Open AccessEnhancement of Maternal ECG Using Short-Term Fourier Transform for Foetal Electrocardiogram Extraction
Author(s) -
Prof. M. Senthil Vadivu,
H Saranya,
Vijay Kumar K S
Publication year - 2021
Publication title -
international journal of advanced research in science, communication and technology
Language(s) - English
Resource type - Journals
ISSN - 2581-9429
DOI - 10.48175/ijarsct-2312
Subject(s) - short time fourier transform , signal (programming language) , artificial intelligence , computer science , pattern recognition (psychology) , fourier transform , speech recognition , medicine , fourier analysis , mathematics , mathematical analysis , programming language
The objective of the project is to improve maternal abdomen recording for better prediction of foetal Electrocardiogram (FECG). One of the most difficult tasks in observing foetal well-being is obtaining a clean foetal Electrocardiogram (FECG) using non-invasive abdominal recordings. The foetal graph's low signal quality, on the other hand, makes morphological examination of its wave structure in clinical follow-up difficult. The signal contains precise information that can help doctors to monitor fetal health during pregnancy and labor. The abdominal signal is normalized and separated in the pre-processing stage for wave shape analysis in clinical follow-up. The Kaiser window is used for spectral analysis and segmenting the signal. The two-dimensional (2D) time-frequency representation is obtained by short-time Fourier transform (STFT). The STFT enhances the abdominal recordings of maternal Electrocardiogram (MECG) for efficient separation of foetal electrocardiogram (FECG) to monitor the foetus well-being.