Passive detection of accelerometer-recorded fetal movements using a time-frequency signal processing approach

Boualem Boashash, Mohamed Salah Khlif, Taoufik Ben-Jabeur, Christine East, Paul B Colditz

Research output: Contribution to journalArticleResearchpeer-review

30 Citations (Scopus)


This paper describes a multi-sensor fetal movement (FetMov) detection system based on a time-frequency (TF) signal processing approach. Fetal motor activity is clinically useful as a core aspect of fetal screening for well-being to reduce the current high incidence of fetal deaths in the world. FetMov are present in early gestation but become more complex and sustained as the fetus progresses through gestation. A decrease in FetMov is an important element to consider for the detection of fetal compromise. Current methods of FetMov detection include maternal perception, which is known to be inaccurate, and ultrasound imaging which is intrusive and costly. An alternative passive method for the detection of FetMov uses solid-state accelerometers, which are safe and inexpensive. This paper describes a digital signal processing (DSP) based experimental approach to the detection of FetMov from recorded accelerometer signals. The paper provides an overview of the significant measurement and signal processing challenges, followed by an approach that uses quadratic time-frequency distributions (TFDs) to appropriately deal with the non-stationary nature of the signals. The paper then describes a proof-of-concept with a solution consisting of a detection method that includes (1) a new experimental set-up, (2) an improved data acquisition procedure, and (3) a TF approach for the detection of FetMov including TF matching pursuit (TFMP) decomposition and TF matched filter (TFMF) based on high-resolution quadratic TFDs. Detailed suggestions for further refinement are provided with preliminary results to establish feasibility, and considerations for application to clinical practice are reviewed.
Original languageEnglish
Pages (from-to)134 - 155
Number of pages22
JournalDigital Signal Processing
Issue number1
Publication statusPublished - 2014

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