Measurement of Brain Activity Rate Based on Electroensefalographical Signals on Smokers

  • Herry Setiawan Politeknik Aceh Selatan

Abstract


Indonesia as a country with 255,182,144 people (Central Bureau of Statistics, 2015) with the number of smokers reached 46.16 percent in the third rank with the largest number of smokers in the world after China and India, there are some elements in cigarettes one of them is nicotine, it will be one of the addictive additives. It's why smokers want to continue smoking cigarettes on a regular basis. It bounds to the brain receptors and in other organs.  Increased activity in the orbitofrontal area of the cortex occurs when a smoker wants a cigarette, while in the prefrontal cortex experiences an increase in activity when smokers smoke cigarettes. Increased activity in the area will produce electrically along the scalp that can be measured using Electroencephalography (EEG). The voltage difference of the ion current will give the addiction level information to the cigarette so it can be classified. Classification is done in 4 classes namely low dependence, Moderate dependence and high dependence. From the results of the study we found that the level of brain activity of the frontal cortex increased. 52% of the 40 sampled data showed the highest increase in activity by reaching 53.17% in the highly dependent addiction category.


References

[1] E. S. Yuni, “Identifikasi nikotin dari daun tembakau (nicotiana tabacum) kering dan uji efektivitas ekstrak daun tembakau sebagai insektisida penggerek batang padi (scirpophaga innonata),” Fakultas matematika dan ilmu pengetahuan alam universitas negeri semarang, 2006.

[2] anonim, “Farmakologi nikotin dan prinsip adiksi,” no. Universitas Gajahmada, Yogyakarta: Universitas Gajahmada, 2015, p. 23.

[3] Badan Pusat Statistik, Result of the 2015 Intercensal Population Census. 2015.

[4] Pusat Data dan Informasi Kementerian Kesehatan RI, “InfoDATIN : Hari Tanpa Tembakau Sedunia,” Kemenkes, p. 12, 2013.

[5] T. Lewis, “Brain Circuitry Behind Cigarette Cravings Revealed,” livescience.com, 2013.

[6] W. Rosenberg, T. Chanwimalueang, V. Goverdovsky, and D. Sharp, “Smart Helmet : Wearable Multichannel ECG and EEG,” IEEE J. Transiational Eng. Heal. Med., vol. 4, no. February, 2016.

[7] H. Setiawan and E. M. Yuniarno, “Features extraction of palm vein image using phase symmetry,” Proc. - 2015 4th Int. Conf. Instrumentation, Commun. Inf. Technol. Biomed. Eng. ICICI-BME 2015, pp. 59–64, 2016.

[8] H. Setiawan, “Biometric Recognition based on Palm Vein Image Using Learning Vector Quntization,” pp. 5–9.

[9] H. Setiawan and N. Saputri, “Biometric Recognition Based on Acoustic Features,” in Seminar Nasional Teknologi Rekayasa (SNTR) III Tahun 2016, 2016, pp. 126–129.

[10] S. Kassim, S. Kassim, M. Salam, and R. Croucher, “Validity and Reliability of the Fagerstrom Test for Cigarette Dependence in a Sample of Arabic Speaking UK-Resident Yemeni ... Validity and Reliability of the Fagerstrom Test for Cigarette Dependence in a Sample of Arabic Speaking UK-Resident Yemeni Khat ,” vol. 13, no. April 2012, pp. 2010–2013, 2014.

[11] I. B. Artana and I. N. Rai, “Tingkat Ketergantungan Nikotin Dan Faktor-Faktor Yang Berhubungan pada Perokok di Desa Penglipuran 2009,” IGN Bagus Artana, IB Ngurah Rai, vol. 11, no. 1–9, 2010.

[12] N. Benowitz, “Nicotine Addiction,” N. Engl. J. Med., vol. 362, no. 24, pp. 2295–2303, 2010.

[13] FAGERSTRÖM K:, “Determinants of tobacco use and renaming the FTND to the Fagerström Test for Cigarette Dependence,” Nicotine Tob Res 14 75-78, 2012.

[14] W. (Eds. . James, Larry, O’Donohue, The Primary Care Toolkit Practical Resources for the Integrated Behavioral Care Provider, 1st ed. New York: Springer-Verlag, 2009.

[15] US Department of Health and Human Services, “Healthy People 2010: Understanding and Improving Health,” Heal. San Fr., vol. 2nd., p. 62 p., 2000.

[16] E. A. Larsen and A. I. Wang, “Classification of EEG Signals in a Brain- Computer Interface System,” no. June, 2011.

[17] S. Neena, “The Brain’s Cerebral Cortex (Neocortex),” 2011.
Published
2018-06-30
How to Cite
SETIAWAN, Herry. Measurement of Brain Activity Rate Based on Electroensefalographical Signals on Smokers. Jurnal Inotera, [S.l.], v. 3, n. 1, p. 15-22, june 2018. ISSN 2581-1274. Available at: <http://inotera.poltas.ac.id/index.php/inotera/article/view/40>. Date accessed: 19 nov. 2018. doi: https://doi.org/10.31572/inotera.Vol3.Iss1.2018.ID40.