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- W3109368956 abstract "At-home monitoring of lung health enables the early detection and treatment of respiratory diseases like asthma and chronic obstructive pulmonary disease (COPD). To allow for discreet continuous monitoring, various approaches have been proposed to estimate the respiratory rate from an electrocardiogram (ECG) signal. Unfortunately, respiratory rate can only provide a non-specific, incomplete picture of lung health. This paper introduces an algorithm to extract more respiratory information from the ECG signal: in addition to respiratory rate, the algorithm also derives the fractional inspiratory time(FIT), which is a direct measure of airway obstruction. The algorithm is based on a gated recurrent neural network that infers vital respiratory information from a two-lead ECG signal. The network is trained and tested on different test subjects and reports up to 0.099, and 0.243 normalized root mean squared error in the computation of FIT and respiration rate, respectively." @default.
- W3109368956 created "2020-12-07" @default.
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- W3109368956 date "2020-12-02" @default.
- W3109368956 modified "2023-09-27" @default.
- W3109368956 title "Extraction Of Fractional Inspiratory Time And Respiration Rate FromElectrocardiogram Signals" @default.
- W3109368956 hasPublicationYear "2020" @default.
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