Methods for Detecting Abnormal Ventilation in Children - the Case Study of 13-Years old Pitt-Hopkins Girl.

We present contactless technology measuring abnormal ventilation and compare it with polysomnography (PSG). A 13-years old girl with Pitt-Hopkins syndrome presented hyperpnoea periods with apneic spells. The PSG was conducted simultaneously with Emfit movement sensor (Emfit, Finland) and video camera with depth sensor (NEL, Finland). The respiratory efforts from PSG, Emfit sensor, and NEL were compared. In addition, we measured daytime breathing with tracheal microphone (PneaVox,France). The aim was to deepen the knowledge of daytime hyperpnoea periods and ensure that no upper airway obstruction was present during sleep. The signs of upper airway obstruction were not detected despite of minor sleep time. Monitoring respiratory effort with PSG is demanding in all patient groups. The used unobtrusive methods were capable to reveal breathing frequency and hyperpnoea periods. Every day diagnostics need technology like this for monitoring vital signs at hospital wards and at home from subjects with disabilities and co-operation difficulties.

Does Recent Hospitalization Increase Antipsychotic Initiation Among Community Dwellers With Alzheimer's Disease?

OBJECTIVES: Antipsychotics are often prescribed to persons with cognitive impairment in the hospital, but it is not known whether recent hospital care increases the risk of antipsychotic initiation in community dwellers with Alzheimer's disease (AD). We studied whether hospital care during the previous 2 weeks is associated with antipsychotic initiation in persons with AD. DESIGN: Register-based study. PARTICIPANTS AND SETTING: The nationwide Medication use and Alzheimer's disease (MEDALZ) cohort containing Finnish community dwellers with AD between 2005 and 2011 (N = 70,718) was used. METHODS: Incident antipsychotic use was identified with a 1-year washout period. Each new initiator was matched with noninitiator according to age, sex, and time since AD diagnosis (n = 22,281 matched pairs). The use of antipsychotics was identified from the Prescription Register. Information on hospital discharge within the past 2 weeks of antipsychotic initiation was extracted from the Hospital Discharge Register. RESULTS: Antipsychotic initiators were 5 times more likely to have recently been discharged from the hospital compared with the matched noninitiators (29.8% and 5.3%, respectively). In adjusted regression analyses, a hospital stay longer than a week and especially more than 2 months [odds ratio (OR) 4.40, 95% confidence interval (CI) 3.51-5.53], use of benzodiazepines and related drugs (OR 1.66, 95% CI 1.44-1.92), and memantine (OR 1.30, 95% CI 1.12-1.52) were associated with antipsychotic initiation. Older age (OR 0.77, 95% CI 0.62-0.95), asthma or chronic obstructive pulmonary disease (OR 0.73, 95% CI 0.60-0.89), diabetes (OR 0.82, 95% CI 0.69-0.97), and cardiovascular disease (OR 0.82, 95% CI 0.72-0.94) were associated with a lower risk of initiation. CONCLUSIONS AND IMPLICATIONS: Recent hospital care seems to be a risk factor for antipsychotic initiation in community-dwelling persons with AD. The need of antipsychotic treatment must be carefully assessed at the time of discharge. Well planned hospital discharge and home care might reduce antipsychotic initiation.

An integrative method to quantitatively detect nocturnal motor seizures.

In this proof-of-concept investigation, we demonstrate a marker-free video-based method to detect nocturnal motor seizures across a spectrum of motor seizure types, in a nighttime setting with a single adult female with refractory epilepsy. In doing so, we further explore the intermediate biosignals, visually mapping seizure "fingerprints" to seizure types. The method is designed to be flexible enough to generalize to unseen data, and shows promising performance characteristics for low-cost seizure detection and classification. The dataset contained recordings from 27 recorded nights. Seizure events were observed in 22 of these nights, with 36 unequivocally confirmed seizures. Each seizure was classified by an expert epileptologist according to both the ILAE 2017 standard and the Lüders semiological classification guidelines, yielding 5 of the ILAE-recognized seizure types and 7 distinct seizure semiologies. Evaluation was based on inference of motion, oscillation, and sound signals extracted from the recordings. The model architecture consisted of two feature extraction and event determination layers and one thresholding layer, establishing a simple framework for multimodal seizure analysis. Training of the optimal parameters was done by randomly resampling the event hits for each signal, and choosing a threshold that kept an expected 90 % sensitivity for the sample distribution. With the cut-off values selected, statistical performance was calculated for two target seizure groups: those containing a clonic component, and those containing a tonic component. When tuned to 90 % sensitivity, the system achieved a very low false discovery rate of 0.038/hour when targeting seizures with a clonic component, and a clinically-relevant rate of 1.02/hour when targeting seizures with a tonic component. These results indicate a sensitive method for detecting various nocturnal motor seizure types, and a high potential to differentiate motor seizures based on their video and audio signal characteristics. Paired with the low cost of this technique, both cost savings and improved quality of care might be achieved through further development and commercialization of this method.

Automatic assessment of the myoclonus severity from videos recorded according to standardized Unified Myoclonus Rating Scale protocol and using human pose and body movement analysis.

PURPOSE: Myoclonus in progressive myoclonus epilepsy type 1 (EPM1) patients shows marked variability, which presents a substantial challenge in devising treatment and conducting clinical trials. Consequently, fast and objective myoclonus quantification methods are needed. METHODS: Ten video-recorded unified myoclonus rating scale (UMRS) myoclonus with action tests were performed on EPM1 patients who were selected for the development and testing of the automatic myoclonus quantification method. Human pose and body movement analyses of the videos were used to identify body keypoints and further analyze movement smoothness and speed. The automatic myoclonus rating scale (ARMS) was developed. It included the jerk count during movement score and the log dimensionless jerk (LDLJ) score to evaluate changes in the smoothness of movement. RESULTS: The scores obtained with the automatic analyses showed moderate to strong significant correlation with the UMRS myoclonus with action scores. The jerk count of the primary keypoints and the LDLJ scores were effective in the evaluation of the myoclonic jerks during hand movements. They also correlated moderately to strongly with the total UMRS test panel scores (r2 = 0,77, P = 0,009 for the jerk count score and r2 = 0,88, P = 0,001 for the LDLJ score). The automatic analyses was weaker in quantification of the neck, trunk, and leg myoclonus. CONCLUSION: Automatic quantification of myoclonic jerks using human pose and body movement analysis of patients' videos is feasible and was found to be quite consistent with the accepted clinical gold standard quantification method. Based on the results of this study, the automatic analytical method should be further developed and validated to improve myoclonus severity follow-up for EPM1 patients.

Automated pose estimation captures key aspects of General Movements at eight to 17 weeks from conventional videos.

AIM: General movement assessment requires substantial expertise for accurate visual interpretation. Our aim was to evaluate an automated pose estimation method, using conventional video records, to see if it could capture infant movements using objective biomarkers. METHODS: We selected archived videos from 21 infants aged eight to 17 weeks who had taken part in studies at the IRCCS Fondazione Stella Maris (Italy), from 2011 to 2017. Of these, 14 presented with typical low-risk movements, while seven presented with atypical movements and were later diagnosed with cerebral palsy. Skeleton videos were produced using a computational pose estimation model adapted for infants and these were blindly assessed to see whether they contained the information needed for classification by human experts. Movements of skeletal key points were analysed using kinematic metrics to provide a biomarker to distinguish between groups. RESULTS: The visual assessments of the skeleton videos were very accurate, with Cohen's K of 0.90 when compared with the classification of conventional videos. Quantitative analysis showed that arm movements were more variable in infants with typical movements. CONCLUSION: It was possible to extract automated estimation of movement patterns from conventional video records and convert them to skeleton footage. This could allow quantitative analysis of existing footage.