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. The performance of both gradient and signed-gradient algorithms with dual adaptive filters is compared by extracting heartbeat signals from ambient noise in stethoscopes. Simulation results demonstrate that though the signed-gradient adaptive step size LMS algorithm converges at a slower rate at the early stage of iteration, it has a smaller mean squared error (MSE) at the stage of convergence, thus achieves a higher SNR.