{"id":{"repo_id":"njit","oai_identifier":"oai:digitalcommons.njit.edu:theses-1424"},"canonical_url":"https://search.dev.ndltd.org/etd/njit/oai:digitalcommons.njit.edu:theses-1424","repository":{"repo_id":"njit","name":"NJIT","base_url":"https://digitalcommons.njit.edu/do/oai/"},"display":{"title":"Automatic detection of qt and related intervals","abstract":"The objective of this thesis is divided into two main sections: The first section comprises of the development of the algorithm for the detection of the Q wave, Tmax afld the Tend to measure the intervals such as QT, QT corrected (QTc), RT, QT_{max} and RT_{max} respectively. The second section deals with the analysis of different variabilities including heart rate variability (HRV), QT, QTc, RT, QT_{max} and RT_{max}. Using the R wave points as reference points, the Q wave was detected by using the Differential Threshold Method (DTH). The Tmax was detected by using a search window on the derived signal of ECG starting from the R peak. The T_{max} was detected by two different procedures. The first procedure was a combination of two different methods: the Least Squares Method (LSI) and the Threshold Method (TH) and the second procedure was based on the Differential Threshold Method (DTH). Once the points were detected, the relationship between the heart rate variability (HRV) and corrected QT variability along with other variabilities was studied in this research. The algorithm was validated on ten patients of five minute data segment of paced breathing at 6 breaths per minute and 12 breaths per minute respectively. The algorithm for the detection of the Q wave, T _{max} and the T_{end} produced an overall success of 99.16% according to automatic verification of accuracy detection and 96.4% based on manual inspection. In this study, the duration of the QT interval was in the range of 450 to 500 milliseconds, which indicated normal duration of ventricular repolarization. The variability plots indicated similarity between HR variability and QT corrected variability.","abstract_html":"The objective of this thesis is divided into two main sections: The first section comprises of the development of the algorithm for the detection of the Q wave, Tmax afld the Tend to measure the intervals such as QT, QT corrected (QTc), RT, QT_{max} and RT_{max} respectively. The second section deals with the analysis of different variabilities including heart rate variability (HRV), QT, QTc, RT, QT_{max} and RT_{max}. Using the R wave points as reference points, the Q wave was detected by using the Differential Threshold Method (DTH). The Tmax was detected by using a search window on the derived signal of ECG starting from the R peak. The T_{max} was detected by two different procedures. The first procedure was a combination of two different methods: the Least Squares Method (LSI) and the Threshold Method (TH) and the second procedure was based on the Differential Threshold Method (DTH). Once the points were detected, the relationship between the heart rate variability (HRV) and corrected QT variability along with other variabilities was studied in this research. The algorithm was validated on ten patients of five minute data segment of paced breathing at 6 breaths per minute and 12 breaths per minute respectively. The algorithm for the detection of the Q wave, T _{max} and the T_{end} produced an overall success of 99.16% according to automatic verification of accuracy detection and 96.4% based on manual inspection. In this study, the duration of the QT interval was in the range of 450 to 500 milliseconds, which indicated normal duration of ventricular repolarization. The variability plots indicated similarity between HR variability and QT corrected variability.","abstract_has_math":false,"creators":["Gowdar, Divya"],"institution":null,"degree_name":"Master of Science in Biomedical Engineering - (M.S.)","degree_level":null,"degree_discipline":"Biomedical Engineering","degree_department":null,"school":null,"contributors":["Ronald H. Rockland","Stanley S. Reisman","Joel Schesser"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2006,"date_issued":"2006-05-31T07:00:00Z","date_published":"2006-05-31T07:00:00Z","updated_at":"2026-07-24T03:23:22Z","subjects":["Q wave","QT interval","Automatic detection","Biomedical Engineering and Bioengineering"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://digitalcommons.njit.edu/theses/425","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Ronald H. Rockland","Stanley S. 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The second section deals with the analysis of different variabilities including heart rate variability (HRV), QT, QTc, RT, QT_{max} and RT_{max}. Using the R wave points as reference points, the Q wave was detected by using the Differential Threshold Method (DTH). The Tmax was detected by using a search window on the derived signal of ECG starting from the R peak. The T_{max} was detected by two different procedures. The first procedure was a combination of two different methods: the Least Squares Method (LSI) and the Threshold Method (TH) and the second procedure was based on the Differential Threshold Method (DTH). Once the points were detected, the relationship between the heart rate variability (HRV) and corrected QT variability along with other variabilities was studied in this research. The algorithm was validated on ten patients of five minute data segment of paced breathing at 6 breaths per minute and 12 breaths per minute respectively. The algorithm for the detection of the Q wave, T _{max} and the T_{end} produced an overall success of 99.16% according to automatic verification of accuracy detection and 96.4% based on manual inspection. In this study, the duration of the QT interval was in the range of 450 to 500 milliseconds, which indicated normal duration of ventricular repolarization. The variability plots indicated similarity between HR variability and QT corrected variability."]},{"key":"dc:title","label":"Title","values":["Automatic detection of qt and related intervals"]}]}],"canonical_facts":{"dc:contributor":["Ronald H. Rockland","Stanley S. Reisman","Joel Schesser"],"dc:creator":["Gowdar, Divya"],"dc:description.abstract":["The objective of this thesis is divided into two main sections: The first section comprises of the development of the algorithm for the detection of the Q wave, Tmax afld the Tend to measure the intervals such as QT, QT corrected (QTc), RT, QT_{max} and RT_{max} respectively. The second section deals with the analysis of different variabilities including heart rate variability (HRV), QT, QTc, RT, QT_{max} and RT_{max}. Using the R wave points as reference points, the Q wave was detected by using the Differential Threshold Method (DTH). The Tmax was detected by using a search window on the derived signal of ECG starting from the R peak. The T_{max} was detected by two different procedures. The first procedure was a combination of two different methods: the Least Squares Method (LSI) and the Threshold Method (TH) and the second procedure was based on the Differential Threshold Method (DTH). Once the points were detected, the relationship between the heart rate variability (HRV) and corrected QT variability along with other variabilities was studied in this research. The algorithm was validated on ten patients of five minute data segment of paced breathing at 6 breaths per minute and 12 breaths per minute respectively. The algorithm for the detection of the Q wave, T _{max} and the T_{end} produced an overall success of 99.16% according to automatic verification of accuracy detection and 96.4% based on manual inspection. In this study, the duration of the QT interval was in the range of 450 to 500 milliseconds, which indicated normal duration of ventricular repolarization. The variability plots indicated similarity between HR variability and QT corrected variability."],"dc:identifier":["https://digitalcommons.njit.edu/theses/425"],"dc:subject":["Q wave","QT interval","Automatic detection","Biomedical Engineering and Bioengineering"],"dc:title":["Automatic detection of qt and related intervals"],"dc:type":["Thesis"],"thesis:degree_discipline":["Biomedical Engineering"],"thesis:degree_name":["Master of Science in Biomedical Engineering - (M.S.)"]},"updated_at":"2026-07-24T03:23:22Z"}