Volume 3 Number 12 December 2017

Effective Dose of Computed Tomography (CT) Chest and Abdomen-Pelvis in Some Selected Hospitals in Federal Capital Territory, Abuja, Nigeria

Authors: E. C. Nwokorie ; S. A. Jonah ; M. Y. Onimisi
Pages: 117-123
The Computed Tomography (CT) dose output of some selected hospitals in the Federal capital Territory, Abuja, Nigeria have been determined by calculating the Effective doses of CT Chest and Abdomen-Pelvis of selected hospitals and compared its average with the Mean Reference Dose of CT Chest and Abdomen-Pelvis from four hospitals in the Federal Capital Territory, Abuja, Nigeria. Effective Dose and Scan type were extracted from the CT Chest and Abdomen-Pelvis examinations recorded. The Effective Dose of each patient undergoing the Chest and Abdomen-Pelvis examinations were calculated using the coefficient factor and the DLP values. Patients’ CT dose data from the ages of 18 to 60years from each of the 4 centres for each study type from January, 2013 to December, 2014 was extracted.  A total of 112 patients’ CT dose data was extracted. Chest CT Effective Dose ranged from 9.0 to 34.0mSv, while Abdomen-Pelvis CT Effective Dose ranged from 15.9 to 61.0 for all the Centres in Federal Capital Territory, Abuja. This is higher than the recommended Reference Effective Dose range for CT Chest which is from 5 – 7mSv. and for CT Abdomen-Pelvis is from 8 – 14mSv. The mean effective dose from the Chest CT is 21.8mSv and from the Abdomen-Pelvis is 31.9mSv.

Analysis of Heart Rate Variability Via Health Care Platform

Authors: Yong-Hong Hsu ; Yang-Yi Chen ; Chun-Liang Lin ; Changchen Zhao
Pages: 103-116
This research task develops a mobile healthcare analysis system (PHAS) which combines both easy ECG signal measurement and reliable analysis of heart rate variability for home care purpose. The PHAS is composed by a health care platform (HCP) and a data system analysis (DSA) module. The HCP consists of a self-developed two pole electrocardiography (ECG) measuring device and the DSA a data processing unit for detection and analysis of heart rate variability. For the DSA module, the adaptive R Peak detection algorithm is proposed to reliably detect the R peak of ECG for HRV analysis. A number of features are extracted from ECG signals. A data mining method is employed for HRV analysis to exploit the correlation between HRV and these features. Experiments are conducted by establishing a database of ECG signals measured from 29 subjects under rest and exercise condition. The results show the PHAS’s significant potential in mobile applications of personal daily health care.