Volume-5 Issue-8


Version
Download 14
Total Views 127
Stock
File Size 4.00 KB
File Type unknown
Create Date July 21, 2018
Last Updated August 28, 2018
Download

Download Abstract Book

S. No

Volume-5 Issue-8, August 2018, ISSN: 2319–6378 (Online)
Published By: Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd.

Page No.

1.

Authors:

Paridhi Goyal, Maya Datt Joshi, Shaktidev Mukherjee

Paper Title:

ECG Signal Analysis for Detecting Cardiac Hypertrophy using MATLAB

Abstract: Cardiac Hypertrophy commonly known as abnormal thickening of the heart muscle. It results from the increase in the cardiomyocyte size and other heart muscle component changes, like extracellular matrix. There can be physiological and pathological causes for the cardiac hypertrophy like amount of strenuous physical activity performed by an athlete and hypertension respectively. Most patients today survive hypertrophy, thanks to a number of efficient treatment options. In ECG, the QRS amplitude and duration plays an important role in determination of Cardiac Hypertrophy. Raw ECG data has been obtained from MIT PTB database and analysed using MATLAB. De-noising of the ECG signal has been done using filters. The clinically important parameters for Cardiac Hypertrophy have been evaluated using Fast Walsh Hadamard Transform. Statistical Analysis is done by comparing the parameters thus obtained with those of normal ECG parameters to gain a deeper understanding of Cardiac Hypertrophy. This research in turn can help the physicians to diagnose the preliminary signs of cardiac hypertrophy which might otherwise go un-noticed.

Keywords: Electrocardiogram (ECG), Cardiac Hypertrophy, Hypertrophy, Statistical Analysis, Cardiac Analysis, QRS amplitude, QRS duration, ECG Monitoring, MATLAB, Fast Walsh Hadamard Transform.

References:

  1. Hall, A. G. (2006): Textbook of Medical Physiology,pp. 103-156. Elsevier Saunders.
  2. Sörnmo, L. a. (2006): Bioelectrical signal processing in cardiac and neurological applications,pp. 411-449. Elsevier Academic Press.
  3. Besma Khelil, A. K. (March 19-22, 2007): P Wave Analysis in ECG Signals using Correlation for Arrhythmias Detection. Fourth International Multi-Conference on Systems, Signals & Devices . Hammamet, Tunisia.
  4. Pipberger H V, M. C. (1990): Methods of ECG interpretation in the AVA program, Methods of Information in Medicine, vol. 29, 337-341.
  5. Zhou, J. (2003): Automatic Detection of Premature Ventricular Contraction Using Quantum Neural Networks. IEEE, pp. 169-173.
  6. Pal, S. (2010): Detection of Premature Ventricular Contraction Beats Using ANN. International Journal of Recent Trends in Engineering and Technology, vol. 3, no. 4.
  7. Anusha F.G, J. S. (2015): Automatic Identification ECG Anomalous Using Xml Data Processing . IJEDR | NC3N 2015 | ISSN: 2321-9939.
  8. Medina Hadjem, O. S.-A. (2014): An ECG Monitoring System For Prediction of Cardiac Anomalies Using WBAN. 2014 IEEE 16th International Conference on e-Health Networking, Applications and Services (Healthcom), pp. 365-370.
  9. Agnes Aruna John, A. P. (2015): Evaluation of cardiac signals using discrete wavelet transform with MATLAB graphical user interface. Indian Heart Journal 67 (2015), pp. 549-551.
  10. Tanmaya, N. P. (2016): Analysis of ECG Signal for Detecting Heart Blocks Using Signal Processing Techniques. International Journal of Innovative Research in Computer and Communication Engineering, pp. 3778-3784.
  11. Çankaya, H. A.-Z. (2015): Heart Rate Monitoring and PQRST Detection Based on Graphical User Interface with Matlab . International Journal of Information and Electronics Engineering, Vol. 5, No. 4, 311-316.
  12. Jyoti Gupta, V. R. (2015): Analyzing Tachycardia, Bradycardia and Transmitting ECG Data Using MATLAB. American International Journal of Research in Science, Technology,Engineering & Mathematics, pp. 207-214.
  13. Apoorva Mahajan, A. B. (2015): Acquisition, Filtering and Analysis of ECG Using MATLAB. International Journal of Advanced Research in Electronics and Communication Engineering (IJARECE), Volume 4, Issue 5, pp. 1170-1173.
  14. Jaylaxmi C Mannurmath, P. R. (2014): MATLAB Based ECG Signal Classification. International Journal of Science, Engineering and Technology Research (IJSETR), Volume 3, Issue 7, pp. 1946-1951.
  15. Jagtap, P. S. (2014): Electrocardiogram (ECG) Signal Analysis and Feature Extraction: A Survey . International Journal of Computer Sciences and Engineering, Volume-2, Issue-5 .
  16. Anand Kumar Joshi, A. T. (2014): A Review Paper on Analysis of Electrocardiograph (ECG) Signal for the Detection of Arrhythmia Abnormalities. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, pp. 12466-12475.
  17. ¨scher, T. F. (2015): Myocardial infarction: mechanisms, diagnosis,and complications. EuropeanHeart Journal 36, pp. 947–949.
  18. C. Saxena, A. S. (1997): Data compression and feature extraction of ECG signals. International Journal of Systems Science,vol. 28, no. 5.
  19. D, M. I. (1992): Analysis of ECG from Pole-zero models. IEEE Trans on BME, vol. 39, no. 7.
  20. Attaway, S. (2006): Matlab: A Practical Introduction to Programming and Problem Solving.
  21. https://www.nottingham.ac.uk/nursing/practice/resources/cardiology/function/conduction.php. (n.d.).
  22. https://lifeinthefastlane.com/ecg-library/basics/qt_interval/. (n.d.).

1-7

http://blueeyesintelligence.org/2checkout_download.html

2.

Authors:

M. Shariz Ansari, Srishti Gupta, Utkarsh, Mayank Agarwal

Paper Title:

Innovative Road Lighting and Smarter System

Abstract: This paper centers around an Intelligent Light Emitting Diode (LED) road lighting framework which offers extensive favorable circumstances over the regular road lighting frameworks. The present framework resembles, the road lights will be exchanged on at night after the sun sets and they are turned off the following day morning consequently which totally dispense with manual task. Encourage the street light's energy is controlled by different time breaks in the midst of night which also makes it most sharp and extras most prominent essentialness. This paper gives the best response for electrical power or imperativeness wastage. In this paper the two sensors are used which are Light Dependent Resistor LDR sensor to demonstrate a day/evening time and the photoelectric sensors to recognize the obstacle all over the place.

Keywords: LDR, Photoelectric Sensor, Arduino Uno, Vitality Sparing and Circuit Configuration, LED. 

References:

  1. A. Devi and A. Kumar, “Design and Implementation of CPLD based Solar Power Saving System for Street Lights and Automatic Traffic Controller”, International Journal of Scientific and Research Publications, Vol. 2, Issue11, November 2012.
  2. A. Wazed, N. Nafis, M. T. Islam and A. S. M. Sayem, “Design and Fabrication of Automatic Street Light Control System”, Engineering e-Transaction, Vol. 5, No. 1, June 2010, pp 27-34.
  3. Priyasree, R. Kauser, E. Vinitha and N. Gangatharan, “Automatic Street Light Intensity Control and Road Safety Module Using Embedded System”, International Conference on Computing and Control Engineering, April2012
  4. S. Sudhakar, A. A. Anil, K. C. Ashok and S. S. Bhaskar, “Automatic Street Light Control System”, International Journal of Emerging Technology and Advanced Engineering, Vol. 3, May 2013, PP. 188-189.
  5. Mohamaddoust, A. T. Haghighat, M. J. M Sharif and N. Capanni, “A Novel Design of an Automatic Lighting Control System for a Wireless Sensor Network with Increased Sensor Lifetime and Reduced Sensor Numbers”, Sensors, Vol.11, PP. 8933-8952.
  6. Popa, C. Cepisca, “Energy Consumption Saving Solutions Based on Intelligent Street Lighting Control System”, U.P.B. Sci. Bull., Vol. 73, April 2011, PP. 297-300.
  7. Mohelnikova, “Electric Energy Savings and Light Guides”, Energy& Environment, 3rd IASME/WSEAS International Conference on, Cambridge, UK, February 2008, pp.470-474.
  8. Y. Rajput, G. Khatav, M. Pujari, P. Yadav, “Intelligent Street Lighting System Using GSM”, International Journal of Engineering Science Invention, Vol2, Issue 3, March 2013, PP. 60-69.

8-12

http://blueeyesintelligence.org/2checkout_download.html

3.

Authors:

Nancy W. Macharia, Hellen M Kinoti, Geoffrey Serede

Paper Title:

Web 2.0 Technologies for Accessing Reproductive Health Information Amongst the Youth in Kenya

Abstract: As young people pass through puberty and adolescence, health needs related to sexual and reproductive health arise. Adolescents and youth have been perceived to have few health needs and little income to access to health services. As a result, they have generally been neglected by the health systems. In Kenya, inadequate dissemination and implementation of existing policies have further hampered the successful implementation of adolescent and youth sexual and reproductive health (AYSRH) programs. If left out, the youth will lack of information may result in behaviors that may affect their future lives. Several attempts have been made by the government and another stakeholder to ensure adequate dissemination of reproductive health information to the youth; however, due to limited resources, the information is still inadequate. With web 2.0 technologies wide adoption by the youth, these technologies can be harnessed to fill the gap. This study sought to find the use of web 2.0 technologies in the provision of the of reproductive health information to the youth. The study found out that Facebook is the most widely used web 2.0 technology followed by WhatsApp. the study also found out that as much as the technologies have a potential in reaching the youth, caution must be exercised not to expose the youth to security breaches while online. 

Keywords: Web 2.0, Social Networking Sites, Reproductive Health, Preference.

References:

  1. Bik, H. M., & Goldstein, M. C. (2013). An introduction to social media for scientists. PLoS biology, 11(4), e1001535.
  2. Boyd, D. M., & Ellison, N. B. (2007). Social network sites: Definition, history, and scholarship. Journal of computer‐mediated Communication, 13(1), 210-230.
  3. Boyer, R., Levine, D., & Zensius, N. (2011). TECHsex USA—youth sexuality and reproductive health in the digital age. Oakland (CA): ISIS.
  4. Buhi, E. R., Klinkenberger, N., McFarlane, M., Kachur, R., Daley, E. M., Baldwin, J., ... & Rietmeijer, C. (2013). Evaluating the Internet as a sexually transmitted disease risk environment for teens: findings from the communication, health, and teens study. Sexually Transmitted Diseases, 40(7), 528-533.
  5. Chu, S. C., & Kim, Y. (2011). Determinants of consumer engagement in electronic word-of-mouth (eWOM) in social networking sites. International journal of Advertising, 30(1), 47-75.
  6. Coyle, C. L., & Vaughn, H. (2008). Social networking: Communication revolution or evolution?. Bell Labs Technical Journal, 13(2), 13-17.
  7. Dooley, J. A., Jones, S. C., & Iverson, D. (2014). Using Web 2.0 for health promotion and social marketing efforts: lessons learned from Web 2.0 experts. Health marketing quarterly, 31(2), 178-196.
  8. Edosomwan, S., Prakasan, S. K., Kouame, D., Watson, J., & Seymour, T. (2011). The history of social media and its impact on business. Journal of Applied Management and entrepreneurship, 16(3), 79-91.
  9. Evers, C., Albury, K., Byron, P., Crowford, K., (2013). Young People, Social Media, Social Network Sites and Sexual Health Communication in Australia: “This is Funny, You Should Watch It”. International Journal of Communication 7 (2013), 263–280
  10. Ezumah, B. (2013). College Students’ Use of Social Media: Site Preferences, Uses and Gratifications Theory Revisited. International Journal of Business and Social Science, 4(5), 28-29.Hendler, J. (2009). Web 3.0 Emerging. Computer, 42(1).
  11. Heldman, A. B, Schindelar J., Weaver J. B. (2013). Social media engagement and public health communication: implications for public health organizations being truly “social”. Public Health Reviews. Vol. 35, No 1: epub ahead of print.
  12. FHI 360/PROGRESS & Ministry of Health (2011). Adolescent and Youth Sexual and Reproductive Health: Taking Stock in Kenya. Nairobi, Kenya, Ministry of Health.
  13. Itimu, K. (2016, November 28). Key Stats About Blogging And Social Media Use From The State Of Internet In Kenya Report. Retrieved March 28, 2017, from http://www.techweez.com/2016/11/28/State-Of-The-Internet-In-Keny a-2016/
  14. Kaplan, A. M., & Haenlein, M. (2010). Users of the world, unite! The challenges and opportunities of Social Media. Business horizons, 53(1), 59-68.
  15. Kaigwa, M., Madung, O., & Costello, S. (2014). NENDO 2014/15 Social Media Trend Report. Available at nendo.co.ke
  16. Knight-McCord, J., Cleary, D., Grant, N., Herron, A., Jumbo, S., (2016). What social media sites do college students use most? Journal of Undergraduate Ethnic Minority Psychology. Spring 2 pp 21- 26.
  17. Kreps, G.L., Neuhauser, L. (2010). New directions in eHealth communication: Opportunities and Challenges, Patient Education and Counseling, 78(3), 329-336.Minority Psychology. Spring 2 pp 21- 26.
  18. Koross, R., & Kosgei, S. (2016). The Role of Social Media on Student Unrests in Kenyan Public Universities. International Journal of Scientific Research and Innovative Technology. Vol. 3(6).
  19. Lenhart, A., & Madden, M. (2007). Social networking websites and teens: An overview.
  20. Lenhart, A., Purcell, L., Smith, A., & Zickuhr, K. (2010). Social media and young adults. Pew Internet and American Life Project. Available at http://www.pewInternet.org/Reports/2010/Social-Media-and-Young-Adults.aspx
  21. Levac & O’Sullivan (2010). Social Media and its Use in Health Promotion. Inter-disciplinary. Journal of Health Sciences, vol. 1 pp 49-57
  22. Livingstone, S., & Brake, D. R. (2010). On the rapid rise of social networking sites: New findings and policy implications. Children and Society, 24(1), 75–83.
  23. Makona, E., Opudo, C., Karechio, E., & Maisori, T. (2008). 2008 National Youth Shadow Report: Progress Made on the 2001 UNGASS Declaration of Commitment on HIV/AIDS. Kenya.
  24. Mayfield, A. (2008). What is social media? iCrossing. Retrieved from www.icrossing.co.uk/.../What_is_Social_Media_iCrossing_ebook.pdf
  25. Morris, M. R., Teevan, J., & Panovich, K. (2010, April). What do people ask their social networks, and why?: a survey study of status message q&a behavior. In Proceedings of the SIGCHI conference on Human factors in computing systems(pp. 1739-1748). ACM.
  26. Mugera, R., N. (2015). Utilization of social media communication in public universities: a case study of Jomo Kenyatta University of Agriculture and Technology. Unpublished thesis. Jomo Kenyatta University of Agriculture and Technology
  27. Newbold, K. B., & Campos, S. (2011). Media and social media in public health messages: A systematic review. Hamilton, ON: McMaster Institue of Environment and Health.
  28. Newman, M. W., Lauterbach, D., Munson, S. A., Resnick, P., & Morris, M. E. (2011, March). It's not that i don't have problems, i'm just not putting them on facebook: challenges and opportunities in using online social networks for health. In Proceedings of the ACM 2011 conference on Computer supported cooperative work (pp. 341-350). ACM.
  29. (2011). Opportunity in Crisis: Preventing HIV from early adolescence to young adulthood. UNICEF.
  30. Reddy, V., P. (2014). The influence of social media on international students’ choice of university and course. Master of Information Technology. Queensland University of Technology.
  31. Taraszow, Aristodemou, Shitta, Laouris, & Arsoy (2010). Disclosure of personal and contact information by young people in social networking sites: an analysis using Facebook profiles as an example. International Journal of Media and Cultural Politics, 6 (1), 81-101.
  32. Trautner, N. (2013). Promoting Health on Social Media; A Case Study. Bachelor Thesis. Aarhus University
  33. Vickery, G., & Wunsch-Vincent, S. (2007). Participative web and user-created content: Web 2.0 wikis and social networking. Organization for Economic Cooperation and Development (OECD).
  34. Wong, Merchant & Moreno, (2015). Using social media to engage adolescents and young adults with their health. Healthc, vol. 2(4): pp 220–224
  35. Zhang, Y. (2012). College students' uses and perceptions of social networking sites for health and wellness information. Information Research, 17(3), paper 523. Retrieved from http://InformationR.net/ir/17-3/paper523.html

13-19

http://blueeyesintelligence.org/2checkout_download.html