Wireless Mobile Communication and Healthcare. Second International ICST Conference, MobiHealth 2011, Kos Island, Greece, October 5-7, 2011. Revised Selected Papers

Research Article

Towards Continuous Wheeze Detection Body Sensor Node as a Core of Asthma Monitoring System

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  • @INPROCEEDINGS{10.1007/978-3-642-29734-2_23,
        author={Dinko Oletic and Bruno Arsenali and Vedran Bilas},
        title={Towards Continuous Wheeze Detection Body Sensor Node as a Core of Asthma Monitoring System},
        proceedings={Wireless Mobile Communication and Healthcare. Second International ICST Conference, MobiHealth 2011, Kos Island, Greece, October 5-7, 2011. Revised Selected Papers},
        proceedings_a={MOBIHEALTH},
        year={2012},
        month={10},
        keywords={asthma telemonitoring body sensor networks wheeze detection LPC Durbin’s recursion},
        doi={10.1007/978-3-642-29734-2_23}
    }
    
  • Dinko Oletic
    Bruno Arsenali
    Vedran Bilas
    Year: 2012
    Towards Continuous Wheeze Detection Body Sensor Node as a Core of Asthma Monitoring System
    MOBIHEALTH
    Springer
    DOI: 10.1007/978-3-642-29734-2_23
Dinko Oletic1,*, Bruno Arsenali1,*, Vedran Bilas1,*
  • 1: University of Zagreb
*Contact email: dinko.oletic@fer.hr, bruno.arsenali@fer.hr, vedran.bilas@fer.hr

Abstract

This article presents a wheeze detection method for wearable body sensor nodes used in management of asthma. Firstly, a short review of current state of telemonitoring in management of chronic asthma is given. A concept of the asthma monitoring system built around a body sensor node analysing respiratory sounds is proposed, with a smart phone as a self-management center and additional sensor nodes for environment monitoring. In search for a wheeze detection algorithm suitable for low power continuous operation on wireless sensor node, a simple algorithm based on the 4-th order linear prediction coefficients (LPC) method is presented. Predictor error energy ratio of Durbin’s algorithm is used as the only feature. Algorithm is implemented on low power digital signal processor (DSP) to evaluate its performance. Sensitivity (SE) of 70.9%, specificity (SP) 98.6% and accuracy (ACC) of 90.29% are achieved using pre-recorded test signals. Program complexity is analysed in order to identify possibilities of lowering power consumption.