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BackgroundBipolar disorder (BD) is linked to circadian rhythm disruptions resulting in aberrant motor activity patterns. We aimed to explore whether motor activity alone, as assessed by longitudinal actigraphy, can be used to classify accurately BD patients and healthy controls (HCs) into their respective groups.MethodsNinety-day actigraphy records from 25 interepisode BD patients (ie, Montgomery–Asberg Depression Rating Scale (MADRS) and Young Mania Rating Scale (YMRS) ResultsUsing all actigraphy features, this method correctly assigned 88% (sensitivity = 85%, specificity = 91%) of BD patients and HCs to their respective group. The classification success may be confounded by differences in employment between BD patients and HCs. When motor activity features resistant to the employment status were used (the strongest feature being time variation of intradaily variability, Cohen’s d = 1.33), 79% of the subjects (sensitivity = 76%, specificity = 81%) were correctly classified.ConclusionA machine-learning actigraphy-based model was capable of distinguishing between interepisode BD patients and HCs solely on the basis of motor activity. The classification remained valid even when features influenced by employment status were omitted. The findings suggest that temporal variability of actigraphic parameters may provide discriminative power for differentiating between BD patients and HCs while being less affected by employment status.

BACKGROUND Seasonal peaks in hospitalizations for mood disorders and schizophrenia are well recognized and often replicated. The within-subject tendency to experience illness episodes in the same season, that is, seasonal course, is much less established, as certain individuals may temporarily meet criteria for seasonal course purely by chance. AIMS In this population, prospective cohort study, we investigated whether between and within-subject seasonal patterns of hospitalizations occurred more frequently than would be expected by chance. METHODS Using a compulsory, standardized national register of hospitalizations, we analyzed all admissions for mood disorders and schizophrenia in the Czech Republic between 1994 and 2013. We used bootstrap tests to compare the observed numbers of (a) participants with seasonal/regular course and (b) hospitalizations in individual months against empirical distributions obtained by simulations. RESULTS Among 87 184 participants, we found uneven distribution of hospitalizations, with hospitalization peaks for depression in April and November (X2 (11) = 363.66, P

BACKGROUND Due to the COVID-19 pandemic, the Czech population experienced a second lockdown lasting for about half a year, restricting free movement and imposing social isolation. However, it is not known whether the impact of this long lockdown resulted in habituation to the adverse situation or in the traumatization of the Czech population, and whether the media and specific media use contributed to these effects. OBJECTIVE The aim of this study was to elucidate the effect of the long lockdown on the mental health of the Czech population, and the role of exposure to COVID-19 news reports and specific forms of media news use in mental health. METHODS We conducted two consecutive surveys in the early (November 2020) and late (March/April 2021) phases of the nationwide lockdown on the same nationally representative group of Czech adults (N=1777) participating in a longitudinal panel study. RESULTS Our findings showed that the self-reported symptoms of anxiety and depression increased in the second observation period, confirming the negative effect of the pandemic lockdown as it unfolded, suggesting that restrictive measures and continuous exposure to a collective stressor did not result in the strengthening of resilience but rather in ongoing traumatization. The results also suggest a negative role of the media’s coverage of the COVID-19 pandemic in mental health during the early, and particularly late, phases of the lockdown. Furthermore, we found several risk and protective factors of specific media news use. The media practice in news consumption connected to social media use was the strongest predictor of exacerbated mental health symptoms, particularly in the late phase of the lockdown. Moreover, news media use characterized by internalization of information learned from the news, as well as negative attitudes toward media news, were associated with higher levels of anxiety and depression. Conversely, the use of infotainment, together with an in-depth and contextual style of reading news articles, were related to improvement of mental health. CONCLUSIONS Our study showed that the long lockdown resulted in traumatization rather than habituation, and in more pronounced effects (both negative and positive) of media use in mental health.

Background Due to the COVID-19 pandemic, the Czech population experienced a second lockdown lasting for about half a year, restricting free movement and imposing social isolation. However, it is not known whether the impact of this long lockdown resulted in habituation to the adverse situation or in the traumatization of the Czech population, and whether the media and specific media use contributed to these effects. Objective The aim of this study was to elucidate the effect of the long lockdown on the mental health of the Czech population, and the role of exposure to COVID-19 news reports and specific forms of media news use in mental health. Methods We conducted two consecutive surveys in the early (November 2020) and late (March/April 2021) phases of the nationwide lockdown on the same nationally representative group of Czech adults (N=1777) participating in a longitudinal panel study. Results Our findings showed that the self-reported symptoms of anxiety and depression increased in the second observation period, confirming the negative effect of the pandemic lockdown as it unfolded, suggesting that restrictive measures and continuous exposure to a collective stressor did not result in the strengthening of resilience but rather in ongoing traumatization. The results also suggest a negative role of the media’s coverage of the COVID-19 pandemic in mental health during the early, and particularly late, phases of the lockdown. Furthermore, we found several risk and protective factors of specific media news use. The media practice in news consumption connected to social media use was the strongest predictor of exacerbated mental health symptoms, particularly in the late phase of the lockdown. Moreover, news media use characterized by internalization of information learned from the news, as well as negative attitudes toward media news, were associated with higher levels of anxiety and depression. Conversely, the use of infotainment, together with an in-depth and contextual style of reading news articles, were related to improvement of mental health. Conclusions Our study showed that the long lockdown resulted in traumatization rather than habituation, and in more pronounced effects (both negative and positive) of media use in mental health.

In this work, we investigated the accuracy of chronotype estimation from actigraphy while evaluating the required recording length and stability over time. Chronotypes have an important role in chronobiological and sleep research. In outpatient studies, chronotypes are typically evaluated by questionnaires. Alternatively, actigraphy provides potential means for measuring chronotype characteristics objectively, which opens many applications in chronobiology research. However, studies providing objective, critical evaluation of agreement between questionnaire-based and actigraphy-based chronotypes are lacking. We recorded 3-months of actigraphy and collected Morningness-Eveningness Questionnaire (MEQ), and Munich Chronotype Questionnaire (MCTQ) results from 122 women. Regression models were applied to evaluate the questionnaire-based chronotypes scores using selected actigraphy features. Changes in predictive strength were evaluated based on actigraphy recordings of different duration. The actigraphy was significantly associated with the questionnaire-based chronotype, and the best single-feature-based models explained 37% of the variability (R2) for MEQ (p < .001), 47% for mid-sleep time MCTQ-MSFsc (p < .001), and 19% for social jetlag MCTQ-SJLrel (p < .001). Concerning stability in time, the Mid-sleep and Acrophase features showed high levels of stability (test-retest R ~ 0.8), and actigraphy-based MSFscacti and SJLrelacti showed high temporal variability (test-retest R ~ 0.45). Concerning required recording length, features estimated from recordings with 3-week and longer observation periods had sufficient predictive power on unseen data. Additionally, our data showed that the subjectively reported extremes of the MEQ, MCTQ-MSFsc, and MCTQ-SJLrel are commonly overestimated compared to objective activity peak and middle of sleep differences measured by actigraphy. Such difference may be associated with chronotype time-variation. As actigraphy is considered accurate in sleep-wake cycle detection, we conclude that actigraphy-based chronotyping is appropriate for large-scale studies, especially where higher temporal variability in chronotype is expected.

Deep brain stimulation (DBS) is an internationally accepted form of treatment option for selected patients with Parkinson’s disease and dystonia. Intraoperative extracellular microelectrode recordings (MER) are considered as the standard electrophysiological method for the precise positioning of the DBS electrode into the target brain structure. Pre-processing of MERs is a key phase in clinical analysis, with intraoperative microelectrode recordings being prone to several artifact groups (up to 25 %). The aim of this methodological article is to provide a convolutional neural network (CNN) processing pipeline for the detection of artifacts in an MER. We applied continuous wavelet transform (CWT) to generate an over-complete time–frequency representation. We demonstrated that when attempting to find artifacts in an MER, the new CNN + CWT provides a high level of accuracy (ACC = 88.1 %), identifies individual classes of artifacts (ACC = 75.3 %) and also offers artifact time onset detail, which can lead to a reduction in false positives/negatives. In summary, the presented methodology is capable of identifying and removing various artifacts in a comprehensive database of MER and represents a substantial improvement over the existing methodology. We believe that this approach will assist in the proposal of interesting clinical hypotheses and will have neurologically relevant effects.

Background Self-reported mood is a valuable clinical data source regarding disease state and course in patients with mood disorders. However, validated, quick, and scalable digital self-report measures that can also detect relapse are still not available for clinical care. Objective In this study, we aim to validate the newly developed ASERT (Aktibipo Self-rating) questionnaire—a 10-item, mobile app–based, self-report mood questionnaire consisting of 4 depression, 4 mania, and 2 nonspecific symptom items, each with 5 possible answers. The validation data set is a subset of the ongoing observational longitudinal AKTIBIPO400 study for the long-term monitoring of mood and activity (via actigraphy) in patients with bipolar disorder (BD). Patients with confirmed BD are included and monitored with weekly ASERT questionnaires and monthly clinical scales (Montgomery-Åsberg Depression Rating Scale [MADRS] and Young Mania Rating Scale [YMRS]). Methods The content validity of the ASERT questionnaire was assessed using principal component analysis, and the Cronbach α was used to assess the internal consistency of each factor. The convergent validity of the depressive or manic items of the ASERT questionnaire with the MADRS and YMRS, respectively, was assessed using a linear mixed-effects model and linear correlation analyses. In addition, we investigated the capability of the ASERT questionnaire to distinguish relapse (YMRS≥15 and MADRS≥15) from a nonrelapse (interepisode) state (YMRS Results A total of 99 patients with BD were included in this study (follow-up: mean 754 days, SD 266) and completed an average of 78.1% (SD 18.3%) of the requested ASERT assessments (completion time for the 10 ASERT questions: median 24.0 seconds) across all patients in this study. The ASERT depression items were highly associated with MADRS total scores (P Conclusions The ASERT questionnaire is a quick and acceptable mood monitoring tool that is administered via a smartphone app. The questionnaire has a good capability to detect the worsening of clinical symptoms in a long-term monitoring scenario.

BACKGROUND Self-reported mood is a valuable clinical data source regarding disease state and course in patients with mood disorders. However, validated, quick, and scalable digital self-report measures that can also detect relapse are still not available for clinical care. OBJECTIVE In this study, we aim to validate the newly developed ASERT (Aktibipo Self-rating) questionnaire—a 10-item, mobile app–based, self-report mood questionnaire consisting of 4 depression, 4 mania, and 2 nonspecific symptom items, each with 5 possible answers. The validation data set is a subset of the ongoing observational longitudinal AKTIBIPO400 study for the long-term monitoring of mood and activity (via actigraphy) in patients with bipolar disorder (BD). Patients with confirmed BD are included and monitored with weekly ASERT questionnaires and monthly clinical scales (Montgomery-Åsberg Depression Rating Scale [MADRS] and Young Mania Rating Scale [YMRS]). METHODS The content validity of the ASERT questionnaire was assessed using principal component analysis, and the Cronbach α was used to assess the internal consistency of each factor. The convergent validity of the depressive or manic items of the ASERT questionnaire with the MADRS and YMRS, respectively, was assessed using a linear mixed-effects model and linear correlation analyses. In addition, we investigated the capability of the ASERT questionnaire to distinguish relapse (YMRS≥15 and MADRS≥15) from a nonrelapse (interepisode) state (YMRS RESULTS A total of 99 patients with BD were included in this study (follow-up: mean 754 days, SD 266) and completed an average of 78.1% (SD 18.3%) of the requested ASERT assessments (completion time for the 10 ASERT questions: median 24.0 seconds) across all patients in this study. The ASERT depression items were highly associated with MADRS total scores (PP CONCLUSIONS The ASERT questionnaire is a quick and acceptable mood monitoring tool that is administered via a smartphone app. The questionnaire has a good capability to detect the worsening of clinical symptoms in a long-term monitoring scenario.

Identification of subcortical structures is an essential step in surgical planning for interventions such as the deep brain stimulation (DBS), in which permanent electrode is implanted in a precisely defined location. For refinement of the target localisation and compensation of brain shift occurring during the surgery, intra-operative electrophysiological recording using microelectrodes is usually undertaken.

Introduction The study investigates the association between circadian phenotype (CP), its stability (interdaily stability - IS) and physical activity (PA) in a weight loss (WL) programme. Methods Seventy-five women in WL conservative treatment (BMI ≥ 25 kg/m2) were measured (for about 3 months in between 2016 and 2018) by actigraphy. Results We observed a difference in time of acrophase (p = 0.049), but no difference in IS (p = 0.533) between women who lost and did not lose weight. There was a difference in PA (mesor) between groups of women who lost weight compared to those who gained weight (p = 0.007). There was a relationship between IS and PA parametres mesor: p0.001; and the most active 10 h of a day (M10): p Discussion According to the results of the study, screening of the CP and its stability may be beneficial in the creation of an individualized WL plan.

Bipolar Disorder (BD) is an illness with high prevalence and a huge social and economic impact. It is recurrent, with a long-term evolution in most cases. Early treatment and continuous monitoring have proven to be very effective in mitigating the causes and consequences of BD. However, no tools are currently available for a massive and semi-automatic BD patient monitoring and control. Taking advantage of recent technological developments in the field of wearables, this paper studies the feasibility of a BD episodes classification analysis while using entropy measures, an approach successfully applied in a myriad of other physiological frameworks. This is a very difficult task, since actigraphy records are highly non-stationary and corrupted with artifacts (no activity). The method devised uses a preprocessing stage to extract epochs of activity, and then applies a quantification measure, Slope Entropy, recently proposed, which outperforms the most common entropy measures used in biomedical time series. The results confirm the feasibility of the approach proposed, since the three states that are involved in BD, depression, mania, and remission, can be significantly distinguished.

The deep brain stimulation (DBS) is a symptomatic treatment technique used mainly for movement disorders, consisting of chronic electrical stimulation of subcortical structures. To achieve very precise electrode implantation, which is necessary for a good clinical outcome, many surgical teams use electrophysiological recording around the assumed target, planned in pre-operative MRI images. In our previous work, we developed a probabilistic model to fit a 3D anatomical atlas of the subthalamic nucleus to the recorded microelectrode activity in Parkinson’s disease (PD) patients. In this paper, we extend the model to incorporate characteristic landmarks of the target nucleus, manually annotated in pre-operative MRI data. We validate the approach on a set of 27 exploration five-electrode trajectories from 15 PD patients. The results show that such combined approach may lead to a vast improvement in optimization reliability, while maintaining good fit to the electrophysiology data. The combination of electrophysiology and MRI-based data thus provides a promising approach for compensating brain shift, occuring during the surgery and achieving accurate localization of recording sites in DBS surgery.

Parkinson is a neurodegenerative disease, in which tremor is the main symptom. This paper investigates the use of different classification methods to identify tremors experienced by Parkinsonian patients. Some previous research has focussed tremor analysis on external body signals (e.g., electromyography, accelerometer signals, etc.). Our advantage is that we have access to sub-cortical data, which facilitates the applicability of the obtained results into real medical devices since we are dealing with brain signals directly. Local field potentials (LFP) were recorded in the subthalamic nucleus of 7 Parkinsonian patients through the implanted electrodes of a deep brain stimulation (DBS) device prior to its internalization. Measured LFP signals were preprocessed by means of splinting, down sampling, filtering, normalization and rectification. Then, feature extraction was conducted through a multi-level decomposition via a wavelet transform. Finally, artificial intelligence techniques were applied to feature selection, clustering of tremor types, and tremor detection. The key contribution of this paper is to present initial results which indicate, to a high degree of certainty, that there appear to be two distinct subgroups of patients within the group-1 of patients according to the Consensus Statement of the Movement Disorder Society on Tremor. Such results may well lead to different resultant treatments for the patients involved, depending on how their tremor has been classified. Moreover, we propose a new approach for demand driven stimulation, in which tremor detection is also based on the subtype of tremor the patient has. Applying this knowledge to the tremor detection problem, it can be concluded that the results improve when patient clustering is applied prior to detection.

Background Extracellular microelectrode recording (MER) is a prominent technique for studies of extracellular single-unit neuronal activity. In order to achieve robust results in more complex analysis pipelines, it is necessary to have high quality input data with a low amount of artifacts. We show that noise (mainly electromagnetic interference and motion artifacts) may affect more than 25% of the recording length in a clinical MER database. New method We present several methods for automatic detection of noise in MER signals, based on (i) unsupervised detection of stationary segments, (ii) large peaks in the power spectral density, and (iii) a classifier based on multiple time- and frequency-domain features. We evaluate the proposed methods on a manually annotated database of 5735 ten-second MER signals from 58 Parkinson's disease patients. Comparison with existing methods The existing methods for artifact detection in single-channel MER that have been rigorously tested, are based on unsupervised change-point detection. We show on an extensive real MER database that the presented techniques are better suited for the task of artifact identification and achieve much better results. Results The best-performing classifiers (bagging and decision tree) achieved artifact classification accuracy of up to 89% on an unseen test set and outperformed the unsupervised techniques by 5–10%. This was close to the level of agreement among raters using manual annotation (93.5%). Conclusion We conclude that the proposed methods are suitable for automatic MER denoising and may help in the efficient elimination of undesirable signal artifacts.

BackgroundEarly diagnosis of schizophrenia could improve the outcomes and limit the negative effects of untreated illness. Although participants with schizophrenia show aberrant functional connectivity in brain networks, these between-group differences have a limited diagnostic utility. Novel methods of magnetic resonance imaging (MRI) analyses, such as machine learning (ML), may help bring neuroimaging from the bench to the bedside. Here, we used ML to differentiate participants with a first episode of schizophrenia-spectrum disorder (FES) from healthy controls based on resting-state functional connectivity (rsFC).MethodWe acquired resting-state functional MRI data from 63 patients with FES who were individually matched by age and sex to 63 healthy controls. We applied linear kernel support vector machines (SVM) to rsFC within the default mode network, the salience network and the central executive network.ResultsThe SVM applied to the rsFC within the salience network distinguished the FES from the control participants with an accuracy of 73.0% (p = 0.001), specificity of 71.4% and sensitivity of 74.6%. The classification accuracy was not significantly affected by medication dose, or by the presence of psychotic symptoms. The functional connectivity within the default mode or the central executive networks did not yield classification accuracies above chance level.ConclusionsSeed-based functional connectivity maps can be utilized for diagnostic classification, even early in the course of schizophrenia. The classification was probably based on trait rather than state markers, as symptoms or medications were not significantly associated with classification accuracy. Our results support the role of the anterior insula/salience network in the pathophysiology of FES.

Detailed study of the period before schizophrenic relapse when early warning signs (EWS) are present is crucial to effective pre-emptive strategies. To investigate the temporal properties of EWS self-reported weekly via a telemedicine system. EWS history was obtained for 61 relapses resulting in hospitalization involving 51 patients with schizophrenia. Up to 20 weeks of EWS history per case were evaluated using a non-parametric bootstrap test and generalized mixed-effects model to test the significance and homogeneity of the findings. A statistically significant increase in EWS sum score was detectable 5 weeks before hospitalization. However, analysis of EWS dynamics revealed a gradual, monotonic increase in EWS score across during the 8 weeks before a relapse. The findings-in contrast to earlier studies-suggest that relapse is preceded by a lengthy period during which pathophysiological processes unfold; these changes are reflected in subjective EWS.

We present a probabilistic model for classification of micro-EEG signals, recorded during deep brain stimulation surgery for Parkinson’s disease. The model uses parametric representation of neuronal background activity, estimated using normalized root-mean-square of the signal. Contrary to existing solutions using Bayes classifiers or Hidden Markov Models, our model uses smooth state-transitions represented by sigmoid functions, which ensures flexible model structure in combination with general optimizers for parameter estimation and model fitting. The presented model can easily be extended with additional parameters and constraints and is intended for fitting of a 3D anatomical model to micro-EEG data in further perspective. In an evaluation on 260 trajectories from 61 patients, the model showed classification accuracy 90.0 %, which was comparable to existing solutions. The evaluation proved the model successful in target identification and we conclude that its use for more complex tasks in the area of DBS planning and modeling is feasible.

This paper explores the development of multi-feature classification techniques used to identify tremor-related characteristics in the Parkinsonian patient. Local field potentials were recorded from the subthalamic nucleus and the globus pallidus internus of eight Parkinsonian patients through the implanted electrodes of a Deep brain stimulation (DBS) device prior to device internalization. A range of signal processing techniques were evaluated with respect to their tremor detection capability and used as inputs in a multi-feature neural network classifier to identify the activity of Parkinsonian tremor. The results of this study show that a trained multi-feature neural network is able, under certain conditions, to achieve excellent detection accuracy on patients unseen during training. Overall the tremor detection accuracy was mixed, although an accuracy of over 86% was achieved in four out of the eight patients.

Early diagnosis of schizophrenia might reduce the negative impact of the untreated disease. Progressive functional/structural changes were repeatedly detected using classical between-group statistics. However, these findings have been due to their low sensitivity and specificity not clinically useful. Machine learning methods are able to learn from the data and make predictions on the individual level, which might have a diagnostic potential. We performed a classification of patients with the first episode of schizophrenia (FES) and healthy controls (HC) from the resting state functional connectivity (rsFC) and fractional anisotropy (FA) using machine learning on 1:1 age and sex matched samples of 63/63 patients/HC (rsFC) and 77/77 (DTI). Support vector machine distinguished between patients and controls with an accuracy 73.0% (p = 0.001) (rsFC) and 62.34% (p = 0.005) (DTI). These results were not influenced by symptoms or medication. Our work shows that rsFC and FA might be used to classify patients with FES and HC on the individual level. The classification reflects ‘’trait” markers of FES, as the symptoms and medication had no significant effect. Additionally the results of the analysis of rsFC show the significance of anterior insula in the pathophysiology of FES.

Background The auditory verbal hallucinations (AVH) prevalence among patients diagnosed with schizophrenia is more than 70% (Hugdahl et al, 2008). The brain mechanisms of AVH are not yet clarified. In the currect study, we explore the specific brain connections that are usually associated with perception and reception of the speech, being present in patients with AVHs. Methods In total 70 patients with first psychotic episode, 41 with auditory hallucinations and 29 patients who had never experienced hallucinations were included into the study based on the following criteria: non-hallucinated patients were defined using PANSS scores (Hallucinations P3 scored as 1)/hallucinated patients (PANSS P3 score 3). All patients underwent resting MRI scans. We analysed the resting state functional connectivity between temporal cortices including TPJ structures (supramarginal gyrus and angular gyrus, parahypocampal gyrus) and predefined thalamic nuclei that are parts of the auditory pathways, specifically: the medial geniculate nuclei(MGB), the mediodorsal nuclei (MDN), having a big spectrum of connections with the limbic system and the reticular thalamic nuclei (RTN) which is a main source of inhibitory signals (using detailed atlas of thalamus, Morel, 2003). Results The analysis revealed the increased connectivity between mediodorsal nucleus L and Hesch gyrus (L, R), pahahippocampal gyrus (L,R) as well as between mediodorsal nucleus (R) and parahipocampal gyrus (L). The decreased connectivity was detected between medial geniculate body (L), Medial geniculate body (R) and inferior parietal lobule (L) as well as between medial geniculate body (L) x parahyppocampal gyrus (L) (FDR corr p=0.05). Discussion We confirmed our hypothesis on the altered connectivity between thalamic nuclei and auditory cortex (Heshl gyrus, parahippocampal gyrus) and inferior parietal cortex in patients with AVHs. The findings go in line with other studies on functional dysconnectivity with right hippocampal formation and mediodorsal thalamus compared to patients without lifetime AHs (Diederen et al, 2010; Shinn et al, 2013). The patterns of decreased connections presumably indicate a sensory defect: decreased connectivity between medial geniculate nuclei (main auditory pathway) and parahippocampal gyrus (memory retrieval) and inferior parietal cortex (interpretation of sensory information, Radua et al, 2010). The patterns of increased connections presumably imply alterations of emotional processing (projections from mediodorsal nuclei to auditory cortex) and deserve further investigation.

Appropriate detection of clean signal segments in extracellular microelectrode recordings (MER) is vital for maintaining high signal-to-noise ratio in MER studies. Existing alternatives to manual signal inspection are based on unsupervised change-point detection. We present a method of supervised MER artifact classification, based on power spectral density (PSD) and evaluate its performance on a database of 95 labelled MER signals. The proposed method yielded test-set accuracy of 90%, which was close to the accuracy of annotation (94%). The unsupervised methods achieved accuracy of about 77% on both training and testing data.

This paper focuses on wrapper-based feature selection for a 1-nearest neighbor classifier. We consider in particular the case of a small sample size with a few hundred instances, which is common in biomedical applications. We propose a technique for calculating the complete bootstrap for a 1-nearest-neighbor classifier (i.e., averaging over all desired test/train partitions of the data). The complete bootstrap and the complete cross-validation error estimate with lower variance are applied as novel selection criteria and are compared with the standard bootstrap and cross-validation in combination with three optimization techniques - sequential forward selection (SFS), binary particle swarm optimization (BPSO) and simplified social impact theory based optimization (SSITO). The experimental comparison based on ten datasets draws the following conclusions: for all three search methods examined here, the complete criteria are a significantly better choice than standard 2-fold cross-validation, 10-fold cross-validation and bootstrap with 50 trials irrespective of the selected output number of iterations. All the complete criterion-based 1NN wrappers with SFS search performed better than the widely-used FILTER and SIMBA methods. We also demonstrate the benefits and properties of our approaches on an important and novel real-world application of automatic detection of the subthalamic nucleus.

Introduction The ITAREPS system (Information Technology Aided Relapse Prevention Program in Schizophrenia) developed in Czech Prague Psychiatric Center, presents a mobile phone-based e-Health solution for weekly remote patient monitoring and disease management in schizophrenia and psychotic disorders in general. Objectives The program provides health professionals with home telemonitoring via a PC-to-phone SMS platform that identifies prodromal symptoms of relapse, to enable early intervention and prevent hospitalizations. Aim This work presents data on ITAREPS effectiveness obtained from a recent mirror-design follow-up evaluation carried out in Czech Republic and a randomized controlled trial performed in Japan. Methods A retrospective within-subject mirror-image study was performed to compare inpatient days and inpatient costs for 134 adults with psychotic illness (ICD-10 dg. F 2X.X) before and after at least one-year ITAREPS participation. Results The total number of hospitalizations and (inpatient days) decreased significantly from 159 and (6980) in pre-enrolment period to 47 and (3138) post-enrolment respectively (Wilcoxon-signed ranks test, Monte Carlo, exact test, two-tailed, Z = 6.13 /hospitalizations/, p Outpatients with schizophrenia were randomized to the ITAREPS (n = 22) or control group (n = 23) and were observed during one- year follow-up (1). Results Time to rehospitalization was statistically significantly longer in the ITAREPS group than in the control group (Kaplan–Meier survival analysis, 13 p = 0.033 /Fig.3), with a hazard ratio of 0.21 (95% CI 0.04–0.99). The total number of rehospitalization days (Fig. 4) was significantly lower in the ITAREPS group (37 days) compared with the control group (710 days, p = 0.023). Conclusions The ITAREPS presents a novel approach towards relapse prevention. The robust effectiveness of ITAREPS program together with its low start-up and recurrent costs makes it an attractive consideration for long-term treatment in outpatients with schizophrenia and psychotic disorders in general. Its web-based platform allows for implementation in clinical practice across different health care systems. This work was supported by grant IGA NT 14387 from Ministry of Health, Czech Republic .