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• W2044284890 abstract "In order to monitor the cardiac arrest patients response to therapy, there is a need for methods that can reliably interpret the different types of cardiac rhythms that can occur during a resuscitation episode. These rhythms can be categorized to five groups; ventricular tachycardia, ventricular fibrillation, pulseless electrical activity, asystole, and pulse generating rhythm. The objective of this study was to develop machine learning algorithms to automatically recognize these rhythms. We proposed a detection algorithm based on the nearest-manifold classification approach using a group of 8 time-domain features as statistical measures on the signal itself, as well as the first and second differences. The overall accuracy of the cardiac arrest rhythm interpretation is 79% which is 9% better than our prior work. The sensitivity/specificity of shockable/non-shockable rhythms is 92/95%." @default.
• W2044284890 created "2016-06-24" @default.
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• W2044284890 date "2014-05-01" @default.
• W2044284890 modified "2023-09-25" @default.
• W2044284890 title "Nearest-manifold classification approach for cardiac arrest rhythm interpretation during resuscitation" @default.
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• W2044284890 doi "https://doi.org/10.1109/icassp.2014.6854276" @default.
• W2044284890 hasPublicationYear "2014" @default.
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