problems of patients, which offers a good basis for shared-decision making with regard to updating the annual treatment plan, next to a wealth of data for scientific research.

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BACKGROUND: The traditional EEG features in the time and frequency domain show limited seizure detection performance in the epileptic population with intellectual disability (ID). In addition, the influence of EEG seizure patterns on detection performance was less studied. NEW METHOD: A single-channel EEG signal can be mapped into visibility graphs (VGS), including basic visibility graph (VG), horizontal VG (HVG), and difference VG (DVG). These graphs were used to characterize different EEG seizure patterns. To demonstrate its effectiveness in identifying EEG seizure patterns and detecting generalized seizures, EEG recordings of 615h on one EEG channel from 29 epileptic patients with ID were analyzed. RESULTS: A novel feature set with discriminative power for seizure detection was obtained by using the VGS method. The degree distributions (DDs) of DVG can clearly distinguish EEG of each seizure pattern. The degree entropy and power-law degree power in DVG were proposed here for the first time, and they show significant difference between seizure and non-seizure EEG. The connecting structure measured by HVG can better distinguish seizure EEG from background than those by VG and DVG. COMPARISON WITH EXISTING METHOD: A traditional EEG feature set based on frequency analysis was used here as a benchmark feature set. With a support vector machine (SVM) classifier, the seizure detection performance of the benchmark feature set (sensitivity of 24%, FDt/h of 1.8s) can be improved by combining our proposed VGS features extracted from one EEG channel (sensitivity of 38%, FDt/h of 1.4s). CONCLUSIONS: The proposed VGS-based features can help improve seizure detection for ID patients.

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OBJECTIVE: Diagnostic value and efficacy of re-interpretation of previous EEGs in 100 patients admitted to a tertiary epilepsy center with EEG results conflicting with the clinical diagnosis after the first visit. METHODS: EEGs were reclassified. A matched control group was included to assess the efficiency of the re-interpretation process. Efficacy was assessed by questionnaires and costs as number of technician hours needed. RESULTS: In 85 patients the previous EEG conclusion was known. In 43 the conclusion was altered. In 23 the epileptic activity changed from positive to negative (17) or the reverse (6). In 15 the focus changed (7 originally classified as generalized epileptic activity). In 5 the syndrome changed. 57% of the re-interpretation group needed no extra EEG afterwards. 96% of the re-interpretations were considered useful by requesting and 72% by not involved neurologists. The average time per EEG technologist per patient was 8,81h in controls and 5,40 in the re-interpretation group. CONCLUSIONS: In 43 from the 85 patients (51%) re-interpretation of 'controversial' EEGs led to a different opinion. The re-interpretations were useful and less time consuming, compared to new EEGs in controls. SIGNIFICANCE: Re-interpretation of 'controversial' EEGs is useful and cost effective.

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Negative symptoms, e.g. social withdrawal, reduced initiative, anhedonia and affective flattening, are notoriously difficult to treat. In this review, we take stock of recent research into treatment of negative symptoms by summarizing psychosocial as well as pharmacological and other biological treatment strategies. Major psychosocial approaches concern social skills training, cognitive behavior therapy for psychosis, cognitive remediation and family intervention. Some positive findings have been reported, with the most robust improvements observed for social skills training. Although cognitive behavior therapy shows significant effects for negative symptoms as a secondary outcome measure, there is a lack of data to allow for definite conclusions of its effectiveness for patients with predominant negative symptoms. With regard to pharmacological interventions, antipsychotics have been shown to improve negative symptoms, but this seems to be limited to secondary negative symptoms in acute patients. It has also been suggested that antipsychotics may aggravate negative symptoms. Recent studies have investigated glutamatergic compounds, e.g. glycine receptor inhibitors and drugs that target the NMDA receptor or metabotropic glutamate 2/3 (mGlu2/3) receptor, but no consistent evidence of improvement of negative symptoms was found. Finally, some small studies have suggested improvement of negative symptoms after non-invasive electromagnetic neurostimulation, but this has only been partly replicated and it is still unclear whether these are robust improvements. We address methodological issues, in particular the heterogeneity of negative symptoms and treatment response, and suggest avenues for future research. There is a need for more detailed studies that focus on different dimensions of negative symptoms.

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Objective: Automated seizure detection and alarming could improve quality of life and potentially prevent sudden, unexpected death in patients with severe epilepsy. As currently available systems focus on tonic-clonic seizures, we want to detect a broader range of seizure types, including tonic, hypermotor, and clusters of seizures. Methods: In this multicenter, prospective cohort study, the nonelectroencephalographic (non-EEG) signals heart rate and accelerometry were measured during the night in patients undergoing a diagnostic video-EEG examination. Based on clinical video-EEG data, seizures were classified and categorized as clinically urgent or not. Seizures included for analysis were tonic, tonic-clonic, hypermotor, and clusters of short myoclonic/tonic seizures. Features reflecting physiological changes in heart rate and movement were extracted. Detection algorithms were developed based on stepwise fulfillment of conditions during increases in either feature. A training set was used for development of algorithms, and an independent test set was used for assessing performance. Results: Ninety-five patients were included, but due to sensor failures, data from only 43 (of whom 23 patients had 86 seizures, representing 402 h of data) could be used for analysis. The algorithms yield acceptable sensitivities, especially for clinically urgent seizures (sensitivity = 71-87%), but produce high false alarm rates (2.3-5.7 per night, positive predictive value = 25-43%). There was a large variation in the number of false alarms per patient. Significance: It seems feasible to develop a detector with high sensitivity, but false alarm rates are too high for use in clinical practice. For further optimization, personalization of algorithms may be necessary.

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UNLABELLED: We evaluated the performance of audio-based detection of major seizures (tonic-clonic and long generalized tonic) in adult patients with intellectual disability living in an institute for residential care. METHODS: First, we checked in a random sample (n=17, 102 major seizures) how many patients have recognizable sounds during these seizures. In the second part of this trial, we followed 10 patients (who had major seizures with recognizable sounds) during four weeks with an acoustic monitoring system developed by CLB ('CLB-monitor') and video camera. In week 1, we adapted the sound detection threshold until, per night, a maximum of 20 sounds was found. During weeks 2-4, we selected the epilepsy-related sounds and performed independent video verification and labeling ('snoring', 'laryngeal contraction') of the seizures. The video images were also fully screened for false negatives. In the third part, algorithms in the CLB-monitor detected one specific sound (sleep-related snoring) to illustrate the value of automatic sound recognition. RESULTS: Part 1: recognizable sounds (louder than whispering) occurred in 23 (51%) of the 45 major seizures, 20 seizures (45%) were below this threshold, and 2 (4%) were without any sound. Part 2: in the follow-up group (n=10, 112 major seizures; mean: 11.2, range: 1-30), we found a mean sensitivity of 0.81 (range: 0.33-1.00) and a mean positive predictive value of 0.40 (range: 0.06-1.00). All false positive alarms (mean value: 1.29 per night) were due to minor seizures. We missed 4 seizures (3%) because of lack of sound and 10 (9%) because of sounds below the system threshold. Part 3: the machine-learning algorithms in the CLB-monitor resulted in an overall accuracy for 'snoring' of 98.3%. CONCLUSIONS: Audio detection of major seizures is possible in half of the patients. Lower sound detection thresholds may increase the proportion of suitable candidates. Human selection of seizure-related sounds has a high sensitivity and moderate positive predictive value because of minor seizures which do not need intervention. Algorithms in the CLB-monitor detect seizure-related sounds and may be used alone or in multimodal systems.

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In preparation for a multicenter randomized controlled trial, a pilot study was conducted investigating the feasibility and acceptance of a shortened version (12 vs. 40 sessions) of an individual metacognitive psychotherapy (Metacognitive Reflection and Insight Therapy [MERIT]). Twelve participants with a diagnosis of schizophrenia were offered 12 sessions of MERIT. Effect sizes were calculated for changes from baseline to treatment end for metacognitive capacity measured by the Metacognition Assessment Scale-Abbreviated. Nine of twelve patients finished treatment. However, nonsignificant moderate to large effect sizes were obtained on the primary outcome measure. This study is among the first to suggest that patients with schizophrenia will accept metacognitive therapy and evidence improvements in metacognitive capacity. Despite limitations typical to a pilot study, including a small sample size and lack of a control group, sufficient evidence of efficacy was obtained to warrant further investigation.

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BACKGROUND: Individuals with a psychotic disorder are at an increased risk of becoming victim of a crime or other forms of aggression. Research has revealed several possible risk factors (e.g. impaired social cognition, aggression regulation problems, assertiveness, self-stigma, self-esteem) for victimization in patients with a psychotic disorder. To address these risk factors and prevent victimization, we developed a body-oriented resilience training with elements of kickboxing: BEATVIC. The present study aims to evaluate the effectiveness of the intervention. METHODS/DESIGN: Seven mental health institutions in the Netherlands will participate in this study. Participants will be randomly assigned to either the BEATVIC training or the control condition: social activation. Follow-ups are at 6, 18 and 30 months. Short term effects on risk factors for victimization will be examined, since these are direct targets of the intervention and are thought to be mediators of victimization, the primary outcome of the intervention. The effect on victimization will be investigated at follow-up. In a subgroup of patients, fMRI scans will be made before and after the intervention period in order to assess potential neural changes associated with the effects of the training. DISCUSSION: This study is the first to examine the effectiveness of an intervention targeted at victimization in psychosis. Methodological issues of the study are addressed in the discussion of this paper. TRIAL REGISTRATION: Current Controlled Trials: ISRCTN21423535 . Retrospectively registered 30-03-2016.

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