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Real-time measurement of signal to noise ratio for harmonic signals

Griszin J., Jańczak D.

Przegląd Elektrotechniczny

2017

R. 93, nr 12

18--21

The paper presents a new real time measuring method of the ratio of sinusoidal signal power to noise power. The recursive estimation procedure was developed on the basis of maximum-likelihood (ML) method with use of the stochastic approximation technique. The performance of the algorithm was checked by means of numerical simulations, which revealed its high efficiency and low numerical load, what enables to use it in real time systems.

W artykule zaprezentowano nową metodę wyznaczania w czasie rzeczywistym stosunku sygnału do szumu w przypadku sygnałów harmonicznych. Rekursywna procedura estymacji została opracowana przy wykorzystaniu estymatora największej wiarygodności oraz metody aproksymacji stochastycznej. Wyniki badań symulacyjnych potwierdziły wysoką efektywność algorytmu i niewielkie obciążenie obliczeniowe.

Artifacts removal from EEG signal: FLM optimization-based learning algorithm for neural network-enhanced adaptive filtering

Quazi M. H., Kahalekar S. G.

Biocybernetics and Biomedical Engineering

2017

Vol. 37, no. 3

401--411

Electroencephalogram (EEG) denotes a neurophysiologic measurement, which observes the electrical activity of the brain through making a record of the EEG signal from the electrodes positioned on the scalp. The EEG signal gets mixed with other biological signals, called artifacts. Few artifacts include electromyogram (EMG), electrocardiogram (ECG) and electrooculogram (EOG). Removal of artifacts from the EEG signal poses a great challenge in the medical field. Hence, the FLM (Firefly + Levenberg Marquardt) optimization-based learning algorithm for neural network-enhanced adaptive filtering model is introduced to eliminate the artifacts from the EEG. Initially, the EEG signal was provided to the adaptive filter for yielding the optimal weights using the renowned optimization algorithms, called firefly algorithm and LM. These two algorithms are effectively hybridized and applied to the neural network to find the optimal weights for adaptive filtering. Then, the designed filtering process renders an improved system for artifacts removal from the EEG signal. Finally, the performance of the proposed model and the existing models regarding SNR, computation time, MSE and RMSE are analyzed. The results conclude that the proposed method achieves a high SNR of 42.042 dB.