Click here to flash read.
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.
No creative common's license