Study of wrist pulse signals using time domain spatial features. (July 2015)

• Record Type: Journal Article
• Authors: Rangaprakash, D.; Narayana Dutt, D.
• Is Part Of: Computers & electrical engineering. Volume 45(2015)
• Page Start: 100
• Page End: 107
• Publication Date: 2015-07
• Subjects: Wrist pulse signal -- Gaussian model -- Spatial features -- Support vector machine -- Biomedical signal processing; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2014.12.016
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: Wrist pulse signals are analyzed using spatial features obtained from a bi-modal Gaussian model. Statistically significant group differences are found for two cases: before and after lunch, before and after exercise. A recursive cluster elimination based support vector machine classifier is used for classification. High classification accuracy is obtained for both exercise case (99.71%) and lunch case (99.94%). There is tangible scope for using these results in various healthcare applications. Abstract: Blood travels throughout the body and thus its flow is modulated by changes in body condition. As a consequence, the wrist pulse signal contains important information about the status of the human body. In this work we have employed signal processing techniques to extract important information from these signals. Radial artery pulse pressure signals are acquired at wrist position noninvasively for several subjects for two cases of interest, viz. before and after exercise, and before and after lunch. Further analysis is performed by fitting a bi-modal Gaussian model to the data and extracting spatial features from the fit. The spatial features show statistically significant ( p < 0.001) changes between the groups for both the cases, which indicates that they are effective in distinguishing the changes taking place due to exercise or food intake. Recursive cluster elimination based support vector machine classifier is used to classify between the groups. A high classification accuracy of 99.71% is achieved for the exercise case and 99.94% is achieved for the lunch case. This paper demonstrates the utility of certain spatial features in studying wrist pulse signals obtained under various experimental conditions. The ability of the spatial features in distinguishing changing body conditions can be potentially used for various healthcare applications.