The Experts below are selected from a list of 5505 Experts worldwide ranked by ideXlab platform
Lars Edenbrandt - One of the best experts on this subject based on the ideXlab platform.
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detection of frequently overlooked Electrocardiographic Lead reversals using artificial neural networks
American Journal of Cardiology, 1996Co-Authors: Bo Heden, Mattias Ohlsson, Ralf Rittner, Olle Pahlm, Carsten Peterson, Holger Holst, Mattias Mjoman, Lars EdenbrandtAbstract:Abstract Artificial neural networks can be used to recognize Lead reversals in the 12-Lead ECG at very high specificity, and the sensitivity was much higher than that of a conventional interpretation program. The neural networks developed in this and an earlier study for detection of Lead reversals, in combination with an algorithm for the right arm/right foot Lead reversal, would recognize approximately 75% of Lead reversals encountered in clinical practice.
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artificial neural networks for recognition of Electrocardiographic Lead reversal
American Journal of Cardiology, 1995Co-Authors: Bo Hede N, Mattias Ohlsson, Lars Edenbrandt, Ralf Rittner, Olle Pahlm, Carsten PetersonAbstract:Misplacement of electrodes during the recording of an electrocardiogram (ECG) can cause an incorrect interpretation, misdiagnosis, and subsequent lack of proper treatment. The purpose of this study was twofold: (1) to develop artificial neural networks that yield peak sensitivity for the recognition of right/left arm Lead reversal at a very high specificity; and (2) to compare the performances of the networks with those of 2 widely used rule-based interpretation programs. The study was based on 11,009 ECGs recorded in patients at an emergency department using computerized electrocardiographs. Each of the ECGs was used to computationally generate an ECG with right/left arm Lead reversal. Neural networks were trained to detect ECGs with right/left arm Lead reversal. Different networks and rule-based criteria were used depending on the presence or absence of P waves. The networks and the criteria all showed a very high specificity (99.87% to 100%). The neural networks performed better than the rule-based criteria, both when P waves were present (sensitivity 99.1%) or absent (sensitivity 94.5%). The corresponding sensitivities for the best criteria were 93.9% and 39.3%, respectively. An estimated 300 million ECGs are recorded annually in the world. The majority of these recordings are performed using computerized electrocardiographs, which include algorithms for detection of right/left arm Lead reversals. In this study, neural networks performed better than conventional algorithms and the differences in sensitivity could result in 100,000 to 400,000 right/left arm Lead reversals being detected by networks but not by conventional interpretation programs.
Carsten Peterson - One of the best experts on this subject based on the ideXlab platform.
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detection of frequently overlooked Electrocardiographic Lead reversals using artificial neural networks
American Journal of Cardiology, 1996Co-Authors: Bo Heden, Mattias Ohlsson, Ralf Rittner, Olle Pahlm, Carsten Peterson, Holger Holst, Mattias Mjoman, Lars EdenbrandtAbstract:Abstract Artificial neural networks can be used to recognize Lead reversals in the 12-Lead ECG at very high specificity, and the sensitivity was much higher than that of a conventional interpretation program. The neural networks developed in this and an earlier study for detection of Lead reversals, in combination with an algorithm for the right arm/right foot Lead reversal, would recognize approximately 75% of Lead reversals encountered in clinical practice.
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artificial neural networks for recognition of Electrocardiographic Lead reversal
American Journal of Cardiology, 1995Co-Authors: Bo Hede N, Mattias Ohlsson, Lars Edenbrandt, Ralf Rittner, Olle Pahlm, Carsten PetersonAbstract:Misplacement of electrodes during the recording of an electrocardiogram (ECG) can cause an incorrect interpretation, misdiagnosis, and subsequent lack of proper treatment. The purpose of this study was twofold: (1) to develop artificial neural networks that yield peak sensitivity for the recognition of right/left arm Lead reversal at a very high specificity; and (2) to compare the performances of the networks with those of 2 widely used rule-based interpretation programs. The study was based on 11,009 ECGs recorded in patients at an emergency department using computerized electrocardiographs. Each of the ECGs was used to computationally generate an ECG with right/left arm Lead reversal. Neural networks were trained to detect ECGs with right/left arm Lead reversal. Different networks and rule-based criteria were used depending on the presence or absence of P waves. The networks and the criteria all showed a very high specificity (99.87% to 100%). The neural networks performed better than the rule-based criteria, both when P waves were present (sensitivity 99.1%) or absent (sensitivity 94.5%). The corresponding sensitivities for the best criteria were 93.9% and 39.3%, respectively. An estimated 300 million ECGs are recorded annually in the world. The majority of these recordings are performed using computerized electrocardiographs, which include algorithms for detection of right/left arm Lead reversals. In this study, neural networks performed better than conventional algorithms and the differences in sensitivity could result in 100,000 to 400,000 right/left arm Lead reversals being detected by networks but not by conventional interpretation programs.
Bryan Navarette - One of the best experts on this subject based on the ideXlab platform.
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effect of low dose droperidol on the qt interval during and after general anesthesia a placebo controlled study
Anesthesiology, 2005Co-Authors: Paul F White, Dajun Song, Joao Abrao, Kevin W Klein, Bryan NavaretteAbstract:Background: Since the effects of antiemetic doses of droperidol on the QT interval have not been previously studied, the authors designed a randomized, double-blind, placebo-controlled study to evaluate the intraoperative and postoperative effects of small-dose droperidol (0.625 and 1.25 mg intravenous) on the QT interval when used for antiemetic prophylaxis during general anesthesia. Methods: One hundred twenty outpatients undergoing otolaryngologic procedures with a standardized general anesthetic technique were enrolled in this study. After anesthetic induction and before the surgical incision, 60 patients were given either saline or 0.625 or 1.25 mg intravenous droperidol in a total volume of 2 ml. A standard Electrocardiographic Lead II was recorded immediately before and every minute after the injection of the study medication during a 10-min observation period. The QTc (QT interval corrected for heart rate) was evaluated from the recorded Electrocardiographic strips. In 60 additional patients, a 12-Lead electrocardiogram was obtained before and at specific intervals up to 2 h after surgery to assess the effects of droperidol and general anesthesia on the QTc. Any abnormal heartbeats or arrhythmias during the operation or the subsequent 2-h monitoring interval were also noted. Results: Intravenous droperidol, 0.625 and 1.25 mg, prolonged the QT interval by an average of 15 ± 40 and 22 ± 41 ms, respectively, at 3–6 min after administration during general anesthesia, but these changes did not differ significantly from that seen with saline (12 ± 35 ms) (all values mean ± SD). There were no statistically significant differences among the three study groups in the number of patients with greater than 10% prolongation in QTc (vs. baseline). Although general anesthesia was associated with a 14- to 16-ms prolongation of the QTc interval in the early postoperative period, there was no evidence of droperidol-induced QTc prolongation after surgery. Finally, there were no ectopic heartbeats observed on any of the Electrocardiographic rhythm strips or 12-Lead recordings during the perioperative period. Conclusion: Use of a small dose of droperidol (0.625–1.25 mg intravenous) for antiemetic prophylaxis during general anesthesia was not associated with a statistically significant increase in the QTc interval compared with saline. More importantly, there was no evidence of any droperidol-induced QTc prolongation immediately after surgery.
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effect of low dose droperidol on the qt interval during and after general anesthesia
2005Co-Authors: Paul F White, Dajun Song, Joao Abrao, Kevin W Klein, Bryan NavaretteAbstract:Background: Since the effects of antiemetic doses of droperidol on the QT interval have not been previously studied, the authors designed a randomized, double-blind, placebo-controlled study to evaluate the intraoperative and postoperative effects of small-dose droperidol (0.625 and 1.25 mg intravenous) on the QT interval when used for antiemetic prophylaxis during general anesthesia. Methods: One hundred twenty outpatients undergoing otolaryngologic procedures with a standardized general anesthetic technique were enrolled in this study. After anesthetic induction and before the surgical incision, 60 patients were given either saline or 0.625 or 1.25 mg intravenous droperidol in a total volume of 2 ml. A standard Electrocardiographic Lead II was recorded immediately before and every minute after the injection of the study medication during a 10-min observation period. The QTc (QT interval corrected for heart rate) was evaluated from the recorded Electrocardiographic strips. In 60 additional patients, a 12-Lead electrocardiogram was obtained before and at specific intervals up to 2 h after surgery to assess the effects of droperidol and general anesthesia on the QTc. Any abnormal heartbeats or arrhythmias during the operation or the subsequent 2-h monitoring interval were also noted. Results: Intravenous droperidol, 0.625 and 1.25 mg, prolonged the QT interval by an average of 15 40 and 22 41 ms, respectively, at 3‐6 min after administration during general anesthesia, but these changes did not differ significantly from that seen with saline (12 35 ms) (all values mean SD). There were no statistically significant differences among the three study groups in the number of patients with greater than 10% prolongation in QTc (vs. baseline). Although general anesthesia was associated with a 14- to 16-ms prolongation of the QTc interval in the early postoperative period, there was no evidence of droperidol-induced QTc prolongation after surgery. Finally, there were no ectopic heartbeats observed on any of the Electrocardiographic rhythm strips or 12-Lead recordings during the perioperative period. Conclusion: Use of a small dose of droperidol (0.625‐1.25 mg intravenous) for antiemetic prophylaxis during general anesthesia was not associated with a statistically significant increase in the QTc interval compared with saline. More importantly, there was no evidence of any droperidol-induced QTc prolongation immediately after surgery.
Olle Pahlm - One of the best experts on this subject based on the ideXlab platform.
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detection of frequently overlooked Electrocardiographic Lead reversals using artificial neural networks
American Journal of Cardiology, 1996Co-Authors: Bo Heden, Mattias Ohlsson, Ralf Rittner, Olle Pahlm, Carsten Peterson, Holger Holst, Mattias Mjoman, Lars EdenbrandtAbstract:Abstract Artificial neural networks can be used to recognize Lead reversals in the 12-Lead ECG at very high specificity, and the sensitivity was much higher than that of a conventional interpretation program. The neural networks developed in this and an earlier study for detection of Lead reversals, in combination with an algorithm for the right arm/right foot Lead reversal, would recognize approximately 75% of Lead reversals encountered in clinical practice.
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artificial neural networks for recognition of Electrocardiographic Lead reversal
American Journal of Cardiology, 1995Co-Authors: Bo Hede N, Mattias Ohlsson, Lars Edenbrandt, Ralf Rittner, Olle Pahlm, Carsten PetersonAbstract:Misplacement of electrodes during the recording of an electrocardiogram (ECG) can cause an incorrect interpretation, misdiagnosis, and subsequent lack of proper treatment. The purpose of this study was twofold: (1) to develop artificial neural networks that yield peak sensitivity for the recognition of right/left arm Lead reversal at a very high specificity; and (2) to compare the performances of the networks with those of 2 widely used rule-based interpretation programs. The study was based on 11,009 ECGs recorded in patients at an emergency department using computerized electrocardiographs. Each of the ECGs was used to computationally generate an ECG with right/left arm Lead reversal. Neural networks were trained to detect ECGs with right/left arm Lead reversal. Different networks and rule-based criteria were used depending on the presence or absence of P waves. The networks and the criteria all showed a very high specificity (99.87% to 100%). The neural networks performed better than the rule-based criteria, both when P waves were present (sensitivity 99.1%) or absent (sensitivity 94.5%). The corresponding sensitivities for the best criteria were 93.9% and 39.3%, respectively. An estimated 300 million ECGs are recorded annually in the world. The majority of these recordings are performed using computerized electrocardiographs, which include algorithms for detection of right/left arm Lead reversals. In this study, neural networks performed better than conventional algorithms and the differences in sensitivity could result in 100,000 to 400,000 right/left arm Lead reversals being detected by networks but not by conventional interpretation programs.
Mattias Ohlsson - One of the best experts on this subject based on the ideXlab platform.
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detection of frequently overlooked Electrocardiographic Lead reversals using artificial neural networks
American Journal of Cardiology, 1996Co-Authors: Bo Heden, Mattias Ohlsson, Ralf Rittner, Olle Pahlm, Carsten Peterson, Holger Holst, Mattias Mjoman, Lars EdenbrandtAbstract:Abstract Artificial neural networks can be used to recognize Lead reversals in the 12-Lead ECG at very high specificity, and the sensitivity was much higher than that of a conventional interpretation program. The neural networks developed in this and an earlier study for detection of Lead reversals, in combination with an algorithm for the right arm/right foot Lead reversal, would recognize approximately 75% of Lead reversals encountered in clinical practice.
-
artificial neural networks for recognition of Electrocardiographic Lead reversal
American Journal of Cardiology, 1995Co-Authors: Bo Hede N, Mattias Ohlsson, Lars Edenbrandt, Ralf Rittner, Olle Pahlm, Carsten PetersonAbstract:Misplacement of electrodes during the recording of an electrocardiogram (ECG) can cause an incorrect interpretation, misdiagnosis, and subsequent lack of proper treatment. The purpose of this study was twofold: (1) to develop artificial neural networks that yield peak sensitivity for the recognition of right/left arm Lead reversal at a very high specificity; and (2) to compare the performances of the networks with those of 2 widely used rule-based interpretation programs. The study was based on 11,009 ECGs recorded in patients at an emergency department using computerized electrocardiographs. Each of the ECGs was used to computationally generate an ECG with right/left arm Lead reversal. Neural networks were trained to detect ECGs with right/left arm Lead reversal. Different networks and rule-based criteria were used depending on the presence or absence of P waves. The networks and the criteria all showed a very high specificity (99.87% to 100%). The neural networks performed better than the rule-based criteria, both when P waves were present (sensitivity 99.1%) or absent (sensitivity 94.5%). The corresponding sensitivities for the best criteria were 93.9% and 39.3%, respectively. An estimated 300 million ECGs are recorded annually in the world. The majority of these recordings are performed using computerized electrocardiographs, which include algorithms for detection of right/left arm Lead reversals. In this study, neural networks performed better than conventional algorithms and the differences in sensitivity could result in 100,000 to 400,000 right/left arm Lead reversals being detected by networks but not by conventional interpretation programs.