{"667677":{"#nid":"667677","#data":{"type":"event","title":"PhD Defense by Michael Chan","body":[{"value":"\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cstrong\u003E\u003Cspan\u003ETitle:\u0026nbsp;\u003C\/span\u003E\u003C\/strong\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EEnabling Accurate Cardiopulmonary Monitoring using Machine Learning and a Chest-Worn Wearable Patch\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cstrong\u003E\u003Cspan\u003EDate\u003C\/span\u003E\u003C\/strong\u003E\u003Cspan\u003E:\u0026nbsp;5\/17\/2023\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cstrong\u003E\u003Cspan\u003ETime\u003C\/span\u003E\u003C\/strong\u003E\u003Cspan\u003E:\u0026nbsp;12pm-2pm\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cstrong\u003E\u003Cspan\u003ELocation \/ Metting Link:\u003C\/span\u003E\u003C\/strong\u003E\u003Cspan\u003E\u0026nbsp;TSRB 523a \/\u0026nbsp;\u003C\/span\u003E\u003Ca href=\u0022https:\/\/gatech.zoom.us\/j\/92139753425?pwd=NEwyZ0tmOU1VVHVldm1kNzl3YzNvQT09\u0026amp;from=addon#success\u0022\u003Ehttps:\/\/gatech.zoom.us\/j\/92139753425?pwd=NEwyZ0tmOU1VVHVldm1kNzl3YzNvQT09\u0026amp;from=addon#success\u003C\/a\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cstrong\u003E\u003Cspan\u003EMichael Chan, MSE\u003C\/span\u003E\u003C\/strong\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EMachine Learning PhD Student\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EDepartment of Biomedical Engineering\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EGeorgia Institute of Technology\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cstrong\u003E\u003Cspan\u003ECommittee\u003C\/span\u003E\u003C\/strong\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E1 Omer T. Inan, PhD (Advisor,\u0026nbsp;School of Electrical and Computer Engineering\u0026nbsp;and Department of Biomedical Engineering\u003Cspan\u003E, Georgia Tech\u003C\/span\u003E)\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E2\u0026nbsp;Rishi Kamaleswaran,\u0026nbsp;PhD (Department of Biomedical Informatics, Emory University)\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E3 Thomas Pl\u00f6tz,\u0026nbsp;PhD (School of Interactive Computing\u003Cspan\u003E, Georgia Tech\u003C\/span\u003E)\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E4 Mozziyar Etemadi, MD,\u0026nbsp;PhD (Department of Biomedical Engineering, Northwestern University)\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E5\u0026nbsp;Mark A. Davenport,\u0026nbsp;PhD (School of Electrical \u0026amp; Computer Engineering, Georgia Tech)\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cstrong\u003E\u003Cspan\u003EAbstract\u003C\/span\u003E\u003C\/strong\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003ENon-invasive physiological signals, particularly Seismocardiogram, Photoplethysmogram, and Electrocardiogram, measured at the chest provide\u0026nbsp;diagnostic and prognostic value for disease monitoring out of the clinic. Conventionally, cardiopulmonary parameters indicative of our health status such as\u0026nbsp;SpO2, respiratory rate (RR), heart rate (HR), and oxygen uptake (VO2) can be estimated from these signals via algorithms based on\u0026nbsp;digital signal processing (DSP) and physiological knowledge. More recently,\u0026nbsp;the emergence of sophisticated machine learning (ML) models brings exciting improvements in this field.\u0026nbsp;By\u0026nbsp;applying black box models that work in natural language processing, computer vision, or automatic speech recognition on the target estimation tasks, we may improve the accuracy of the algorithms quickly. However, doing so may also result in a missed opportunity to leverage the conventional DSP techniques and the accumulated physiological knowledge toward the\u0026nbsp;estimation of\u0026nbsp;cardiopulmonary parameters.\u0026nbsp;To address this, we attempt to merge DSP, physiological knowledge, and ML gradually\u0026nbsp;in this dissertation. First,\u0026nbsp;we start by conservatively replacing one functional block of a conventional approach with a data-driven model to estimate SpO2. Next, we further replace multiple functional blocks\u0026nbsp;of conventional approaches\u0026nbsp;with ML to estimate RR and HR. Lastly, we merge\u0026nbsp;DSP, physiological knowledge, and ML cohesively to\u0026nbsp;estimate VO2, harnessing the representation capability of convolutional neural network, leveraging the interpretability of DSP, and exploiting the additional labels available at hand\u0026nbsp;and\u0026nbsp;known\u0026nbsp;physiological\u0026nbsp;knowledge through multi-task learning. Collectively, this work demonstrates an alternative algorithmic direction to improve the accuracy of\u0026nbsp;cardiopulmonary parameters \u003Cspan\u003Eestimated from\u0026nbsp;\u003C\/span\u003Enon-invasive physiological signals.\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EEnabling Accurate Cardiopulmonary Monitoring using Machine Learning and a Chest-Worn Wearable Patch\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n","format":"limited_html"}],"field_summary_sentence":[{"value":"Enabling Accurate Cardiopulmonary Monitoring using Machine Learning and a Chest-Worn Wearable Patch"}],"uid":"27707","created_gmt":"2023-05-08 16:42:35","changed_gmt":"2023-05-08 16:42:35","author":"Tatianna Richardson","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2023-05-17T12:00:00-04:00","event_time_end":"2023-05-17T14:00:00-04:00","event_time_end_last":"2023-05-17T14:00:00-04:00","gmt_time_start":"2023-05-17 16:00:00","gmt_time_end":"2023-05-17 18:00:00","gmt_time_end_last":"2023-05-17 18:00:00","rrule":null,"timezone":"America\/New_York"},"location":"TSRB 523a ","extras":[],"groups":[{"id":"221981","name":"Graduate Studies"}],"categories":[],"keywords":[{"id":"100811","name":"Phd Defense"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1788","name":"Other\/Miscellaneous"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"78771","name":"Public"},{"id":"174045","name":"Graduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}}}