MULTI-PARAMETER PHYSIOLOGICAL TRACKING SYSTEM FOR DIAGNOSIS OF SEPSIS

Bibliographic Details
Main Author:	Guo, Fei
Language:	English
Published:	Case Western Reserve University School of Graduate Studies / OhioLINK 2021
Subjects:	Electrical Engineering Biomedical Research
Online Access:	http://rave.ohiolink.edu/etdc/view?acc_num=case1613129350013438

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spelling	ndltd-OhioLink-oai-etd.ohiolink.edu-case16131293500134382021-08-03T07:16:40Z MULTI-PARAMETER PHYSIOLOGICAL TRACKING SYSTEM FOR DIAGNOSIS OF SEPSIS Guo, Fei Electrical Engineering Biomedical Research Sepsis is a systemic inflammatory response to infection that can progress to septic shock and multi-organ dysfunction and a life-threating situation without proper and prompt clinical treatment. It causes numerous alterations to the human dynamic system that if quantified can provide diagnostic and prognostic insights.To address this issue, this dissertation proposes a multi-channel physiological signal analysis system that elucidates the association of characteristic alterations of the cardiovascular and ventilatory systems along with their impact on sepsis. Variability analysis techniques depict biological time series with regards to the fluctuations, spectral composition, scale-free variations and degrees of regularity or complexity. Specifically, multi-dimensional noninvasive biomarkers are generated from Heart Rate Variability (HRV) analysis, Respiratory Rate Variability (RRV) analysis and Blood Pressure Variability (BPV) analysis. In this dissertation, linear and nonlinear analysis including Time-Frequency domain analysis, Detrended Fluctuation Analysis (DFA), Multiscale Entropy (MSE) and Poincare analysis can differentiate pathological sepsis patients from normal ICU patients with statistically significant levels.In addition, a septic prediction framework using low-density vital signs is proposed to provide early warning indicators of the onset of sepsis for clinical personnel. The proposed early prediction index generated from LSTM network and the ensemble XGBoosting classifier overcame challenges from a high percentage of unavailable data and extreme unbalanced classes challenges to reach prediction results with the AUROC at 0.8132 and 0.8255, respectively. 2021-06-21 English text Case Western Reserve University School of Graduate Studies / OhioLINK http://rave.ohiolink.edu/etdc/view?acc_num=case1613129350013438 http://rave.ohiolink.edu/etdc/view?acc_num=case1613129350013438 unrestricted This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws.
collection	NDLTD
language	English
sources	NDLTD
topic	Electrical Engineering Biomedical Research
spellingShingle	Electrical Engineering Biomedical Research Guo, Fei MULTI-PARAMETER PHYSIOLOGICAL TRACKING SYSTEM FOR DIAGNOSIS OF SEPSIS
author	Guo, Fei
author_facet	Guo, Fei
author_sort	Guo, Fei
title	MULTI-PARAMETER PHYSIOLOGICAL TRACKING SYSTEM FOR DIAGNOSIS OF SEPSIS
title_short	MULTI-PARAMETER PHYSIOLOGICAL TRACKING SYSTEM FOR DIAGNOSIS OF SEPSIS
title_full	MULTI-PARAMETER PHYSIOLOGICAL TRACKING SYSTEM FOR DIAGNOSIS OF SEPSIS
title_fullStr	MULTI-PARAMETER PHYSIOLOGICAL TRACKING SYSTEM FOR DIAGNOSIS OF SEPSIS
title_full_unstemmed	MULTI-PARAMETER PHYSIOLOGICAL TRACKING SYSTEM FOR DIAGNOSIS OF SEPSIS
title_sort	multi-parameter physiological tracking system for diagnosis of sepsis
publisher	Case Western Reserve University School of Graduate Studies / OhioLINK
publishDate	2021
url	http://rave.ohiolink.edu/etdc/view?acc_num=case1613129350013438
work_keys_str_mv	AT guofei multiparameterphysiologicaltrackingsystemfordiagnosisofsepsis
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MULTI-PARAMETER PHYSIOLOGICAL TRACKING SYSTEM FOR DIAGNOSIS OF SEPSIS

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