W W Good1, B Erem2, B Zenger3, J Coll-Font4, J A Bergquist5, D H Brooks6, R S MacLeod5. 1. Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA; Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, USA; Nora Eccles Cardiovascular Research and Training Institute, University of Utah, Salt Lake City, UT, USA. Electronic address: wgood@sci.utah.edu. 2. TrueMotion, Boston, MA, USA. 3. Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA; Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, USA; Nora Eccles Cardiovascular Research and Training Institute, University of Utah, Salt Lake City, UT, USA; School of Medicine, University of Utah, Salt Lake City, UT, USA. 4. Cardiovascular Research Center (CVRC), Massachusetts General Hospital, Boston, MA, USA. 5. Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA; Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, USA; Nora Eccles Cardiovascular Research and Training Institute, University of Utah, Salt Lake City, UT, USA. 6. SPIRAL Group, ECE Dept., Northeastern University, Boston, MA, USA.
Abstract
OBJECTIVE: Despite a long history of ECG-based monitoring of acute ischemia quantified by several widely used clinical markers, the diagnostic performance of these metrics is not yet satisfactory, motivating a data-driven approach to leverage underutilized information in the electrograms. This study introduces a novel metric for acute ischemia, created using a machine learning technique known as Laplacian eigenmaps (LE), and compares the diagnostic and temporal performance of the LE metric against traditional metrics. METHODS: The LE technique uses dimensionality reduction of simultaneously recorded time signals to map them into an abstract space in a manner that highlights the underlying signal behavior. To evaluate the performance of an electrogram-based LE metric compared to current standard approaches, we induced episodes of transient, acute ischemia in large animals and captured the electrocardiographic response using up to 600 electrodes within the intramural and epicardial domains. RESULTS: The LE metric generally detected ischemia earlier than all other approaches and with greater accuracy. Unlike other metrics derived from specific features of parts of the signals, the LE approach uses the entire signal and provides a data-driven strategy to identify features that reflect ischemia. CONCLUSION: The superior performance of the LE metric suggests there are underutilized features of electrograms that can be leveraged to detect the presence of acute myocardial ischemia earlier and more robustly than current methods. SIGNIFICANCE: The earlier detection capabilities of the LE metric on the epicardial surface provide compelling motivation to apply the same approach to ECGs recorded from the body surface.
OBJECTIVE: Despite a long history of ECG-based monitoring of acute ischemia quantified by several widely used clinical markers, the diagnostic performance of these metrics is not yet satisfactory, motivating a data-driven approach to leverage underutilized information in the electrograms. This study introduces a novel metric for acute ischemia, created using a machine learning technique known as Laplacian eigenmaps (LE), and compares the diagnostic and temporal performance of the LE metric against traditional metrics. METHODS: The LE technique uses dimensionality reduction of simultaneously recorded time signals to map them into an abstract space in a manner that highlights the underlying signal behavior. To evaluate the performance of an electrogram-based LE metric compared to current standard approaches, we induced episodes of transient, acute ischemia in large animals and captured the electrocardiographic response using up to 600 electrodes within the intramural and epicardial domains. RESULTS: The LE metric generally detected ischemia earlier than all other approaches and with greater accuracy. Unlike other metrics derived from specific features of parts of the signals, the LE approach uses the entire signal and provides a data-driven strategy to identify features that reflect ischemia. CONCLUSION: The superior performance of the LE metric suggests there are underutilized features of electrograms that can be leveraged to detect the presence of acute myocardial ischemia earlier and more robustly than current methods. SIGNIFICANCE: The earlier detection capabilities of the LE metric on the epicardial surface provide compelling motivation to apply the same approach to ECGs recorded from the body surface.
Authors: Brett M Burton; Kedar K Aras; Wilson W Good; Jess D Tate; Brian Zenger; Rob S MacLeod Journal: Ann Biomed Eng Date: 2018-05-21 Impact factor: 3.934
Authors: F Kornreich; T J Montague; M Kavadias; J Segers; P M Rautaharju; M B Horacek; B Taccardi Journal: Am J Cardiol Date: 1987-12-01 Impact factor: 2.778
Authors: Brian Zenger; Wilson W Good; Jake A Bergquist; Brett M Burton; Jess D Tate; Leo Berkenbile; Vikas Sharma; Rob S MacLeod Journal: Physiol Meas Date: 2020-02-05 Impact factor: 2.833
Authors: Brian Zenger; Wilson W Good; Jake A Bergquist; Lindsay C Rupp; Maura Perez; Gregory J Stoddard; Vikas Sharma; Rob S MacLeod Journal: J Electrocardiol Date: 2021-07-21 Impact factor: 1.438