{"666553":{"#nid":"666553","#data":{"type":"event","title":"Ph.D. Proposal Oral Exam - Yen-Pang Lai","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003ETitle:\u0026nbsp; \u003C\/strong\u003E\u003Cem\u003EAutomatic Multiple Fiducial Point Delineation for the Non-contact Seismocadiogram Signals Using Deep Learning Technology\u003C\/em\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003ECommittee:\u0026nbsp; \u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Ying Zhang, Advisor\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Durgin, Chair\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Inan\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EAbstract: \u003C\/strong\u003EThe objective of the proposed research is to develop a standalone automatic multiple fiducial-point delineation for the non-contact seismocardiogram (SCG) signals. SCG is the precordial vibration that contains temporal information about cardiac micro-events, and this vibration can be recorded in a non-contact fashion using a Doppler radar device. Non-contact SCG measurement alleviates patients\u0026rsquo; annoyance and can advance the development of the wireless healthcare system. However, delineation work for non-contact SCG signals is more difficult since they are more vulnerable to interference, and any assistant contact signals are avoided to achieve the fully non-contact measurement. To address this challenge, we formulated the multiple fiducial point delineation as a sequence-to-sequence task and leveraged multiple deep learning technologies to build up a standalone delineation framework for non-contact SCG signals. First, a SCG-CRF network consisting of a feature extraction block, a time series analysis block, and a joint tagging block was constructed to learn the conversion of the SCG signals and their corresponding labels. The SCG-CRF network was validated using both the contact SCG signal from the combined measurement of electrocardiography, breathing, and seismocardiogram (CEBS) database and the radar acceleration waveforms. As a part of the proposed work, a generative data-augmentation network will be developed to enhance the generalization of the SCG-CRF network. In addition, a segment filter will also be built to identify recognizable segments of non-contact SCG signals, and a waveform transformation model will be investigated to convert the non-contact SCG signals to contact-like SCG signals for the subsequent delineation. The accuracy and generalizability of the overall delineation framework will be evaluated using non-contact SCG signals captured in various states.\u003C\/p\u003E\r\n","summary":null,"format":"limited_html"}],"field_subtitle":"","field_summary":"","field_summary_sentence":[{"value":"Automatic Multiple Fiducial Point Delineation for the Non-contact Seismocadiogram Signals Using Deep Learning Technology"}],"uid":"28475","created_gmt":"2023-03-08 22:56:48","changed_gmt":"2023-03-08 22:56:48","author":"Daniela Staiculescu","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2023-03-23T13:00:00-04:00","event_time_end":"2023-03-23T15:00:00-04:00","event_time_end_last":"2023-03-23T15:00:00-04:00","gmt_time_start":"2023-03-23 17:00:00","gmt_time_end":"2023-03-23 19:00:00","gmt_time_end_last":"2023-03-23 19:00:00","rrule":null,"timezone":"America\/New_York"},"extras":[],"groups":[{"id":"434371","name":"ECE Ph.D. Proposal Oral Exams"}],"categories":[],"keywords":[{"id":"102851","name":"Phd proposal"},{"id":"1808","name":"graduate students"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1788","name":"Other\/Miscellaneous"}],"invited_audience":[{"id":"78771","name":"Public"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}}}