Predicting Pulmonary Hypertension by Electrocardiograms Using Machine Learning

• URL: http://arxiv.org/abs/2304.12447v1
• Date: Mon, 24 Apr 2023 21:00:16 GMT
• Title: Predicting Pulmonary Hypertension by Electrocardiograms Using Machine Learning
• Authors: Eashan Kosaraju, Praveen Kumar Pandian Shanmuganathan
• Abstract summary: Pulmonary hypertension (PH) is a condition of high blood pressure that affects the arteries in the lungs and the right side of the heart. The goal of this project is to create a neural network model which can process an ECG signal and detect the presence of PH with a confidence probability.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Pulmonary hypertension (PH) is a condition of high blood pressure that affects the arteries in the lungs and the right side of the heart (Mayo Clinic, 2017). A mean pulmonary artery pressure greater than 25 mmHg is defined as Pulmonary hypertension. The estimated 5-year survival rate from the time of diagnosis of pulmonary hypertension is only 57% without therapy and patients with right heart failure only survive for approximately 1 year without treatment (Benza et al., 2012). Given the indolent nature of the disease, early detection of PH remains a challenge leading to delays in therapy. Echocardiography is currently used as a screening tool for diagnosing PH. However, electrocardiography (ECG), a more accessible, simple to use, and cost-effective tool compared to echocardiography, is less studied and explored for screening at-risk patients for PH. The goal of this project is to create a neural network model which can process an ECG signal and detect the presence of PH with a confidence probability. I created a dense neural network (DNN) model that has an accuracy of 98% over the available training sample. For future steps, the current model will be updated with a model suited for time-series data. To balance the dataset with proper training samples, I will generate additional data using data augmentation techniques. Through early and accurate detection of conditions such as PH, we widen the spectrum of innovation in detecting chronic life-threatening health conditions and reduce associated mortality and morbidity.

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