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Isaac Yang – One of the best experts on this subject based on the ideXlab platform.

  • Signal quality classification for an Ambulatory Monitoring system
    2010 Annual International Conference of the IEEE Engineering in Medicine and Biology, 2010
    Co-Authors: Yongji Fu, Hallberg S. Bryan, Isaac Yang

    Abstract:

    A signal quality classification algorithm is presented to evaluate signal quality in Ambulatory Monitoring system. Acoustic based signal is classified as good signal, weak signal or noisy signal. Certain features in the acquired signal are extracted and analyzed to differentiate the class of signal quality. With this classification, wrong physiological estimation due to poor signal quality can be eliminated to avoid wrong conclusions and instructions in the Ambulatory system.

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Igone Etxeberria – One of the best experts on this subject based on the ideXlab platform.

  • A specific neural network used on a portable system for classifying activities in Ambulatory Monitoring
    , 2006
    Co-Authors: Nicolas Fourty, David Guiraud, Philippe Fraisse, Guillaume Perolle, Igone Etxeberria

    Abstract:

    Our modern societies are confronted to a new growing problem: the global ageing of population. In order to find ways to encourage elderly people living longer at their own home, ensuring the necessary vigilance and security at the lower cost possible, some tele-assistance systems are already available commercially. This article presents a specific neural network used on a portable system for classifying activities in Ambulatory Monitoring. After more precisions about this specific neural network in the second part we will present some results from our prototype stemmed from gerontologic institute Ingema.

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  • A specific neural network used on a portable system for classifying activities in Ambulatory Monitoring
    2006 IEEE International Conference on Industrial Technology, 2006
    Co-Authors: Nicolas Fourty, David Guiraud, Philippe Fraisse, Guillaume Perolle, Igone Etxeberria

    Abstract:

    Our modern societies are confronted to a new growing problem: the global ageing of population. In order to find ways to encourage elderly people living longer at their own home, ensuring the necessary vigilance and security at the lower cost possible, some tele-assistance systems are already available commercially. This article presents a specific neural network used on a portable system for classifying activities in Ambulatory Monitoring. After more precisions about this specific neural network in the second part we will present some results from our prototype stemming from gerontologic institute Ingema.

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Hallberg S. Bryan – One of the best experts on this subject based on the ideXlab platform.

  • EMBC – Rapid trend detection for an Ambulatory Monitoring system
    2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2011
    Co-Authors: Hallberg S. Bryan

    Abstract:

    An algorithm for rapid trend detection of physiological parameter is introduced for Ambulatory Monitoring applications. Kalman prediction error of monitored parameter is used to estimate the physiological status and detect rapid change. With this algorithm, rapid trend during Ambulatory Monitoring can be found to predict disease exacerbation; and it is also applied to identify outliers of measurement due to poor signal quality to avoid false alarms.

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  • Signal quality classification for an Ambulatory Monitoring system
    2010 Annual International Conference of the IEEE Engineering in Medicine and Biology, 2010
    Co-Authors: Yongji Fu, Hallberg S. Bryan, Isaac Yang

    Abstract:

    A signal quality classification algorithm is presented to evaluate signal quality in Ambulatory Monitoring system. Acoustic based signal is classified as good signal, weak signal or noisy signal. Certain features in the acquired signal are extracted and analyzed to differentiate the class of signal quality. With this classification, wrong physiological estimation due to poor signal quality can be eliminated to avoid wrong conclusions and instructions in the Ambulatory system.

    Free Register to Access Article