Comparison of Different Segmentations in Automated Detection of Hypertension Using Electrocardiography with Empirical Mode Decomposition

Hypertension (HPT) refers to a condition where the pressure exerted on the walls of arteries by blood pumped from the heart to the body reaches levels that can lead to various ailments. Annually, a significant number of lives are lost globally due to diseases linked to HPT. Therefore, the early and accurate diagnosis of HPT is of utmost importance. This study aimed to automatically and with minimal error detect patients suffering from HPT by utilizing electrocardiogram (ECG) signals. The research involved the collection of ECG signals from two distinct groups. These groups consisted of ECG data of both five thousand and ten thousand data points in length, respectively. The performance in HPT detection was evaluated using entropy measurements derived from the 5-layer Intrinsic Mode Function(IMF) signals through the application of the Empirical Mode Decomposition method. The resulting performances were compared based on the nine features extracted from each IMF. To summarize, employing the 5-fold cross-validation technique, the most exceptional accuracy rates achieved were 99.9991% and 99.9989% for ECG data of lengths five thousand and ten thousand,respectively, using decision tree algorithms. These remarkable performance results indicate the potential usefulness of this method in assisting medical professionals to identify individuals with HPT.