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Mobile Networks for Biometric Data Analysis pp 247–258Cite as

A New Segmented-Beat Modulation Algorithm for Maternal ECG Estimation from Abdominal Recordings

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Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 392))

Abstract

The noninvasive fetal electrocardiogram (fECG) provides precious information about the physiological fetus state. It is extracted from abdominal recordings, obtained positioning surface electrodes on the maternal abdomen, by subtraction of the maternal ECG (mECG), often roughly estimated by simply concatenating a maternal-beat template. Aim of the present study is to propose a new algorithm for the mECG estimation based on a segmented-beat modulation method (SBMM) that adjusts the template length to the maternal physiological heart-rate variability (HRV) and reduces the level of noise. According to the SBMM, each maternal cardiac cycle (CC) is segmented into two segments, QRS and TUP, respectively independent and proportional to preceding RR interval. The estimated mECG is the concatenation of the template-beat, obtained as the median of the maternal beat after modulation and demodulation of TUP segment. The algorithm was applied to two (ARec1 and ARec2) 4-channel abdominal recordings obtained from pregnant women. ARec1 and ARec2 were both 60 s long and characterized by similar heart rate (HR: 80 bpm and 82 bpm) but different HRV (42 ms vs. 139 ms). Results indicate that the error in the mECG estimation is always small (<2.5 µV) but increases with HRV (ARec1: 0.87–1.65 µV; ARec2: 1.98–2.37 µV). In conclusion, the proposed algorithm based on the SBMM allows a clean mECG estimation from abdominal recordings thanks to a modulation procedure introduced to track physiological variation in the maternal heart rhythm.

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Authors and Affiliations

  1. Department of Information Engineering, Università Politecnica delle Marche, Via Brecce Bianche 12, 60131, Ancona, Italy

    A. Agostinelli, C. Giuliani, S. Fioretti, F. Di Nardo & L. Burattini

Authors
  1. A. Agostinelli
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  2. C. Giuliani
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  3. S. Fioretti
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  4. F. Di Nardo
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  5. L. Burattini
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Corresponding author

Correspondence to L. Burattini .

Editor information

Editors and Affiliations

  1. DIBET, Università Politecnica delle Marche Dipt di Ingegneria Biomed, Elettronica e, Ancona, Italy

    Massimo Conti

  2. Informatics, Reutlingen University, Reutlingen, Germany

    Natividad Martínez Madrid

  3. Computer Science / Ubiquitous Computing Lab, HTWG Konstanz, Konstanz, Germany

    Ralf Seepold

  4. Dipartimento di Ingegneria dell'Informazione, Università Politecnica delle Marche, Ancona, Italy

    Simone Orcioni

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© 2016 Springer International Publishing Switzerland

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Agostinelli, A., Giuliani, C., Fioretti, S., Di Nardo, F., Burattini, L. (2016). A New Segmented-Beat Modulation Algorithm for Maternal ECG Estimation from Abdominal Recordings. In: Conti, M., Martínez Madrid, N., Seepold, R., Orcioni, S. (eds) Mobile Networks for Biometric Data Analysis. Lecture Notes in Electrical Engineering, vol 392. Springer, Cham. https://doi.org/10.1007/978-3-319-39700-9_20

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  • DOI: https://doi.org/10.1007/978-3-319-39700-9_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-39698-9

  • Online ISBN: 978-3-319-39700-9

  • eBook Packages: EngineeringEngineering (R0)

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