Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/154194
Title: A computational intelligence tool for the detection of hypertension using empirical mode decomposition
Authors: Soh, Desmond Chuang Kiat
Ng, Eddie Yin Kwee
Jahmunah, V.
Oh, Shu Lih
San, Tan Ru
Acharya, U. Rajendra
Keywords: Engineering::Mechanical engineering
Issue Date: 2020
Source: Soh, D. C. K., Ng, E. Y. K., Jahmunah, V., Oh, S. L., San, T. R. & Acharya, U. R. (2020). A computational intelligence tool for the detection of hypertension using empirical mode decomposition. Computers in Biology and Medicine, 118, 103630-. https://dx.doi.org/10.1016/j.compbiomed.2020.103630
Journal: Computers in Biology and Medicine
Abstract: Hypertension (HPT), also known as high blood pressure, is a precursor to heart, brain or kidney diseases. Some symptoms of HPT include headaches, dizziness and fainting. The potential diagnosis of masked hypertension is of specific interest in this study. In masked hypertension (MHPT), the instantaneous blood pressure appears normal, but the 24-h ambulatory blood pressure is abnormal. Hence patients with MHPT are difficult to identify and thus remain untreated or are treated insufficiently. Hence, a computational intelligence tool (CIT) using electrocardiograms (ECG) signals for HPT and possible MHPT detection is proposed in this work. Empirical mode decomposition (EMD) is employed to decompose the pre-processed signals up to five levels. Nonlinear features are extracted from the five intrinsic mode functions (IMFs) thereafter. Student's t-test is subsequently applied to select a set of highly discriminatory features. This feature set is then input to various classifiers, in which, the best accuracy of 97.70% is yielded by the k-nearest neighbor (k-NN) classifier. The developed tool is evaluated by the 10-fold cross validation technique. Our findings suggest that the developed system is useful for diagnostic computational intelligence tool in hospital settings, and that it enables the automatic classification of HPT versus normal ECG signals.
URI: https://hdl.handle.net/10356/154194
ISSN: 0010-4825
DOI: 10.1016/j.compbiomed.2020.103630
Rights: © 2020 Elsevier Ltd. All rights reserved
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:MAE Journal Articles

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