Quality of life, social support, and pathways of care among persons with psychiatric disorders-A cross-sectional study.

Background: Patients with psychiatric disorders have varied psychosocial realities embedded in the community context. Help-seeking behavior is extremely crucial in determining the prognosis and outcome of interventions. Objectives: (1) To assess the levels of quality of life, perceived social support, and decision to first seek help. (2) To assess the association of socio-demographic variables with these domains. Materials and Methods: A cross-sectional descriptive survey with 100 psychiatric patients in a tertiary care setting employed three scales, namely WHOQOL-BREF, PSSS, and WHO Pathways Encounter form. Results: Patients had higher levels of quality of life and perceived social support. The majority of patients chose health professionals over faith healers at the onset of symptoms, had higher reliance on family members, and continued with hospital visits till the third follow-up. Sex, occupation, and marital status were associated with perceived social support, and duration of illness was associated with help-seeking behavior. Conclusions: Community-based interventions must include holistic services and reduce the gap between onset and first contact, leading to higher quality of life and perceived social support.

Machine learning approach to detect focal-onset seizures in the human anterior nucleus of the thalamus.

Objective.There is an unmet need to develop seizure detection algorithms from brain regions outside the epileptogenic cortex. The study aimed to demonstrate the feasibility of classifying seizures and interictal states from local field potentials (LFPs) recorded from the human thalamus-a subcortical region remote to the epileptogenic cortex. We tested the hypothesis that spectral and entropy-based features extracted from LFPs recorded from the anterior nucleus of the thalamus (ANT) can distinguish its state of ictal recruitment from other interictal states (including awake, sleep).Approach. Two supervised machine learning tools (random forest and the random kitchen sink) were used to evaluate the performance of spectral (discrete wavelet transform-DWT), and time-domain (multiscale entropy-MSE) features in classifying seizures from interictal states in patients undergoing stereo-electroencephalography (EEG) evaluation for epilepsy surgery. Under the supervision of IRB, field potentials were recorded from the ANT in consenting adults with drug-resistant temporal lobe epilepsy. Seizures were confirmed in the ANT using line-length and visual inspection. Wilcoxon rank-sum method was used to test the differences in spectral patterns between seizure and interictal (awake and sleep) states.Main results.79 seizures (10 patients) and 158 segments (approx. 4 h) of interictal stereo-EEG data were analyzed. The mean seizure detection latencies with line length in the ANT varied between seizure types (range 5-34 s). However, the DWT and MSE in the ANT showed significant changes for all seizure types within the first 20 s after seizure onset. The random forest (accuracy 93.9% and false-positive 4.6%) and the random kitchen sink (accuracy 97.3% and false-positive 1.8%) classified seizures and interictal states.Significance.These results suggest that features extracted from the thalamic LFPs can be trained to detect seizures that can be used for monitoring seizure counts and for closed-loop seizure abortive interventions.