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DTSTART;TZID=Asia/Kolkata:20230417T210000
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DTSTAMP:20260421T221523
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UID:240578-1681765200-1681772400@ee.iisc.ac.in
SUMMARY:Ph.D. Thesis colloquium of Ms. Ritika Jain
DESCRIPTION:Advisor: Prof. A. G. Ramakrishnan  \n\n\n   \n\n\nTITLE: Multimodal sleep staging and diagnosis of sleep disorders \n MS Teams link   \n\n\nSleep staging is a tedious and time-consuming process carried out manually by clinicians in which they annotate overnight polysomnograph recordings. An automated sleep scoring system can perform faster and objective sleep staging. Methods are proposed to classify the sleep EEG data into multiple stages by utilizing temporal\, spectral\, time-frequency\, non-linear\, and statistical features and random undersampling with boosting technique (RUSBoost) on a decision tree classifier. The role of data augmentation and temporal context on classifier performance is evaluated for healthy controls and clinical populations. This work also attempts to classify different sleep disorders using single-channel EEG and evaluate the role of individual sleep stages in that task.  \n\n\n  \n\n\nSignificant contributions of the thesis:         \n\n\n        \n\n\nBinary classification of sleep and wake states for healthy individuals and clinical population:   \n\n\n\nFor this two-class classification problem\, we explored the performances of different modalities such as EEG\, EOG & EMG.  \n\n\nWe also performed ensemble empirical mode decomposition and Poincare plot analysis of the signal for identifying sleep and wake states. \n\n\n\nMulti-class classification of sleep stages using single channel EEG:  \n\n\n\nUtilising the knowledge from earlier works on binary classification\, we considered different sets of features and evaluated the performance of RUSBoost classifier on unseen test subjects. This work reports the performance of different n-class (n=2\,3\,4\,5\,6) classification problems on three publicly available datasets of overnight polysomnography recordings. \n\n\n\nMulti-modal classification of sleep stages using a hierarchical model  \n\n\n\nIn this work\, a six-level hierarchical model (HM) has been designed. The aim is to improve the sleep staging accuracy by breaking down the 5-class classification problem into six binary classification problems\, while also reducing the misclassifications among N1\, REM\, and wake stages.  \n\n\nIntroducing data augmentation (DA) and temporal context (TC) in the proposed hierarchical model to further improve sleep staging performance. We validated the results of DA and TC on healthy as well as clinical populations from seven publicly available datasets. \n\n\n\nDiagnosis of different sleep disorders using a single EEG channel  \n\n\n\nThis work aims to classify seven different sleep disorders and healthy controls using light gradient boosting model with a single-channel EEG.   \n\n\nWe examined the importance of different features in distinguishing various pathological groups and healthy individuals.  \n\n\nWe also evaluated the role of individual sleep stages in distinguishing the different disorders. \n\n\n                                                                                                           ALL ARE WELCOME \nPublications based on this Thesis \n  \nJournals \n1. Ritika Jain and Angarai Ganesan Ramakrishnan. Electrophysiological and neuroimaging studies–during resting state and sensory stimulation in disorders of consciousness: a review. Frontiers Neurosc.\, 14:987\, 2020 \n2. Ritika Jain and Ramakrishnan A G. Reliable sleep staging of unseen subjects with fusion of multiple EEG features and RUSBoost. Biomed. Sig. Proc. Control\, 70:103061\, 2021 \n  \nConferences \n1. Ritika Jain and Ramakrishnan Angarai Ganesan. Assessment of submentalis muscle activity for sleep-wake classification of healthy individuals and patients with sleep disorders. \nIn 44th IEEE EMBC 2022. IEEE\, 2022 \n2. Ritika Jain and Ramakrishnan Angarai Ganesan. Single EOG channel performs well in distinguishing sleep from wake state for both healthy individuals and patients. In 44th \nIEEE EMBC. IEEE\, 2022 \n3. Ritika Jain and Ramakrishnan Angarai Ganesan. Poincar ́e plot analysis for sleep-wake classification of unseen patients using a single EEG channel. In 17th IEEE Int. Symp. \nMed. Meas. Applns. IEEE\, 2022 \n4. Ritika Jain and Ramakrishnan Angarai Ganesan. Classifying sleep-wake states of patients by training on single EEG or EOG channel data from normal subjects. In 2022 IEEE Region 10 Symposium (TENSYMP)\, pages 1–5. IEEE\, 2022 \n5. Ritika Jain and Ramakrishnan Angarai Ganesan. An efficient sleep scoring method using visibility graph and temporal features of single-channel EEG. In 43rd Ann. Int. Conf IEEE EMBC\, pages 6306–6309. IEEE\, 20216. Ramakrishnan A G and Ritika Jain. Binary state prediction of sleep or wakefulness using EEG and EOG features. In 17th India Council Int. Conf (INDICON)\, pages 1–7. IEEE\, 20207. Ritika Jain and Angarai Ganesan Ramakrishnan. Sleep-awake classification using EEG band-power-ratios and complexity measures. In 2020 IEEE 17th India Council International Conference (INDICON)\, pages 1–6. IEEE\, 2020Manuscripts under Review \n  \n• Ritika Jain and Ramakrishnan A G. Modality-specific feature selection\, data augmentation\, and temporal context for superior performance in sleep staging. IEEE Jl. Of Biomedical & Health Informatics\, 2023. \n  \n                                                                 ALL ARE WELCOME – People outside IISc can join through the MS Teams link given.
URL:https://ee.iisc.ac.in/event/ph-d-thesis-colloquium-of-ritika-jain/
LOCATION:MMCR\, Hall C 241\, 1st floor\, EE department
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