ICACT 2019
Permanent URI for this collectionhttp://repository.kln.ac.lk/handle/123456789/20316
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Item Automatic Motion Artefacts Recognition in Resting ECG/EKG to Identify Failed Tests using Machine Learning(4th International Conference on Advances in Computing and Technology (ICACT ‒ 2019), Faculty of Computing and Technology, University of Kelaniya, Sri Lanka, 2019) Nanayakkara, S.A.; Meegama, R.G.N.Although an ECG is able to identify certain heart diseases, an uninterrupted and a clear signal is essential to accurately diagnose any abnormalities in the heart functions. Obtaining such a crisp ECG is a challenging task due to several artifacts such as motions because muscle movements are inevitable even in resting ECGs due to medical conditions such as anxiety, Parkinson’s disease and body tremors. In addition, skin stretching too, produces electricity that disturbs the potentials involved in an ECG. There are numerous experiments have been conducted to find effective and efficient motion artifact removal methods from ECGs. In this study, we use cleaned and disturbed ECGs to implement more effective and efficient method to remove motion artifacts and evaluation mechanism for ECGs. The first stage of the proposed technique involved gathering more than 500 ECGs having 12 leads data from public sources available on PhysioNet online database. These data contained cleaned ECGs and disturbed ECGs of healthy and unhealthy patients. The data set is cleaned to remove noise and undesirable effects such as baseline wander. A technique based on multi-resolution thresholding is used to recognize and remove motion artifacts and further, the Savitzky-Golay filter is used to reduce the mean squared error of this process. In the second stage, a convolution neural network (CNN) is implemented on the cleaned ECG dataset. Initially, datasets of 12 leads are shuffled under two categories: with and without noise. These shuffled images, numbering more than 36,000, are then categorized for training, validation and testing of data with and without motion artifacts. Results indicate a 98.7% accuracy in predicting whether a given ECG can be used or not by examining more than 500 cleaned ECGs.Item 3D Visualization of Human EEG Signals(4th International Conference on Advances in Computing and Technology (ICACT ‒ 2019), Faculty of Computing and Technology, University of Kelaniya, Sri Lanka, 2019) Pradeep, H.B.A.C.; Meegama, R.G.N.; Kalinga, S.The brain is the most important and the most complex human organ that is responsible for all the functions that we do in our routine life. Moreover, the brain consists of millions of neurons that utilize electro-chemical signals to transmit information to other parts of the body. Whenever a neuron triggers an electrical impulse to another neuron, it generates electricity, referred to as an EEG wave that can be measured by a sensitive device. Using such brain patterns, it is possible to identify normal day-to-day human behavior. The brain commences its work before birth and works continuously until death during which brain waves are constantly generated according to what we perceive from the environment. By analyzing brain wave patterns, we can predict and identify valuable information on human or animal health. For examples we can monitor coma and brain death in human or animals, various effects of drugs on sleep disorder, day-to-day life human behavior, post-traumatic stress disorders (PTSD), etc. In the experiments conducted, we took the potential differences between the respective channels to identify the variations in brain wave data among the individuals. We used linear interpolation to generate 3D views of the potential data between the locations where the electrodes were placed. A color code is then applied to indicate the range of potential values projected on the human skull. High frequency components were observed near the right parietal and right occipital lobes of the brain. Significant variations were not observed near the frontal or the left region of the brain for a specific activity. The proposed project will introduce a technique to visualize human brain waves in 3D over the skull that will enable us to interpret how these brain waves are associated with various regions on the human brain.