Multi-lead T wave end detection based on statistical hypothesis testing

Abstract : Automatic detection of electrocardiograms (ECG) waves provides important information for cardiac disease diagnosis. A new T wave end location algorithm based on multi-lead ECG processing is proposed in this paper. A statistical hypothesis testing algorithm is applied to two auxiliary signals computed by filtering and differentiating ECG signals. The performance of the algorithm has been evaluated using the PhysioNet QT database. The standard deviation of the errors between automatic annotations and manual ones are within tolerance accepted by cardiologist.
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Communication dans un congrès
David Dagan Feng and Olivier Dubois and Janan Zaytoon and Ewart Carson. Modelling and Control in Biomedical Systems, Sep 2006, Reims, France. IFAC, pp.93-98, 2006, 〈10.3182/20060920-3-FR-2912.00021〉
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https://hal.inria.fr/hal-00854838
Contributeur : Qinghua Zhang <>
Soumis le : mercredi 28 août 2013 - 11:28:09
Dernière modification le : vendredi 25 mai 2018 - 12:02:05

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Alfredo Illanes Manriquez, Qinghua Zhang, Claire Médigue, Yves Papelier, Michel Sorine. Multi-lead T wave end detection based on statistical hypothesis testing. David Dagan Feng and Olivier Dubois and Janan Zaytoon and Ewart Carson. Modelling and Control in Biomedical Systems, Sep 2006, Reims, France. IFAC, pp.93-98, 2006, 〈10.3182/20060920-3-FR-2912.00021〉. 〈hal-00854838〉

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