In this work we develop a very simple batch learning algorithm for semiblind extraction of a desired source signal with temporal structure from linear mixtures. Although we use the concept of sequential blind extraction of sources and independent component analysis, we do not carry out the extraction in a completely blind manner; neither do we assume that sources are statistically independent. In fact, we show that the a priori information about the autocorrelation function of primary sources can be used to extract the desired signals (sources of interest) from their linear mixtures. Extensive computer simulations and real data application experiments confirm the validity and high performance of the proposed algorithm.

Background: Due to its easy applicability, pulse wave has been proposed as a surrogate of electrocardiogram (ECG) for the analysis of heart rate variability (HRV). However, its smoother waveform precludes accurate measurement of pulse-to-pulse interval by fiducial-point algorithms.Here we report a pulse frequency demodulation (PFDM) technique as a method for extracting instantaneous pulse rate function directly from pulse wave signal and its usefulness for assessing pulse rate variability (PRV).

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Independent component analysis (ICA) is a powerful tool for separating signals from their mixtures. In this field, many algorithms were proposed, but they poorly use a priori information in order to find the desired signal. Here, we propose a fixed point algorithm which uses a priori information to find the signal of interest out of a number of sensors. We particularly applied the algorithm to cancel cardiac artifacts from a magnetoencephalogram.

Impedance cardiography (ICG) may be altered by noises as respiration and movement artifacts, mainly during exercise. In this work, a scaled Fourier linear combiner (SFLC) event-related to the R-R interval of ECG is proposed. It estimates the deterministic component of the impedance cardiographic signal and removes the noises uncorrelated to this interval. The impedance cardiographic signal is modeled as Fourier series with the coefficients estimated by the least mean square (LMS) algorithm. Simulations have been carried out to evaluate the filter performance for different noise conditions. Moreover, the method capability to remove uncorrelated noises was also examined in physiological data obtained in rest and exercise, by synchronizing respiration and pedaling with a metronome. Analyzing the ICG power spectrum, it was concluded that the proposed filter could remove the noises that are not synchronized with heart rate.