{"id":{"repo_id":"eastern-wash","oai_identifier":"oai:dc.ewu.edu:theses-1277"},"canonical_url":"https://search.dev.ndltd.org/etd/eastern-wash/oai:dc.ewu.edu:theses-1277","repository":{"repo_id":"eastern-wash","name":"Eastern Washington University","base_url":"https://dc.ewu.edu/do/oai/"},"display":{"title":"Heartbeat location assistance for electrocardiograms","abstract":"\"The electrocardiogram (ECG) is the main source of heartbeat analysis throughout the medical community, due to the distinctive appearance of the QRS complex at the time of each beat. There are other signals that also exhibit distinctive patterns at the time of each heartbeat; however, the ECG is still the most prevalently used. Analyzing the ECG alone can be problematic because ECG data can be noisy. The noise often makes it appear as if the heartbeat is missing or that multiple beats occurred in quick succession. This research will analyze the association between a variety of signals and the ECG, and use those associations to predict heartbeat location with greater accuracy than just analyzing the ECG alone. After analyzing all 10 minutes of an ECG signal along with other signals such as Blood Pressure (BP) and Stroke Volume (SV), the goals of this research are: (1) Preprocess the data so that signals show clear shapes and noise is minimized; (2) Generate templates from the provided signals that correspond to a clear QRS complex in the ECG to provide information for what needs to occur in other signals in order for a beat to be annotated; (3) Compare these templates to test records and annotate beats where the templates are approximately matched. By achieving the stated goals this research will aid the medical community in determining the optimal type of template to use, and the number and type of signals required to locate beats with accuracy\"--Leaf iv.","abstract_html":""The electrocardiogram (ECG) is the main source of heartbeat analysis throughout the medical community, due to the distinctive appearance of the QRS complex at the time of each beat. There are other signals that also exhibit distinctive patterns at the time of each heartbeat; however, the ECG is still the most prevalently used. Analyzing the ECG alone can be problematic because ECG data can be noisy. The noise often makes it appear as if the heartbeat is missing or that multiple beats occurred in quick succession. This research will analyze the association between a variety of signals and the ECG, and use those associations to predict heartbeat location with greater accuracy than just analyzing the ECG alone. After analyzing all 10 minutes of an ECG signal along with other signals such as Blood Pressure (BP) and Stroke Volume (SV), the goals of this research are: (1) Preprocess the data so that signals show clear shapes and noise is minimized; (2) Generate templates from the provided signals that correspond to a clear QRS complex in the ECG to provide information for what needs to occur in other signals in order for a beat to be annotated; (3) Compare these templates to test records and annotate beats where the templates are approximately matched. By achieving the stated goals this research will aid the medical community in determining the optimal type of template to use, and the number and type of signals required to locate beats with accuracy"--Leaf iv.","abstract_has_math":false,"creators":["Bass, Sarah"],"institution":null,"degree_name":"Master of Science (MS) in Computer Science","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2015,"date_issued":"2015-01-01T08:00:00Z","date_published":"2015-01-01T08:00:00Z","updated_at":"2026-07-24T02:13:00Z","subjects":["Electrocardiography--Data processing","Electrocardiography--Interpretation","Time-series analysis--Data processing","Signal processing--Digital techniques","Sequential pattern mining","Computer Sciences"],"languages":[],"rights":["Access is available to all users"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://dc.ewu.edu/theses/278","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Bass, Sarah"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science (MS) in Computer Science"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Electrocardiography--Data processing","Electrocardiography--Interpretation","Time-series analysis--Data processing","Signal processing--Digital techniques","Sequential pattern mining","Computer Sciences"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["Access is available to all users"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://dc.ewu.edu/theses/278"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["\"The electrocardiogram (ECG) is the main source of heartbeat analysis throughout the medical community, due to the distinctive appearance of the QRS complex at the time of each beat. There are other signals that also exhibit distinctive patterns at the time of each heartbeat; however, the ECG is still the most prevalently used. Analyzing the ECG alone can be problematic because ECG data can be noisy. The noise often makes it appear as if the heartbeat is missing or that multiple beats occurred in quick succession. This research will analyze the association between a variety of signals and the ECG, and use those associations to predict heartbeat location with greater accuracy than just analyzing the ECG alone. After analyzing all 10 minutes of an ECG signal along with other signals such as Blood Pressure (BP) and Stroke Volume (SV), the goals of this research are: (1) Preprocess the data so that signals show clear shapes and noise is minimized; (2) Generate templates from the provided signals that correspond to a clear QRS complex in the ECG to provide information for what needs to occur in other signals in order for a beat to be annotated; (3) Compare these templates to test records and annotate beats where the templates are approximately matched. By achieving the stated goals this research will aid the medical community in determining the optimal type of template to use, and the number and type of signals required to locate beats with accuracy\"--Leaf iv."]},{"key":"dc:title","label":"Title","values":["Heartbeat location assistance for electrocardiograms"]}]}],"canonical_facts":{"dc:creator":["Bass, Sarah"],"dc:description.abstract":["\"The electrocardiogram (ECG) is the main source of heartbeat analysis throughout the medical community, due to the distinctive appearance of the QRS complex at the time of each beat. There are other signals that also exhibit distinctive patterns at the time of each heartbeat; however, the ECG is still the most prevalently used. Analyzing the ECG alone can be problematic because ECG data can be noisy. The noise often makes it appear as if the heartbeat is missing or that multiple beats occurred in quick succession. This research will analyze the association between a variety of signals and the ECG, and use those associations to predict heartbeat location with greater accuracy than just analyzing the ECG alone. After analyzing all 10 minutes of an ECG signal along with other signals such as Blood Pressure (BP) and Stroke Volume (SV), the goals of this research are: (1) Preprocess the data so that signals show clear shapes and noise is minimized; (2) Generate templates from the provided signals that correspond to a clear QRS complex in the ECG to provide information for what needs to occur in other signals in order for a beat to be annotated; (3) Compare these templates to test records and annotate beats where the templates are approximately matched. By achieving the stated goals this research will aid the medical community in determining the optimal type of template to use, and the number and type of signals required to locate beats with accuracy\"--Leaf iv."],"dc:identifier":["https://dc.ewu.edu/theses/278"],"dc:rights":["Access is available to all users"],"dc:subject":["Electrocardiography--Data processing","Electrocardiography--Interpretation","Time-series analysis--Data processing","Signal processing--Digital techniques","Sequential pattern mining","Computer Sciences"],"dc:title":["Heartbeat location assistance for electrocardiograms"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["Master of Science (MS) in Computer Science"]},"updated_at":"2026-07-24T02:13:00Z"}