Automated detection of hypertension using wavelet transform and nonlinear techniques with ballistocardiogram signals
Article
Rajput, Jaypal Singh, Sharma, Manish, Kumbhani, Divyash and Acharya, U. Rajendra. 2021. "Automated detection of hypertension using wavelet transform and nonlinear techniques with ballistocardiogram signals." Informatics in Medicine Unlocked. 26. https://doi.org/10.1016/j.imu.2021.100736
| Article Title | Automated detection of hypertension using wavelet transform and nonlinear techniques with ballistocardiogram signals |
|---|---|
| ERA Journal ID | 212809 |
| Article Category | Article |
| Authors | Rajput, Jaypal Singh, Sharma, Manish, Kumbhani, Divyash and Acharya, U. Rajendra |
| Journal Title | Informatics in Medicine Unlocked |
| Journal Citation | 26 |
| Article Number | 100736 |
| Number of Pages | 8 |
| Year | 2021 |
| Publisher | Elsevier |
| Place of Publication | United Kingdom |
| ISSN | 2352-9148 |
| Digital Object Identifier (DOI) | https://doi.org/10.1016/j.imu.2021.100736 |
| Web Address (URL) | https://www.sciencedirect.com/science/article/pii/S2352914821002148 |
| Abstract | Arterial hypertension (HT) is a common cardiovascular disease and, if not treated at an early stage, can lead to serious complications. It is difficult to precisely describe because it is a dynamic physiological state. Monitoring of HT is subjective and prone to mistakes. Therefore, various computer-assisted diagnostic methods have been designed. The proposed work is based on ballistocardiography (BCG) signals for the diagnosis of healthy control (HC) and HT subjects. The identification of HT based on BCG signals of 30-s duration using empirical mode decomposition (EMD) and wavelet transform (WT) with nonlinear techniques is proposed in this work. The BCG signal is decomposed into five sub-bands (SBs) using WT and five-level of intrinsic mode functions (IMFs) using EMD. Then, the various non-linear features are calculated for all five-level wavelet decomposition and IMFs. The non-linear features are extracted from the SBs of WT and IMFs of EMD. These features are fed to ensemble gentleboost (EGB) classifier with 10-fold cross-validation strategy for automated detection of HC and HT groups. In this study, the performance of WT and EMD techniques are compared. The proposed HT identification model accomplished the highest average classification accuracy of 89% using WT method. In future, we plan to extend this work using more BCG data. |
| Keywords | BCG signal; Hypertension; Supervised machine learning; Hypertension BCG signal classification; Empirical mode decomposition; Wavelet decomposition (DB8); Signal fractal dimension; Log Energy; Shannon entropy |
| ANZSRC Field of Research 2020 | 400306. Computational physiology |
| Byline Affiliations | Institute of Infrastructure, Technology, Research and Management (IITRAM), India |
| Ngee Ann Polytechnic, Singapore | |
| Asia University, Taiwan | |
| Singapore University of Social Sciences (SUSS), Singapore |
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https://research.usq.edu.au/item/z1vzx/automated-detection-of-hypertension-using-wavelet-transform-and-nonlinear-techniques-with-ballistocardiogram-signals
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