Next Article in Journal
A Perspective of Non-Fiber-Optical Metamaterial and Piezoelectric Material Sensing in Automated Structural Health Monitoring
Next Article in Special Issue
Exploring the Role of Wearable Technology in Sport Kinematics and Kinetics: A Systematic Review
Previous Article in Journal
Container Migration in the Fog: A Performance Evaluation
Previous Article in Special Issue
ECG Noise Cancellation Based on Grey Spectral Noise Estimation
Open AccessArticle

Unobtrusive Mattress-Based Identification of Hypertension by Integrating Classification and Association Rule Mining

1
School of Computer Science, Northwestern Polytechnical University, Xi’an 710072, Shaanxi, China
2
College of Engineering and Science, Victoria University, Melbourne, VIC 3011, Australia
3
Department of Computer Science and Information Technology, La Trobe University, Melbourne, VIC 3086, Australia
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(7), 1489; https://doi.org/10.3390/s19071489
Received: 16 January 2019 / Revised: 13 March 2019 / Accepted: 22 March 2019 / Published: 27 March 2019
(This article belongs to the Collection Wearable and Unobtrusive Biomedical Monitoring)
Hypertension is one of the most common cardiovascular diseases, which will cause severe complications if not treated in a timely way. Early and accurate identification of hypertension is essential to prevent the condition from deteriorating further. As a kind of complex physiological state, hypertension is hard to characterize accurately. However, most existing hypertension identification methods usually extract features only from limited aspects such as the time-frequency domain or non-linear domain. It is difficult for them to characterize hypertension patterns comprehensively, which results in limited identification performance. Furthermore, existing methods can only determine whether the subjects suffer from hypertension, but they cannot give additional useful information about the patients’ condition. For example, their classification results cannot explain why the subjects are hypertensive, which is not conducive to further analyzing the patient’s condition. To this end, this paper proposes a novel hypertension identification method by integrating classification and association rule mining. Its core idea is to exploit the association relationship among multi-dimension features to distinguish hypertensive patients from normotensive subjects. In particular, the proposed method can not only identify hypertension accurately, but also generate a set of class association rules (CARs). The CARs are proved to be able to reflect the subject’s physiological status. Experimental results based on a real dataset indicate that the proposed method outperforms two state-of-the-art methods and three common classifiers, and achieves 84.4%, 82.5% and 85.3% in terms of accuracy, precision and recall, respectively. View Full-Text
Keywords: hypertension identification; class association rule (CAR); classification; association rule mining; heart rate variability (HRV); ballistocardiogram (BCG) hypertension identification; class association rule (CAR); classification; association rule mining; heart rate variability (HRV); ballistocardiogram (BCG)
Show Figures

Figure 1

MDPI and ACS Style

Liu, F.; Zhou, X.; Wang, Z.; Cao, J.; Wang, H.; Zhang, Y. Unobtrusive Mattress-Based Identification of Hypertension by Integrating Classification and Association Rule Mining. Sensors 2019, 19, 1489.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop