Alaa Kamal Yousif Dafhalla

Bio: Alaa Kamal Yousif Dafhalla is an academic researcher. The author has contributed to research in topics: Smart grid & Heartbeat. The author has co-authored 2 publications.

Deep learning based cardiovascular disease diagnosis system from heartbeat sound

Abstract: During each cardiac cycle of heart, vibrations creates sound and murmur. When these sound and murmur wave is represented graphically then it is called phonocardiogram (PCG). Digital stethoscope is used to record the audio wave signals generated due to heart vibration. Audio waves recorded through digital stethoscope can be used to fetch information like tone, quality, intensity, frequency, heart rate etc. Based on the heart condition, this information will be different for different people and can be used to predict the status of heart at early stage in non-invasive manner. In this research work, by using deep learning models, authors have classified PCG signals into 5 classes namely extra systole, extra heart sound, artifacts, normal heartbeat and murmur. Initially spectrograms in the form of images are extracted from PCG sound and feed into Regularized Convolutional Neural Network. From the simulation environment designed in python, it has found that proposed model has shown the average accuracy of 94% while doing the classification of PCG sound in five classes.

Development of a deep learning system for preventive detection of heart failure due to pulmonary disease

Abstract: In the context of addressing the problem of people who do not undergo a diagnosis of heart failure due to pulmonary conditions on time, a solution to this problem would allow early preventive detection to avoid the development of severe disease efficiently. Our approach employs the use of medical data retrieved from the user to determine and predict whether there is a likelihood of a potential condition. To solve this problem, according to a users medical measurement history, a deep learning model can be implemented to determine a preventive diagnosis or otherwise to follow up on an already detected condition. By posing the problem as a classification task, it can be taken advantage of a deep learning model focused on heart failure or pulmonary conditions to make a preliminary diagnosis and determine if there are signs of any symptomatology.