{"id":{"repo_id":"vcu","oai_identifier":"oai:scholarscompass.vcu.edu:etd-1137"},"canonical_url":"https://search.dev.ndltd.org/etd/vcu/oai:scholarscompass.vcu.edu:etd-1137","repository":{"repo_id":"vcu","name":"Virginia Commonwealth University","base_url":"https://scholarscompass.vcu.edu/do/oai/"},"display":{"title":"A Comparative Analysis of Methods for Baseline Drift Removal in Preterm Infant Respiration Signals","abstract":"Breathing is a vital function intrinsic to the survival of any human being. In preterm infants it is an important indicator of maturation and feeding competency, which is a hallmark for hospital release. The recommended method of measurement of infant respiration is the use of thermistors. Accurate event detection within thermistor generated signals relies heavily upon effective noise reduction, specifically baseline drift removal. Baseline drift originates from several sensor-based factors, including thermistor placement within the sensor and in relation to the infant nares. This work compares four methods for baseline drift removal using the same event detection algorithm. The methods compared were a linear spline subtraction, a cubic spline subtraction, a neural network baseline approximation, and a double differentiation of the thermistor signal. The method yielding the highest event detection rate was shown to be the double differentiation method, which serves to attenuate the baseline drift to zero without approximating and subtracting it.","abstract_html":"Breathing is a vital function intrinsic to the survival of any human being. In preterm infants it is an important indicator of maturation and feeding competency, which is a hallmark for hospital release. The recommended method of measurement of infant respiration is the use of thermistors. Accurate event detection within thermistor generated signals relies heavily upon effective noise reduction, specifically baseline drift removal. Baseline drift originates from several sensor-based factors, including thermistor placement within the sensor and in relation to the infant nares. This work compares four methods for baseline drift removal using the same event detection algorithm. The methods compared were a linear spline subtraction, a cubic spline subtraction, a neural network baseline approximation, and a double differentiation of the thermistor signal. The method yielding the highest event detection rate was shown to be the double differentiation method, which serves to attenuate the baseline drift to zero without approximating and subtracting it.","abstract_has_math":false,"creators":["Ramnarain, Pallavi"],"institution":null,"degree_name":"Master of Science","degree_level":"Thesis","degree_discipline":"Biomedical Engineering","degree_department":null,"school":null,"contributors":["Paul Wetzel"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2010,"date_issued":"2010-06-08T07:00:00Z","date_published":"2010-06-08T07:00:00Z","updated_at":"2026-07-24T05:53:26Z","subjects":["Biomedical Engineering and Bioengineering","Engineering"],"languages":[],"rights":["© The Author"],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["https://scholarscompass.vcu.edu/etd/138"],"render_values":[{"text":"https://scholarscompass.vcu.edu/etd/138","href":"https://scholarscompass.vcu.edu/etd/138","code":true}]}]},"links":{"outbound_url":"https://doi.org/10.25772/4MCA-Z955","outbound_label":"DOI","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Paul Wetzel"]},{"key":"dc:creator","label":"Author","values":["Ramnarain, Pallavi"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2015-08-13T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Biomedical Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Biomedical Engineering and Bioengineering","Engineering"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["© The Author"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://doi.org/10.25772/4MCA-Z955","https://scholarscompass.vcu.edu/etd/138"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Breathing is a vital function intrinsic to the survival of any human being. In preterm infants it is an important indicator of maturation and feeding competency, which is a hallmark for hospital release. The recommended method of measurement of infant respiration is the use of thermistors. Accurate event detection within thermistor generated signals relies heavily upon effective noise reduction, specifically baseline drift removal. Baseline drift originates from several sensor-based factors, including thermistor placement within the sensor and in relation to the infant nares. This work compares four methods for baseline drift removal using the same event detection algorithm. The methods compared were a linear spline subtraction, a cubic spline subtraction, a neural network baseline approximation, and a double differentiation of the thermistor signal. The method yielding the highest event detection rate was shown to be the double differentiation method, which serves to attenuate the baseline drift to zero without approximating and subtracting it."]},{"key":"dc:title","label":"Title","values":["A Comparative Analysis of Methods for Baseline Drift Removal in Preterm Infant Respiration Signals"]}]}],"canonical_facts":{"dc:contributor":["Paul Wetzel"],"dc:creator":["Ramnarain, Pallavi"],"dc:date.available":["2015-08-13T07:00:00Z"],"dc:description.abstract":["Breathing is a vital function intrinsic to the survival of any human being. In preterm infants it is an important indicator of maturation and feeding competency, which is a hallmark for hospital release. The recommended method of measurement of infant respiration is the use of thermistors. Accurate event detection within thermistor generated signals relies heavily upon effective noise reduction, specifically baseline drift removal. Baseline drift originates from several sensor-based factors, including thermistor placement within the sensor and in relation to the infant nares. This work compares four methods for baseline drift removal using the same event detection algorithm. The methods compared were a linear spline subtraction, a cubic spline subtraction, a neural network baseline approximation, and a double differentiation of the thermistor signal. The method yielding the highest event detection rate was shown to be the double differentiation method, which serves to attenuate the baseline drift to zero without approximating and subtracting it."],"dc:identifier":["https://doi.org/10.25772/4MCA-Z955","https://scholarscompass.vcu.edu/etd/138"],"dc:rights":["© The Author"],"dc:subject":["Biomedical Engineering and Bioengineering","Engineering"],"dc:title":["A Comparative Analysis of Methods for Baseline Drift Removal in Preterm Infant Respiration Signals"],"thesis:degree_discipline":["Biomedical Engineering"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["Master of Science"]},"updated_at":"2026-07-24T05:53:26Z"}