Published in IEEE BHI (to appear), 2024
Sensation seeking prediction model based on fMRI where we demonstrate the need for age-specific modeling.
Recommended citation: Zishen Li, Bishal Lamichhane, Ankit Patel, Ramiro Salas, Nidal Moukaddam, and Ashutosh S (2024), Predicting Sensation-Seeking from Resting-State fMRI: The Need for Age-Specific Models, IEEE BHI 2024.
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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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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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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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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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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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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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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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Published in Interspeech, 2022
We report the findings of our discover of depression severity markers in free-living dyadic interaction parameters.
Recommended citation: Lamichhane, Bishal, Nidal Moukaddam, Ankit B. Patel, and Ashutosh Sabharwal. "Dyadic interaction assessment from free-living audio for depression severity assessment." arXiv preprint arXiv:2209.03901 (2022).
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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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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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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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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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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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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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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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Published in BioMed Research International, 2017
The application of Eigendecomposition is demonstrated for modeling daily behaviors.
Recommended citation: Schiavone, G., Lamichhane, B., & Van Hoof, C. (2017). The Double Layer Methodology and the Validation of Eigenbehavior Techniques Applied to Lifestyle Modeling. BioMed Research International, 2017(1), 4593956.
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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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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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