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Scalable Noise Mining in Long-Term Electrocardiographic Time-Series to Predict Death Following Heart Attacks
Chih-Chun Chia, Zeeshan SyedUniversity of Michigan
Presenter: Sen JiaoMar. 19, 2015
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Objective• Improve an algorithm for mining useful
information in electrocardiogram (ECG) to identify patients at an increased risk of death following heart attacks.
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Introduction
• Heart disease: 34% of all deaths each year in the U.S, 1 death in every 38 seconds
• Reducing mortality: inability to match patients to treatments that are most appropriate for individual risk.
• Implantable cardioverter defibrillator (ICD)• Current decision-making methods fail to
prescribe ICD to the majority.
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Introduction
• Biomarkers: to estimate patient risk and to match patients to treatments.
• Blood-based measurements, medical imaging: limited to available information.
• ECG may contain subtle but useful information, commonly perceived as noise.
• Morphologic variability (MV) in ECG: indicator of heart function.
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Background
• Electrocardiogram (ECG)
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Background
• Pathophysiology
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Background
• Morphological Variability (MV)
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Background
• Dynamic Time-Warping (DTW)
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Background
• Dynamic Time-Warping (DTW)
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Background
• Constrained DTW: prevent biologically implausible alignments– Boundary Conditions– Continuity– Monotonicity
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Background
• Constrained DTW
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Background
• Power Spectral Density – Lomb-Scargle periodogram
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Overall Flow Chart
O(pn2)
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Adaptive Down-sampling (ADAP)
• PAA, FastDTW,ADAP
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DTW with ADAP
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DTW with ADAP
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Evaluation
• 4-day continuous ECG data recorded• Patients follow-up for 90 days for
cardiovascular death• Evaluate MV, MV measured with PAA, MV
measured with ADAP• Measure areas under the receiver operating
characteristic curves (AUROCs)
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Results
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Results
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Results
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Results
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Results
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Conclusion
• ADAP substantially reduces runtime while providing similar performance to the basic MV algorithm that is not optimized for large volumes of data.
• The use of ADAP leads to more accurate performance than downsampling through the commonly used approach of PAA.
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Q&A
• Thank you!