Automatic diagnosis of valvular heart diseases by impedance cardiography signal processing

Publication date: March 2020Source: Biomedical Signal Processing and Control, Volume 57Author(s): Ihsèn Ben Salah, Ramón De la Rosa, Kaïs Ouni, Ridha Ben SalahAbstractValvular heart diseases (VHDs) are an abnormal activity of the heart caused by a damage of one of the four heart valves. The impedance cardiography (ICG) is a non-invasive method employed to identify and classify the heart abnormalities. Despite its importance, there are not many works in scientific literature that use the ICG method in order to diagnose VHDs. Therefore, this paper deals with the ICG signal processing for the classification of normal (N) and various VHDs classes of heartbeats. In this work, six types of heartbeat classes of VHD are used, namely: aortic insufficiency (AOI), aortic stenosis (AOS), aortic disease (AOD), mitral disease (MD), mitral-aortic heart disease (MAOHD) and tricuspid insufficiency + mitral disease (TI + MD). The classification of these heartbeat classes is performed using a combination among statistical, morphological and spectral features. For each ICG heartbeat, the statistical features (median, mean, standard deviation, kurtosis, skewness, central moment and Shannon entropy) are computed from the first four intrinsic mode functions (IMFs) calculated using the empirical mode decomposition (EMD) technique. These statistical features are subjected to principal component analysis (PCA) to reduce the dimensionality. Then, the morphological features are extracted by c...
Source: Biomedical Signal Processing and Control - Category: Biomedical Science Source Type: research