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Identifying airway obstructions from photoplethysmography (PPG).

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Call No.	Ph.D. 2006
Author	Knorr, Bethany
Title	Identifying airway obstructions from photoplethysmography (PPG).
Date	2006
Abstract	Emergency medical response requires timely and efficient assessment and treatment of casualties. Mass casualty situations, in particular, strain the resources of emergency responders, prompting the need for a well-organized, effective, automated triage system. The Automated Remote Triage and Emergency Management Information System (ARTEMIS) has been developed to provide such a system. Based on the outcome of a specific medical model, ARTEMIS focuses on fundamental physiological characteristics including the ABC's (airway, breathing, and circulation) of triage to provide casualty and responder state information. Most recent work has focused on determining the state of the airway. Current technologies used for identifying airway status involve invasive techniques, are too cumbersome to implement in the field, or are not as reliable as desired for the purposes of ARTEMIS. A multitude of studies have shown links between the PPG signal and respiratory functions. Some preliminary work conducted by this group have also suggested that potential exists to use this data to identify airway obstruction in obstructive sleep apnea subjects as well as in pediatric patients. The paramount study focused on collecting data from a set of anesthetized, spontaneously breathing patients to further determine the effectiveness of using PPG analysis to detect airway obstruction. The subject set included 20 healthy adults (12 male, 8 female) anesthetized to undergo general surgery. Data were collected during three time periods: pre-, peri-, and postoperatively. The classifier employed features from the PPG waveform and it was first trained on a subject by subject basis with an overall accuracy of 85.5%. In order to be implemented on a wide-scale, however, the data must be combined to achieve generalized results yielding an accuracy of only 66.8%. It was determined, however, that the pre-operative PPG data were subject to influence from the anesthetic induction, which could lead to erroneous segment classification. The generalized model, therefore, focused on perioperative data resulting in an accuracy of 83.3% and postoperative data yielding results of an accuracy of 86.1%. The final model will be implemented to provide information on the state of the airway, a critical aspect of the ARTEMIS system.
Description	1 online resource (ix, 175 leaves) : illustrations (some color)
Format	application/pdf
Advisor	McGrath, Susan P.
Subject	Plethysmography. ; Airway (Medicine) -- Obstructions. ; Respiration -- Mathematical models. ; Respiration -- Data processing. ; Airway Obstruction -- diagnosis.
College	Thayer School of Engineering.
Issuing Body	Dartmouth Dissertations are made available through the Dartmouth Digital Library.
Degree	Thesis (Ph.D)--Dartmouth College.
Copyright	In Copyright. This material is protected by copyright and/or related rights. You are free to use this material in any way that is permitted by copyright law that applies to your use. For other uses, you need to obtain permission from the rights-holder(s). http://rightsstatements.org/page/InC/1.0/
DOI	https://doi.org/10.1349/ddlp.3189

Title	knorr2006 1
Full Text	IDENTIFYING AIRWAY OBSTRUCTIONS FROM PHOTOPLETHYSMOGRAPHY (PPG) A Thesis Submitted to the Faculty in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Engineering Sciences by Bethany Knorr DARTMOUTH COLLEGE Hanover, New Hampshire July 2006

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