Publication Type:

Conference Proceedings

Source:

Advances in Signal Processing and Intelligent Recognition Systems, Springer International Publishing, Volume 678, Cham, p.3-14 (2018)

ISBN:

9783319679341

URL:

https://link.springer.com/chapter/10.1007/978-3-319-67934-1_1

Abstract:

Electrocardiogram (ECG) signals are one of the most important diagnostic tools for any doctor, especially a cardiologist. It is important that the fetus present inside the abdomen undergoes a fetal ECG recording to assess the health of the fetus. Complications like disturbance because of movement of abdominal muscles are usually present during the recording and leads to the wrong diagnosis of the fetus ECG. In this paper, the signal in dispute had been altered in the proposed method so as to eliminate the wandering of the baseline, respiration noise and also expel the noise from other sources. The acquired abdominal ECG signal in a noninvasive manner had been considered for extracting the fetal ECG after eliminating the noise. The windowed zero mean method is used where the first step is segmentation. In segmentation, the abdominal ECG signal is divided into set of samples based on window size. Zero mean is applied across each of the windowed abdominal ECG signals to address the issue of baseline wandering and respiration noise. This is followed by the application of a bandpass filter to cancel the high-frequency noise component. This process results in an ECG signal that almost has no complications as present before. The fetal ECG signal that is procured using such a method is now easier to diagnose as compared to the acquired signal which contains noise. Thus, for a fetus, this can help in proper diagnosis. It is further noted that this method is very reliant on using and is lucid. It can be used to augment and alter signals where such complications arise in the field of medicine and clinical diagnosis

Cite this Research Publication

J. Joseph, J. Gini, R., and Dr. K. I. Ramachandran, “Removal of BW and Respiration Noise in abdECG for fECG Extraction”, Advances in Signal Processing and Intelligent Recognition Systems, vol. 678. Springer International Publishing, Cham, pp. 3-14, 2018.