Signed-gradient adaptive step size LMS algorithm for biomedical applications.

Gradient adaptive step size adaptive filters have been widely used to adapt different biomedical application environments and obtain useful life signals from serious ambient noise and interferences. In order to further improve the signal-to-noise ratio (SNR) of the life signals, this paper presents a class of signed-gradient adaptive step size least mean square (LMS) adaptive filters. The proposed algorithms introduce a sign function to replace the gradient of squared error in the step size updating process of the gradient adaptive step size LMS adaptive filters.

A novel gradient adaptive step size LMS algorithm with dual adaptive filters.

Least mean square (LMS) adaptive filter has been used to extract life signals from serious ambient noises and interferences in biomedical applications. However, a LMS adaptive filter with a fixed step size always suffers from slow convergence rate or large signal distortion due to the diversity of the application environments. An ideal adaptive filtering system should be able to adapt different environments and obtain the useful signals with low distortion.

Modified Log-LMS adaptive filter with low signal distortion for biomedical applications.

Life signals from human body, e.g. heartbeat or electrocardiography (ECG), are usually weak and susceptible to external noise and interference.