A New Method of Identifying Characteristic Points in the Impedance Cardiography Signal Based on Empirical Mode Decomposition

Abstract

The accurate detection of fiducial points in the impedance cardiography signal (ICG) has a decisive impact on the proper estimation of diagnostic parameters such as stroke volume or cardiac output. It is, therefore, necessary to find an algorithm that is able to assess their positions with great precision. The solution to this problem is, however, quite challenging with regard to the high sensitivity of the ICG technique to the noise and varying morphology of the acquired signals. The aim of this study is to propose a novel method that allows us to overcome these limitations. The developed algorithm is based on Empirical Mode Decomposition (EMD)—an effective technique for processing and analyzing various types of non-stationary signals. We find high correlations between the results obtained from the algorithm and annotated by an expert. This, in turn, implies that the difference in estimation of the diagnostic-relevant parameters is small, which suggests that the method can automatically provide precise clinical information.

Intelligent Analysis of Vibration Faults in Hydroelectric Generating Units Based on Empirical Mode Decomposition

“…The method proposed in this experiment has a high AUC value, with AUC values of 0.7365, 0.7335, 0.9232, and 0.9141 for fan, water pump, slide rail, and valve sound detection, respectively, with an average AUC value of 0.8268. This paper's method increased the AUC value by 11.8722% compared to the method in [18], 35.6518% compared to the method in [19], and 38.4322% compared to the method in [20]. At the same time, the accuracy rate of the model proposed in this experiment is 90.1%, and the loss function value is 0.27, both higher than those of the methods in the literature.…”

“…EMD is an effective method for analyzing nonstationary signals. It is an adaptive signal processing method that does not require empirical selection of wavelet basis functions like wavelet transform [20]. It can decompose a series of intrinsic mode functions from complex signals and then perform Hilbert transform on each of them to further calculate the instantaneous frequency of the signal.…”

“…The value of α is finally determined through multiple experiments. The hidden layer neuron number is calculated using the empirical Formula (20), and α values are in [1,14].…”