The 8th EAI International Conference on Mobile Computing, Applications and Services

Research Article

A Cross-Layer Coding for Scalable ECG streaming

  • @INPROCEEDINGS{10.4108/eai.30-11-2016.2267099,
        author={Mohammed Alloulah and Mark Dawkins and Alison Burdett},
        title={A Cross-Layer Coding for Scalable ECG streaming},
        proceedings={The 8th EAI International Conference on Mobile Computing, Applications and Services},
        publisher={ACM},
        proceedings_a={MOBICASE},
        year={2016},
        month={12},
        keywords={wireless ban ecg coding},
        doi={10.4108/eai.30-11-2016.2267099}
    }
    
  • Mohammed Alloulah
    Mark Dawkins
    Alison Burdett
    Year: 2016
    A Cross-Layer Coding for Scalable ECG streaming
    MOBICASE
    ACM
    DOI: 10.4108/eai.30-11-2016.2267099
Mohammed Alloulah1,*, Mark Dawkins1, Alison Burdett1
  • 1: Toumaz
*Contact email: alloulah@outlook.com

Abstract

Mobile electrocardiogram (ECG) streaming in body area networks (BANs) is challenging owing to an inherently inconsistent wireless channel, which generally cannot be assumed wide-sense-stationary. Common conventional ECG compression is entropy-based and thus is fundamentally at odds with a BAN channel plagued with variabilities. That is, if the wireless signal experiences a deep fade regime, excessive errors, user contention, and RF interference could all combine so as to result in an interruption in ECG streaming until channel quality recovers. To mitigate against this hard limit on channel quality (i.e. the cliff effect), this paper proposes a linear ECG coding method whereby proneness to mis-reception due to channel errors, contention, and/or interference is traded for a soft, proportional degradation in signal definition. As such, the likelihood of ECG streaming interruption in BANs is vastly lessened while also enhancing capacity and relieving wireless medium contention. This improved robustness and scalability in the wireless network is particularly sought after in mission-critical healthcare applications with stringent QoS demands.