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An empirical wavelet transform based approach for multivariate data processing application to cardiovascular physiological signals

Singh O., Sunkaria R. K.

Bio-Algorithms and Med-Systems

2018

Vol. 14, no. 4

art. no. 20180030

Background: This article proposes an extension of empirical wavelet transform (EWT) algorithm for multivariate signals specifically applied to cardiovascular physiological signals. Materials and methods: EWT is a newly proposed algorithm for extracting the modes in a signal and is based on the design of an adaptive wavelet filter bank. The proposed algorithm finds an optimum signal in the multivariate data set based on mode estimation strategy and then its corresponding spectra is segmented and utilized for extracting the modes across all the channels of the data set. Results: The proposed algorithm is able to find the common oscillatory modes within the multivariate data and can be applied for multichannel heterogeneous data analysis having unequal number of samples in different channels. The proposed algorithm was tested on different synthetic multivariate data and a real physiological trivariate data series of electrocardiogram, respiration, and blood pressure to justify its validation. Conclusions: In this article, the EWT is extended for multivariate signals and it was demonstrated that the component-wise processing of multivariate data leads to the alignment of common oscillating modes across the components.

Empirical wavelet transform-based delineator for arterial blood pressure waveforms

Singh O., Sunkaria R. K.

Bio-Algorithms and Med-Systems

2016

Vol. 12, no. 2

61--66

Arterial blood pressure (ABP) waveforms provide plenty of pathophysiological information about the cardiovascular system. ABP pulse analysis is a routine process used to investigate the health status of the cardiovascular system. ABP pulses correspond to the contraction and relaxation phenomena of the human heart. The contracting or pumping phase of the cardiac chamber corresponds to systolic pressure, whereas the resting or filling phase of the cardiac chamber corresponds to diastolic pressure. An ABP waveform commonly comprises systolic peak, diastolic onset, dicrotic notch, and dicrotic peak. Automatic ABP delineation is extremely important for various biomedical applications. In this paper, a delineator for onset and systolic peak detection in ABP signals is presented. The algorithm uses a recently developed empirical wavelet transform (EWT) for the delineation of arterial blood pulses. EWT is a new mathematical tool used to decompose a given signal into different modes and is based on the design of an adaptive wavelet filter bank. The performance of the proposed delineator is evaluated and validated over ABP waveforms of standard databases, such as the MIT-BIH Polysomnoghaphic Database, Fantasia Database, and Multiparameter Intelligent Monitoring in Intensive Care Database. In terms of pulse onset detection, the proposed delineator achieved an average error rate of 0.11%, sensitivity of 99.95%, and positive predictivity of 99.92%. In a similar manner for systolic peak detection, the proposed delineator achieved an average error rate of 0.10%, sensitivity of 99.96%, and positive predictivity of 99.92%.