Interview

Abstract

Dr Xueyun Wei from Jiangsu University of Science and Technology, China, talks to Electronics Letters about the work behind the paper ‘Application of kernel PCA for foetal electrocardiogram estimation’, page 340. Dr Xueyun Wei I joined a team of scientific researchers who, using both advanced signal processing and detection technology, focus on biomedical signal processing and methods that can help to diagnose diseases. At the moment, my research focuses on the non-invasive detection of foetal electrocardiogram (ECG). Foetal ECG monitoring using abdominal ECG signals involves the detection of signals with low amplitudes that are produced by the foetal heartbeat. These signals are often overwhelmed by large interference and noise. The maternal ECG is the predominant source of interference, which is the most important consideration that must be accounted for when estimating foetal ECG signals. Various techniques have been employed to address foetal ECG estimation, including correlation techniques, adaptive filtering, wavelet analysis, blind source separation, neural networks and singular value decomposition. When employing an analysis method, such as principal component analysis, the powerful maternal ECG is considered to be the principal component as it dominates the foetal ECG and other background noises. Utilising the correlation between the maternal components in different lead ECG signals, allows maternal components from abdominal signals to be removed and foetal ECG to be estimated. In our Letter, we have reported a new method to estimate the antepartum foetal ECG signal using kernel principal component analysis (kPCA). We show that this method can reveal nonlinearities in the principal components of signals measured with different leads, thereby allowing the maternal components in different lead signals to be extracted. When compared to the popular independent component analysis method, experimental data from the proposed method has shown strong nonlinear analysis ability for foetal ECG estimation. The work reported in our Letter shows strong nonlinearity between the maternal components of every collected signal due to observed diversity in the signal propagation path. Kernel PCA can be considered to be a non-linear form of PCA that can extract the non-linear principal component from multi-dimensional data. Thus, this method can be applied to ECG signals that have been recorded using multiple leads to eliminate the maternal ECG components and accurately estimate foetal ECG signals. Selecting an appropriate kernel function and parameters based on multi-dimensional nonlinear data is the only challenge associated with this method. Although many signal processing methods have been used to extract foetal ECG information from the abdominal ECG signal, these methods still experience problems that limit their clinical application, such as low extraction accuracy and reliability difficulties. Most of the existing research methods use the linear mixed model or the local linear mixed model, which lack the ability to perform extensive nonlinear analysis. This work provides a new nonlinear un-mixing model for foetal ECG extraction, which could be used for clinical applications in the near future. We are planning to apply this method in appropriate technology as soon as possible. Changes in the foetal heart function are due to the regulating function of the central nervous system. Therefore, foetal heart monitoring plays a very important role in foetal disease/deficiency diagnosis. I am also studying big data issues associated with biomedical signals. At the moment, data storage capacities and modern technology has exceeded the intellectual ability of people. This issue is particularly prevalent for contemporary medical service data, which is ever-increasing. However, traditional statistical techniques and data managerial tools cannot meet current demands. The demand for quantitative diagnosis and precise treatment is increasing in modern medicine. Therefore, the application of biomedical signal processing is a field that is rapidly expanding. This field is full of challenging and innovative opportunities. The excitement it brings to researchers is like climbing Mount Qomolangma (Everest), making people full of passion and expectation.