Browsing by Author "Lamichhane, Bishal"

Now showing 1 - 6 of 6
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    Automated detection of activity onset after postictal generalized EEG suppression
    (BioMed Central, 2020) Lamichhane, Bishal; Kim, Yejin; Segarra, Santiago; Zhang, Guoqiang; Lhatoo, Samden; Hampson, Jaison; Jiang, Xiaoqian
    Background: Sudden unexpected death in epilepsy (SUDEP) is a leading cause of premature death in patients with epilepsy. If timely assessment of SUDEP risk can be made, early interventions for optimized treatments might be provided. One of the biomarkers being investigated for SUDEP risk assessment is postictal generalized EEG suppression [postictal generalized EEG suppression (PGES)]. For example, prolonged PGES has been found to be associated with a higher risk for SUDEP. Accurate characterization of PGES requires correct identification of the end of PGES, which is often complicated due to signal noise and artifacts, and has been reported to be a difficult task even for trained clinical professionals. In this work we present a method for automatic detection of the end of PGES using multi-channel EEG recordings, thus enabling the downstream task of SUDEP risk assessment by PGES characterization. Methods: We address the detection of the end of PGES as a classification problem. Given a short EEG snippet, a trained model classifies whether it consists of the end of PGES or not. Scalp EEG recordings from a total of 134 patients with epilepsy are used for training a random forest based classification model. Various time-series based features are used to characterize the EEG signal for the classification task. The features that we have used are computationally inexpensive, making it suitable for real-time implementations and low-power solutions. The reference labels for classification are based on annotations by trained clinicians identifying the end of PGES in an EEG recording. Results: We evaluated our classification model on an independent test dataset from 34 epileptic patients and obtained an AUreceiver operating characteristic (ROC) (area under the curve) of 0.84. We found that inclusion of multiple EEG channels is important for better classification results, possibly owing to the generalized nature of PGES. Of among the channels included in our analysis, the central EEG channels were found to provide the best discriminative representation for the detection of the end of PGES. Conclusion: Accurate detection of the end of PGES is important for PGES characterization and SUDEP risk assessment. In this work, we showed that it is feasible to automatically detect the end of PGES—otherwise difficult to detect due to EEG noise and artifacts—using time-series features derived from multi-channel EEG recordings. In future work, we will explore deep learning based models for improved detection and investigate the downstream task of PGES characterization for SUDEP risk assessment.
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    Mobile sensing-based depression severity assessment in participants with heterogeneous mental health conditions
    (Springer Nature, 2024) Lamichhane, Bishal; Moukaddam, Nidal; Sabharwal, Ashutosh
    Mobile sensing-based depression severity assessment could complement the subjective questionnaires-based assessment currently used in practice. However, previous studies on mobile sensing for depression severity assessment were conducted on homogeneous mental health condition participants; evaluation of possible generalization across heterogeneous groups has been limited. Similarly, previous studies have not investigated the potential of free-living audio data for depression severity assessment. Audio recordings from free-living could provide rich sociability features to characterize depressive states. We conducted a study with 11 healthy individuals, 13 individuals with major depressive disorder, and eight individuals with schizoaffective disorders. Communication logs and location data from the participants’ smartphones and continuous audio recordings of free-living from a wearable audioband were obtained over a week for each participant. The depression severity prediction model trained using communication log and location data features had a root mean squared error (rmse) of 6.80. Audio-based sociability features further reduced the rmse to 6.07 (normalized rmse of 0.22). Audio-based sociability features also improved the F1 score in the five-class depression category classification model from 0.34 to 0.46. Thus, free-living audio-based sociability features complement the commonly used mobile sensing features to improve depression severity assessment. The prediction results obtained with mobile sensing-based features are better than the rmse of 9.83 (normalized rmse of 0.36) and the F1 score of 0.25 obtained with a baseline model. Additionally, the predicted depression severity had a significant correlation with reported depression severity (correlation coefficient of 0.76, $$p<$$0.001). Thus, our work shows that mobile sensing could model depression severity across participants with heterogeneous mental health conditions, potentially offering a screening tool for depressive symptoms monitoring in the broader population.
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    Modeling Suicidality with Multimodal Impulsivity Characterization in Participants with Mental Health Disorder
    (Hindawi, 2023) Moukaddam, Nidal; Lamichhane, Bishal; Salas, Ramiro; Goodman, Wayne; Sabharwal, Ashutosh
    Introduction. Suicide is one of the leading causes of death across different age groups. The persistence of suicidal ideation and the progression of suicidal ideations to action could be related to impulsivity, the tendency to act on urges with low temporal latency, and little forethought. Quantifying impulsivity could thus help suicidality estimation and risk assessments in ideation-to-action suicidality frameworks. Methods. To model suicidality with impulsivity quantification, we obtained questionnaires, behavioral tests, heart rate variability (HRV), and resting state functional magnetic resonance imaging measurements from 34 participants with mood disorders. The participants were categorized into three suicidality groups based on their Mini-International Neuropsychiatric Interview: none, low, and moderate to severe. Results. Questionnaire and HRV-based impulsivity measures were significantly different between the suicidality groups with higher subscales of impulsivity associated with higher suicidality. A multimodal system to characterize impulsivity objectively resulted in a classification accuracy of 96.77% in the three-class suicidality group prediction task. Conclusions. This study elucidates the relative sensitivity of various impulsivity measures in differentiating participants with suicidality and demonstrates suicidality prediction with high accuracy using a multimodal objective impulsivity characterization in participants with mood disorders.
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    Objective Sociability and Impulsivity Measures for Dimensional Assessment of Mental Health
    (2024-04-16) Lamichhane, Bishal; Sabharwal, Ashutosh; Sano, Akane; Patel, Ankit; Beier, Margaret
    Mental health disorders have a high prevalence and are on the rise globally. Subjective self-reports used for mental health assessment, with their recall and reporting biases, pose a scalability challenge for accurate and frequent assessment. Additionally, the existing nosology of disorders has rampant comorbidity and heterogeneity, creating challenges in the diagnosis and treatment. Instead of the dichotomous diagnostic labeling of mental health based on subjective self-reports, as is currently practiced, assessing mental health along the objectively measured bio-behavioral dimensions of functioning could provide a foundation for scalable, robust, and accurate mental healthcare. Towards the goal of improved mental health assessment, this thesis investigated objective measures of sociability and impulsivity, the bio-behavioral dimensions of functioning implicated in several mental health disorders. Sociability represents an individual’s tendency to interact and socialize with others. Sociability deficits and altered sociability patterns are commonly implicated across several mental health disorders. In this thesis, we propose using free-living audio to obtain sociability measures objectively. We developed a deep learning-based audio processing pipeline, ECoNet, that addresses the challenges of free-living audio processing to infer sociability measures accurately. The inferred sociability measures were significantly correlated with dimensional mental health measures in a clinical study comprising participants with diverse mental health conditions. Free-living audio also complemented the conventional mobile sensing modalities for dimensional mental health score prediction. Our results establish the potential of free-living audio-based objective sociability measures to represent mental health state. Impulsivity represents an individual’s tendency to act on urges without sufficient forethought about one’s action. Several mental health disorders commonly implicate heightened impulsivity. In this thesis, we propose using multimodal behavioral, physiological, and neurobiological measurements to model impulsivity objectively. We conducted a clinical study with mood disorder participants and discovered the complementarity of objective modalities to model impulsivity and predict the clinical outcome of suicidality with high accuracy. The complementarity of modalities was additionally validated with a large open-source dataset. To exploit the richness of neurobiological measurements for predicting impulsivity, we developed ImpulsNet. ImpulsNet is a lightweight deep learning model based on multi-task learning that outperforms existing models in the literature to predict impulsivity. Our results establish the feasibility of objectively modeling impulsivity for mental health applications. This thesis provides a foundation for dimensional mental health assessment using objective sociability and impulsivity measures. Further validation in larger studies should be pursued in future work. Other objectively assessed dimensions of functioning should also be explored to better represent mental health in the vastness of bio-behavioral space.
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    Predicting Psychotic Relapse in Schizophrenia With Mobile Sensor Data: Routine Cluster Analysis
    (JMIR, 2022) Zhou, Joanne; Lamichhane, Bishal; Ben-Zeev, Dror; Campbell, Andrew; Sano, Akane
    Background: Behavioral representations obtained from mobile sensing data can be helpful for the prediction of an oncoming psychotic relapse in patients with schizophrenia and the delivery of timely interventions to mitigate such relapse. Objective: In this study, we aim to develop clustering models to obtain behavioral representations from continuous multimodal mobile sensing data for relapse prediction tasks. The identified clusters can represent different routine behavioral trends related to daily living of patients and atypical behavioral trends associated with impending relapse. Methods: We used the mobile sensing data obtained from the CrossCheck project for our analysis. Continuous data from six different mobile sensing-based modalities (ambient light, sound, conversation, acceleration, etc) obtained from 63 patients with schizophrenia, each monitored for up to a year, were used for the clustering models and relapse prediction evaluation. Two clustering models, Gaussian mixture model (GMM) and partition around medoids (PAM), were used to obtain behavioral representations from the mobile sensing data. These models have different notions of similarity between behaviors as represented by the mobile sensing data, and thus, provide different behavioral characterizations. The features obtained from the clustering models were used to train and evaluate a personalized relapse prediction model using balanced random forest. The personalization was performed by identifying optimal features for a given patient based on a personalization subset consisting of other patients of similar age. Results: The clusters identified using the GMM and PAM models were found to represent different behavioral patterns (such as clusters representing sedentary days, active days but with low communication, etc). Although GMM-based models better characterized routine behaviors by discovering dense clusters with low cluster spread, some other identified clusters had a larger cluster spread, likely indicating heterogeneous behavioral characterizations. On the other hand, PAM model-based clusters had lower variability of cluster spread, indicating more homogeneous behavioral characterization in the obtained clusters. Significant changes near the relapse periods were observed in the obtained behavioral representation features from the clustering models. The clustering model-based features, together with other features characterizing the mobile sensing data, resulted in an F2 score of 0.23 for the relapse prediction task in a leave-one-patient-out evaluation setting. The obtained F2 score was significantly higher than that of a random classification baseline with an average F2 score of 0.042. Conclusions: Mobile sensing can capture behavioral trends using different sensing modalities. Clustering of the daily mobile sensing data may help discover routine and atypical behavioral trends. In this study, we used GMM-based and PAM-based cluster models to obtain behavioral trends in patients with schizophrenia. The features derived from the cluster models were found to be predictive for detecting an oncoming psychotic relapse. Such relapse prediction models can be helpful in enabling timely interventions.
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    Towards objective, temporally resolved neurobehavioral predictors of emotional state
    (Elsevier, 2024) Kabotyanski, Katherine E.; Yi, Han G.; Hingorani, Rahul; Robinson, Brian S.; Cowley, Hannah P.; Fifer, Matthew S.; Wester, Brock A.; Lamichhane, Bishal; Sabharwal, Ashutosh; Allawala, Anusha B.; Rajesh, Sameer V.; Diab, Nabeel; Mathura, Raissa K.; Pirtle, Victoria; Adkinson, Joshua; Watrous, Andrew J.; Bartoli, Eleonora; Xiao, Jiayang; Banks, Garrett P.; Mathew, Sanjay J.; Goodman, Wayne K.; Pitkow, Xaq; Pouratian, Nader; Hayden, Benjamin Y.; Provenza, Nicole R.; Sheth, Sameer A.