Abstract
Breastfeeding is a process of nursing a baby with milk straight from the women’s breast. It is suggested that a baby should be breastfed within one hour of birth, exclusively breastfed for the first six months, and then breastfed until age two with age-appropriate, nutritionally sufficient and harmless complementary diets. This paper focuses on the examination of breastfeeding women on their stress level and the brainwave characteristics. The brainwave signals were documented using wireless Electroencephalogram (EEG) equipment via Bluetooth while. The stress level was performed using a questionnaire. The EEG signals were derived using an existing system with the electrodes attached to human scalp. These electrodes measure the electrical signals which are produced by the activities of the neurons in the human brain, such as Delta, Theta, Alpha and Beta waves in the brain to determine the Brainwave Balancing Index (BBI) and also the brain hemispheric dominance which is right or left dominance. The outcome presented balanced BBI of the breastfeeding women after the breastfeeding session, although most of them were high in stress. Right brain dominance was also recorded for the majority of them for both sessions, before and after breastfeeding. Statistical investigation leads to no significant correlations between stress and BBI of breastfeeding women after breastfeeding session, in terms of breastfeeding method and in terms of breastfeeding women’ categories.
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Acknowledgments
This project supported by a Research Acculturation Grant Scheme (RAGS) 600—RMI/RAGS 5/3 (58/2013) funded by the Ministry of Higher Education, Malaysia. The authors also gratefully acknowledge the use of the facilities of the Biomedical Research and Human Potential, Faculty of Electrical Engineering, Universiti Teknologi MARA, Malaysia.
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Hambali, N., Halin, N.I.A., Murat, Z.H., Razak, N.I.A. (2016). Perceived Stress Scale and Brainwave Characteristic of Breastfeeding Women. In: Soh, P., Woo, W., Sulaiman, H., Othman, M., Saat, M. (eds) Advances in Machine Learning and Signal Processing. Lecture Notes in Electrical Engineering, vol 387. Springer, Cham. https://doi.org/10.1007/978-3-319-32213-1_21
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DOI: https://doi.org/10.1007/978-3-319-32213-1_21
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