Lullaby: A Novel Algorithm to Extract Fetal QRS in Real Time Using Periodic Trend Feature

Jilani, Daniel; Le, Tai; Etchells, Tim; Lau, Michael; Cao, Hung

doi:10.1109/lsens.2022.3200072

Cited by 2 publications

(1 citation statement)

References 11 publications

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“…Compared with other recent works in the field [32][33][34][35][36] , the presented approach is much more reliable and accurate. On the one hand, the process is based on very subtle mathematics tools that can be developed numerically even for twins and are easily implemented in real-time using embedded systems such as DSP or Raspberry.…”

Section: Discussionmentioning

confidence: 80%

Extraction of Fetal Electrocardiogram by Combining Deep Learning and SVD-ICA-NMF Methods

Ziani

Farhaoui

Moutaib

2023

Big Data Min. Anal.

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This paper deals with detecting fetal electrocardiogram FECG signals from single-channel abdominal lead.It is based on the Convolutional Neural Network (CNN) combined with advanced mathematical methods, such as Independent Component Analysis (ICA), Singular Value Decomposition (SVD), and a dimension-reduction technique like Nonnegative Matrix Factorization (NMF). Due to the highly disproportionate frequency of the fetus's heart rate compared to the mother's, the time-scale representation clearly distinguishes the fetal electrical activity in terms of energy. Furthermore, we can disentangle the various components of fetal ECG, which serve as inputs to the CNN model to optimize the actual FECG signal, denoted by FECGr, which is recovered using the SVD-ICA process. The findings demonstrate the efficiency of this innovative approach, which may be deployed in real-time.

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