eHealth 360°. International Summit on eHealth, Budapest, Hungary, June 14-16, 2016, Revised Selected Papers

Research Article

Use Moving Average Filter to Reduce Noises in Wearable PPG During Continuous Monitoring

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  • @INPROCEEDINGS{10.1007/978-3-319-49655-9_26,
        author={Yan Chen and Dan Li and Yanhai Li and Xiaoyuan Ma and Jianming Wei},
        title={Use Moving Average Filter to Reduce Noises in Wearable PPG During Continuous Monitoring},
        proceedings={eHealth 360°. International Summit on eHealth, Budapest, Hungary, June 14-16, 2016, Revised Selected Papers},
        proceedings_a={EHEALTH360},
        year={2017},
        month={1},
        keywords={Heart rate PPG Moving average},
        doi={10.1007/978-3-319-49655-9_26}
    }
    
  • Yan Chen
    Dan Li
    Yanhai Li
    Xiaoyuan Ma
    Jianming Wei
    Year: 2017
    Use Moving Average Filter to Reduce Noises in Wearable PPG During Continuous Monitoring
    EHEALTH360
    Springer
    DOI: 10.1007/978-3-319-49655-9_26
Yan Chen1,*, Dan Li1,*, Yanhai Li1,*, Xiaoyuan Ma1,*, Jianming Wei1,*
  • 1: Shanghai Advanced Research Institute, Chinese Academy of Sciences
*Contact email: cheny@sari.ac.cn, lid@sari.ac.cn, liyh@sari.ac.cn, maxy@sari.ac.cn, wjm@sari.ac.cn

Abstract

In order to improve the accuracy of heart rate extracted from wearable photoplethysmography (PPG) signal, a new processing method based on moving average filtering is proposed. There are two cascaded moving average filters. The first filter is designed to remove baseline wandering as preprocessing. The second filter whose window size is adjusted according to the additional accelerometer signal is used to remove motion artifacts. During continuous monitoring, the parameters of these two filters change adaptively in accordance with a batch processing method. The results show that the proposed method can reconstruct a better waveform and improve the signal quality for calculating the beats per minute (BPM). Referenced with the vital sign monitoring instrument VS800 of Mindray company, the detecting accuracy of the proposed method is 7%–10% higher than adaptive filtering.