Bishal Lamichhane

Bishal Lamichhane

Assistant Research Professor, Rice University

Publications

Please refer to my google scholar page for latest updates my Google Scholar profile.

Multimodal Objective Assessment of Impulsivity in Healthy and Mood Disorder Participants

Published in NPP - Digital Psychiatry and Neuroscience (accepted for publication), 2025

We demonstrate how the signatures of impulsivity are distributed across multiple body systems in healthy and mood disorder participants.

Recommended citation: Bishal Lamichhane, Nidal Moukaddam, Ramiro Salas, Wayne Goodman, and Ashutosh Sabharwal (2025), 'Multimodal Objective Assessment of Impulsivity in Healthy and Mood Disorder Participants", NPP - Digital Psychiatry and Neuroscience.
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Mobile sensing-based depression severity assessment in participants with heterogeneous mental health conditions

Published in Scientific Reports, 2024

We develop mobile sensing-based depression severity prediction in a cohort of healthy, depressed, and psychotic individuals.

Recommended citation: Lamichhane, B., Moukaddam, N., & Sabharwal, A. (2024). Mobile sensing-based depression severity assessment in participants with heterogeneous mental health conditions. Scientific Reports, 14(1), 2024
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Modeling suicidality with multimodal impulsivity characterization in participants with mental health disorder

Published in Behavioural neurology, 2023

We demonstrate high suicidality classification using multimodal impulsivity representation.

Recommended citation: Moukaddam, N., Lamichhane, B., Salas, R., Goodman, W., & Sabharwal, A. (2023). Modeling suicidality with multimodal impulsivity characterization in participants with mental health disorder. Behavioural neurology, 2023(1), 8552180.
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Unsupervised Wireless Diarization: A Potential New Attack on Encrypted Wireless Networks

Published in IEEE International Conference on Communications, 2023

We provide a novel application of Unsupervised representation/clustering for node identification in wireless networks.

Recommended citation: Barati, C. N., Lamichhane, B., Liao, S., Graves, E., Swami, A., & Sabharwal, A. (2023, May). Unsupervised Wireless Diarization: A Potential New Attack on Encrypted Wireless Networks. In ICC 2023-IEEE International Conference on Communications (pp. 2312-2318). IEEE.
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Psychotic relapse prediction in schizophrenia patients using a personalized mobile sensing-based supervised deep learning model

Published in IEEE Journal of Biomedical and Health Informatics, 2023

We developed RelapsePredNet, a deep learning model to automatically predict psychotic relapses using smartphone data.

Recommended citation: Lamichhane, Bishal, Joanne Zhou, and Akane Sano. "Psychotic relapse prediction in schizophrenia patients using a personalized mobile sensing-based supervised deep learning model." IEEE Journal of Biomedical and Health Informatics 27, no. 7 (2023): 3246-3257.
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Psychotic relapse prediction in schizophrenia patients using a personalized mobile sensing-based supervised deep learning model

Published in IEEE Journal of Biomedical and Health Informatics, 2023

We developed RelapsePredNet, a deep learning model to automatically predict psychotic relapses using smartphone data.

Recommended citation: Lamichhane, Bishal, Joanne Zhou, and Akane Sano. "Psychotic relapse prediction in schizophrenia patients using a personalized mobile sensing-based supervised deep learning model." IEEE Journal of Biomedical and Health Informatics 27, no. 7 (2023): 3246-3257.
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Evaluation of chatgpt for nlp-based mental health applications

Published in Arxiv, 2023

We provide one of the first demonstrations of zero-shot prediction capability of ChatGPT across a broad NLP-based mental health application tasks.

Recommended citation: Lamichhane, B. (2023). Evaluation of chatgpt for nlp-based mental health applications. arXiv preprint arXiv:2303.15727.
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A Template Matching Based Cough Detection Algorithm Using IMU Data From Earbuds

Published in International Conference on Biomedical and Health Informatics (BHI), 2022

We provide a hardware/run-time efficient cough detection algorithm using IMU sensor (inertial measurement unit sensor).

Recommended citation: Lamichhane, B., Nemati, E., Ahmed, T., Rahman, M., Kuang, J., & Gao, A. (2022, September). A Template Matching Based Cough Detection Algorithm Using IMU Data From Earbuds. In 2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI) (pp. 01-04). IEEE.
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Improved Healthcare Access in Low-resource Regions: A Review of Technological Solutions

Published in Arxiv, 2022

We review technological solutions to healthcare challenges for low-resource settings, identifying cluster of papers in four broad areas: hardware, ICT, mobile health, and emerging technologies.

Recommended citation: Lamichhane, B., & Neupane, N. (2022). Improved Healthcare Access in Low-resource Regions: A Review of Technological Solutions. arXiv preprint arXiv:2205.10913.
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ECoNet: Estimating Everyday Conversational Network From Free-Living Audio for Mental Health Applications

Published in IEEE Pervasive Computing, 2022

The technical paper on ECoNet where we describe the development of Everyday conversational network estimator.

Recommended citation: Lamichhane, B., Moukaddam, N., Patel, A. B., & Sabharwal, A. (2022). ECoNet: Estimating Everyday Conversational Network From Free-Living Audio for Mental Health Applications. IEEE Pervasive Computing, 21(2), 32-40.
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Predicting psychotic relapse in schizophrenia with mobile sensor data: routine cluster analysis

Published in JMIR mHealth and uHealth, 2022

We develop a novel behavioral representation of mobile sensing data using cluster analysis for psychotic relapse prediction.

Recommended citation: Zhou, J., Lamichhane, B., Ben-Zeev, D., Campbell, A., & Sano, A. (2022). Predicting psychotic relapse in schizophrenia with mobile sensor data: routine cluster analysis. JMIR mHealth and uHealth, 10(4), e31006.
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Patient-independent schizophrenia relapse prediction using mobile sensor based daily behavioral rhythm changes

Published in MobiHealth, 2021

We provide a first demonstration of patient-independent psychotic relapse prediction performance using mobile sensing data with behavioral templates.

Recommended citation: Lamichhane, B., Ben-Zeev, D., Campbell, A., Choudhury, T., Hauser, M., Kane, J., ... & Sano, A. (2021). Patient-independent schizophrenia relapse prediction using mobile sensor based daily behavioral rhythm changes. In Wireless Mobile Communication and Healthcare: 9th EAI International Conference, MobiHealth 2020, Virtual Event, November 19, 2020, Proceedings 9 (pp. 18-33). Springer International Publishing.
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Automated detection of activity onset after postictal generalized EEG suppression

Published in BMC Medical Informatics and Decision Making, 2021

We investigate automatic detection of postictal generalized EEG suppression signature using a machine learning model, demonstrating the feasibility of such approaches.

Recommended citation: Lamichhane, B., Kim, Y., Segarra, S., Zhang, G., Lhatoo, S., Hampson, J., & Jiang, X. (2020). Automated detection of activity onset after postictal generalized EEG suppression. BMC Medical Informatics and Decision Making, 20, 1-10.
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CNN-SkelPose: a CNN-based skeleton estimation algorithm for clinical applications

Published in Journal of Ambient Intelligence and Humanized Computing, 2019

We develop a deep learning-based model for skeleton joint detection, one of the first such model for patient monitoring scenario with low patient-background contrast.

Recommended citation: Zavala-Mondragon, L. A., Lamichhane, B., Zhang, L., & Haan, G. D. (2020). CNN-SkelPose: a CNN-based skeleton estimation algorithm for clinical applications. Journal of Ambient Intelligence and Humanized Computing, 11(6), 2369-2380.
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Towards stress detection in real-life scenarios using wearable sensors: normalization factor to reduce variability in stress physiology

Published in eHealth 360°: International Summit on eHealth, 2017

One of the first studies to investigate stress detection in free-living with physiological sensors. We report the large effect of inter-individual differences in stress physiology and propose normalization factor leading to improved stress predictin accuracy.

Recommended citation: Lamichhane, B., Großekathöfer, U., Schiavone, G., & Casale, P. (2017). Towards stress detection in real-life scenarios using wearable sensors: normalization factor to reduce variability in stress physiology. In eHealth 360°: International Summit on eHealth, Budapest, Hungary, June 14-16, 2016, Revised Selected Papers (pp. 259-270). Springer International Publishing.
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Comparison of machine learning techniques for psychophysiological stress detection

Published in Pervasive Computing Paradigms for Mental Health: 5th International Conference, MindCare, 2016

We conduct a laboratory-based stress test and evaluate different machine learning models for stress detection.

Recommended citation: Smets, E., Casale, P., Großekathöfer, U., Lamichhane, B., De Raedt, W., Bogaerts, K., ... & Van Hoof, C. (2016). Comparison of machine learning techniques for psychophysiological stress detection. In Pervasive Computing Paradigms for Mental Health: 5th International Conference, MindCare 2015, Milan, Italy, September 24-25, 2015, Revised Selected Papers 5 (pp. 13-22). Springer International Publishing.
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Unobtrusive monitoring of neonatal brain temperature using a zero-heat-flux sensor matrix

Published in IEEE Journal of Biomedical and Health Informatics, 2014

We develop a novel zero-heat-flux-based temperature monitoring sensor with novelty on sensor form factor with matrix design. The sensor is tested in neonatal monitoring in a hospital and compared with alternative modalities for core temperature monitoring.

Recommended citation: Atallah, L., Bongers, E., Lamichhane, B., & Bambang-Oetomo, S. (2014). Unobtrusive monitoring of neonatal brain temperature using a zero-heat-flux sensor matrix. IEEE Journal of Biomedical and Health Informatics, 20(1), 100-107.
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