Empirical Mode Decomposition and Principal Component Analysis implementation in processing non-invasive cardiovascular signals

Biomedical signals are relentlessly superimposed with interferences. The nonlinear processes which generate the signals and the interferences regularly exclude or limit the usage of classical linear techniques, and even of wavelet transforms, to decompose the signal.Empirical Mode Decomposition (EMD) is a nonlinear and adaptive technique to decompose data. Biomedical data has been one of its most active fields. EMD is fully data-driven, thus producing a variable number of modes. When applied to cardiovascular signals, the modes expressing cardiac-related information vary with the signal, the subject, and the measurement conditions. This makes problematic to reconstruct a noiseless signal from the modes EMD generates.To synthesize and recompose the results of EMD, Principal Component Analysis (PCA) was used. PCA is optimal in the least squares sense, removing the correlations between the modes EMD discovers, thus generating a smaller set of orthogonal components. As EMD-PCA combination seems profitable its impact is evaluated for non-invasive cardiovascular signals: ballistocardiogram, electrocardiogram, impedance and photo plethysmogram.These cardiovascular signals are very meaningful physiologically. Sensing hardware was embedded in a chair, thus acquiring also motion artefacts and interferences, which EMD-PCA aims at separating. EMD is seen to be important, because of its data adaptability, while PCA is a good approach to synthesize EMD outcome, and to represent only the cardiovascular portion of the signals.     

Multivariate and multiscale monitoring of large-size low-speed bearings using Ensemble Empirical Mode Decomposition method combined with Principal Component Analysis

With a view to detecting incipient failures in large-size low-speed rolling bearings and ensuring minimal effect of subjectivity on the process, a new data-driven multivariate and multiscale statistical monitoring method is proposed. The proposed method which combines the Principal Component Analysis (PCA) multivariate monitoring approach and the Ensemble Empirical Mode Decomposition (EEMD) method, which adaptively decomposes signals into various time scales, was called the EEMD-based multiscale PCA (EEMD–MSPCA). The method is very general in nature, which is why it could also be used in different areas and for various tasks. It can be used for controlling each time scale of decomposition or only the selected ones, for multivariate and multiscale filtering or for monitoring system operation on the basis of reconstructed i.e. filtered signals. The efficiency of the proposed EEMD–MSPCA method for the task of bearing condition monitoring and signal filtering was evaluated on simulated as well as on actual vibration and Acoustic Emission (AE) signals measured on a purpose built test stand. The fact that the proposed method is able to identify the local bearing defect of a very small size indicates that AE and vibration signals carry sufficient information on the bearing condition and that the proposed EEMD–MSPCA method ensures high-reliability bearing fault detection.