Bangladesh Journal of Medical Physics

A Study on Shield Design Aspects of a Semi-Industrial Co-60 Gamma Irradiation Facility at Gamma Source Division of Institute of Food and Radiation Biology of the Bangladesh Atomic Energy Commission

S Ahmed, MJH Khan, A Nahar, MF Mortuza, MI Hosan — Sun, 25 Dec 2022 00:00:00 +0000

The aim of this work is to investigate the existing shield design aspects of a semi-industrial Cobalt- 60 Gamma Irradiation Facility for 100 kCi, designed for irradiation of pharmaceutical products and daily foodstuffs as well as for research and development purposes at the Institute of Food and Radiation Biology (IFRB) of the Bangladesh Atomic Energy Commission. Earlier, the initial activity of this facility was 65 kCi and stone based reinforced concrete (SBRC) of density 2.60 g/cc and specific thicknesses were used to shield the facility for safety to operational personnel and outside public. Due to a large demand for irradiation of pharmaceutical products as well as of daily foodstuffs, IFRB has decided to increase the initial activity up to 100 kCi for this facility. For this reason, the shielding design aspects of this facility were needed to be recalculated and checked for safety. This analysis was done using the Point-kernel Shielding Code Micro-Shield 5.05 and public domain Point-kernel Shielding code QAD-CGGP2 respectively considering the radiation worker dose limit of 10 μSv/hr of the International Commission on Radiological Protection, ICRP-60, 1990. The corresponding dose limit for public at large is 0.5 μSv/hr. The calculated dose rates at different locations around the facility are far below 10 μSv/hr except in one area. This location is inside the source room. No one is allowed to enter the room when the facility is in the ON mode. However, except one location, positions outside the facility have dose rates about 3 time higher than 0.5 μSv/hr, the dose limit for public at large. Therefore, this analysis shows that the existing shield design aspects of IFRB’s Co-60 gamma source room is safe for a 100 kCi source from the viewpoint of radiation safety to operating personnel but not for public at large within proximity of the facility.

Bangladesh Journal of Medical Physics Vol.15 No.1 2022 P 1-7

Detection of Schizophrenia from EEG Signals using Dual Tree Complex Wavelet Transform and Machine Learning Algorithms

Sun, 25 Dec 2022 00:00:00 +0000

This research was conducted with the aim to detect schizophrenia automatically from EEG signals using machine learning algorithms. The 16 electrode EEG data were collected from the online repository where 43 schizophrenic and 39 healthy persons’ dataset is available. By applying Low Pass Filter and Total Variation Denoising method, raw EEG signals were denoised and were decomposed into beta, alpha, theta and delta waves by using Dual Tree Complex Wavelet Transform. To apply machine learning algorithms, five features: mean, median, standard deviation, energy and kurtosis were considered for all the four wave bands. With Linear Support Vector Machine and Random Forest classifier machine learning algorithms, 12 out of 16 channels were classified with test accuracy above 95% and F1 score above 90%. Among them, 7 channels were predicted with 100% test accuracy. This research thus has the potential to detect schizophrenia unsupervised and within a noticeably short period of time giving the opportunity to real time monitoring of patients. Hence, people living in remote areas or deprived of adequate healthcare professionals can be benefitted through the outcome of this research.

Bangladesh Journal of Medical Physics Vol.15 No.1 2022 P 8-27

Detection of Epileptic Seizures from EEG Signals Using Machine Learning Classifiers

Avijit Dey Joy, Sraboni Sarkar, Abul Kalam Azad — Sun, 25 Dec 2022 00:00:00 +0000

Epileptic seizure is a chronic neurological disorder which affects millions of people all over the globe. It can be treated in a better way if the symptoms are detected at an early stage. In this study, we have demonstrated and evaluated the classification performances of different machine learning classifiers for the detection of epileptic seizures from electroencephalography (EEG) signals. For this, we have first applied principal component analysis (PCA) on EEG signals to obtain much reduced-length PCA vectors. These vectors are then applied to decision tree (DT), k-nearest neighbor (KNN), Naïve Bayes (NB), support vector machine (SVM) and artificial neural network (ANN) classifiers for the detection of epileptic seizures. The effects of length of PCA vectors on the performances of these classifiers have also been analyzed rigorously for 2-class, 3-class and 5-class classification of EEG signals. Besides such PCA-based classifiers, we have also proposed and evaluated the performances of a customized convolutional neural network (CNN) to directly extract features from the EEG signals as well as to perform classification tasks. The results showed that CNN outperforms PCA-based machine learning classifiers. For 2-class classification cases, CNN attains classification accuracies in the range from 99.50% to 100%, whereas 98.48% and 96.32% accuracies are obtained with CNN for 3-class and 5-class classification cases. The results signify that the proposed CNN classifier can be considered as a highly-efficient scheme for the reliable detection of epileptic seizures from EEG signals.

Bangladesh Journal of Medical Physics Vol.15 No.1 2022 P 28-42

Improving Deep Sensitivity of Four-Electrode Focused Impedance Method In Lungs by Varying Electrode Geometries

Mahjabin Mobarak, K Siddique e Rabbani — Sun, 25 Dec 2022 00:00:00 +0000

Bio-Electrical impedance is of special interest in the detection and diagnosis of lung problems, particularly in the low and medium income countries. An age old simple technique employing four electrodes is known as ‘Tetra Polar Impedance Measurement (TPIM)’ but it cannot localize a particular zone of interest region. A new technique named as ‘Focused Impedance Method (FIM)’ was innovated by a Dhaka University group which gives high sensitivity in a localized zone of interest. Previously FIM was used from one side of the thorax which gave a rather limited information from the lungs, from shallow depths only. In order to get information from deeper regions of the lungs a new configuration of electrodes for FIM was proposed by the same group at Dhaka University which placed two electrodes at the front and two electrodes at the back of the thorax in a horizontal plane. It is expected that the degree of depth sensitivity would depend on electrode separation on both the sides. The electrode width may also have an effect. In order to study these quantitatively, COMSOL Multiphysics software was used to simulate the measurements in a rounded rectangular volume to represent a typical thorax, which was filled with isotonic saline. Electrode separations of 5cm, 10cm, 15cm and 20cm were studied while electrode widths studied were 0.15cm, 1cm and 3cm. The work supported the proposed new configuration of electrodes for FIM in that this method gives enhanced sensitivity throughout the depths of a lung and that for a thorax with a cross section of 33cm26cm, an electrode separation between 10cm and 15cm would give optimum results. For electrode width, the ones studied did not give any significant difference, however, the smallest (0.15cm) one appeared to give slightly better results.

Bangladesh Journal of Medical Physics Vol.15 No.1 2022 P 43-54