Automated diagnostic tool for hypertension using convolutional neural network

Background: Hypertension (HPT) occurs when there is increase in blood pressure (BP) within the arteries, causing the heart to pump harder against a higher afterload to deliver oxygenated blood to other parts of the body. Purpose: Due to fluctuation in BP, 24-h ambulatory blood pressure monitoring has emerged as a useful tool for diagnosing HPT but is limited by its inconvenience. So, an automatic diagnostic tool using electrocardiogram (ECG) signals is used in this study to detect HPT automatically. Method: The pre-processed signals are fed to a convolutional neural network model. The model learns and identifies unique ECG signatures for classification of normal and hypertension ECG signals. The proposed model is evaluated by the 10-fold and leave one out patient based validation techniques. Results: A high classification accuracy of 99.99% is achieved for both validation techniques. This is one of the first few studies to have employed deep learning algorithm coupled with ECG signals for the detection of HPT. Our results imply that the developed tool is useful in a hospital setting as an automated diagnostic tool, enabling the effortless detection of HPT using ECG signals.