Merging technology with human intelligence is creating an age of Artificial Intelligence(AI). I want to contribute to this age of evolution. Thus, a Master’s degree in Computer Science with emphasis on biomedical signal analysis, machine learning and AI is my passion. The potential topic I want to work on is “machine learning based algorithms for electrocardiogram (ECG) arrhythmia detection.”


Heart arrhythmia, is a group of conditions in which the heartbeat is irregular. Arrhythmias is a condition where the electrical signals to the heart responsible for  managing heartbeats do not work. It affects millions of people around the globe. In 2013, Atrial fibrillation and flutter caused 112,000 deaths. Around 80% of cardiac deaths are due to ventricular arrhythmias. Some of the techniques being used for treatment are Cardioversion, where the doctor uses an electric shock to reset the heart to its regular rhythm and ICD (implantable cardioverter-defibrillator), where a device is implanted near the collarbone to detect an abnormally fast rhythm. Temporary cardiac pacing may be necessary for reversible causes of very slow heartbeats

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An electrocardiogram (ECG) measures the electric activity of the heart. It is possible to detect some of abnormalities by analyzing and electrical signals of heartbeat.  Computer-based cardiac arrhythmia detection can play a significant role in detection of cardiac disorders. Independent Component Analysis (ICA) and further processing of the data can help in classifying the heart beats and the disease diagnosis on time. The experimental research methodology will include Data Acquisition, Signal Preprocessing, segmentation techniques, Features Extraction and Machine Learning algorithms to get results.

I’m extremely interested to work in this area as it will be very similar to my Final Year Project at NUST i.e. heartbeat monitoring, signal processing and information extraction. A detailed thesis at Master’s level will help me to gain a wider-range interdisciplinary knowledge in health technology and pursue a strong research career.