Rolant Gini J. currently serves as Assistant Professor at Department of Electronics and Communication Engineering, School of Engineering, Coimbatore Campus. Her areas of research include Signal Processing in VLSI.


Publication Type: Conference Paper

Year of Publication Publication Type Title


Conference Paper

R. J. Gini, Ramachandran, K. I., and Ceerthibala, U. K., “Approach to extract twin fECG for different cardiac conditions during prenatal”, in IFMBE Proceedings, 2017, vol. 61, pp. 104-108.[Abstract]

During multiple fetus pregnancy, degree of risk for distinguishing the information of mother and fetus health condition is high. A proper distinguishable ECG of each fetus and mother gives information about the health conditions of individuals. In case of multiple fetal conditions, the heartbeat of the fetuses will be almost at the same rate. This algorithm has been aimed to separate mECG and the fECGs of the individual fetus. First, the signal for different medical conditions like Fibrillation, Apnea, Ventricular Ectopy, Singleton and Normal has been considered. The synthetic abdECG signal for the above mentioned cases has been formulated by preprocessing and considered as the input signal. RPeak of mECG in the abdECG signal has been located using First Order Gaussian Differentiator and Zero Crossing Detector. QRS complex has been considered around the identified R-Peak of abdECG. Identified QRS has been removed from the abdECG signal to obtain fECG with residual noise. The QRS complexes of fECG are detected the same way as mECG QRS were detected, and is represented as binary signals. The separation of the fetal ECG is done based on the individual presence of the fetus in the signals using Inter-beat averaging and Inter-beat standard deviation of the binary signal. The algorithm has been tested for above mentioned cardiac conditions during prenatal. The algorithm has been able to achieve 99% accuracy for particular cardiac condition with overall system accuracy of 80.4%. The standard cardiac signals of different cases have been sourced from Physionet database to construct the abdECG. © Springer Nature Singapore Pte Ltd. 2017.

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Conference Paper

R. H. Nair, J. Gini, R., and Dr. K. I. Ramachandran, “A simplified approach to identify the fetal ECG from abdECG and to measure the fHR”, in IFMBE Proceedings, 2015, vol. 52, pp. 23-26.[Abstract]

Fetal ECG (fECG) recording aids physicians to diagnose congenital disorders and other anomalies like asphyxia at the early stages of pregnancy. The fECG extraction has been an area of intensive research. Despite the existence of sophisticated and detailed algorithms – based on adaptive filters, independent component analysis (ICA), &c – filtering out the fECG, buried in the noise and mixed up with the maternal ECG (mECG) remains a challenging task. Some residues of mECG are always present in the fECG extracted with all such techniques. A simple algorithm has been developed here to identify the local maxima in the pre-processed abdominal ECG (abdECG) through thresholding; it locates the mECG peaks explicitly. At the outset, the abdECG has been refined by removing the baseline wander and power line interference at a pre-processing stage. With these as pivots the mECG component is eliminated and the fECG of good quality culled out. The fetal heart rate (fHR) and information required to know the condition of fetal heart can be extracted from this fECG effectively. Extraction of these information helps reducing the rate of fetal mortality, and improving the health condition of fetus as well as mother. Performance of the method is better than the conventional adaptive filtering method and the same is proven quantitatively. A processor based realization of the scheme adds to its credibility substantially to ensure its usability in practice. © Springer International Publishing Switzerland 2015.

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Faculty Research Interest: 
NIRF 2017