Sharif Digital Repository

We present a new modified wavelet transform, called the multiadaptive bionic wavelet transform (MABWT), that can be applied to ECG signals in order to remove noise from them under a wide range of variations for noise. By using the definition of bionic wavelet transform and adaptively determining both the center frequency of each scale together with the T-function, the problem of desired signal decomposition is solved. Applying a new proposed thresholding rule works successfully in denoising the ECG. Moreover by using the multiadaptation scheme, lowpass noisy interference effects on the baseline of ECG will be removed as a direct task. The method was extensively clinically tested with real... 

The depth of anaesthesia estimation has been of a great interest in recent decades. In this paper we present a new methodology to quantify the depth of anaesthesia by quantifying the power-law correlations of the EEG signal. Extraction of useful information about the nonlinear dynamics of the brain during anaesthesia has been proposed with the optimum fractal scaling exponent. This optimum solution is based on the best domain of box sizes in the detrended fluctuation analysis (DFA) algorithm which have meaningful changes at different depth of anaesthesia. The experimental results confirm that our new algorithm on the raw EEG data can clearly discriminate between aware to moderate and deep... 

The activity generated in the brain in response to external stimulations which is named the evoked potential (EP) is typically buried in the background EEG. Because of the low signal to noise ratio ofEPs, it is difficult to record single trial evoked potentials. The traditional technique which is based on ensemble averaging destroys the dynamic information of single trials. In this paper, a new method has been proposed based on generalized eigen value decomposition to extract single trial EPs from single channel EEG recordings. The extraction of the N75-P100-N135 complex in simulated and actual visual evoked potentials is mainly taken under consideration. To illustrate the effectiveness of... 

In this paper, an approach based on adaptive noise cancellation (ANC) is evaluated for extraction of the fetal heart rate using photoplethysmographic signals from the maternal abdomen. A simple optical model is proposed in which the maternal and fetal blood pulsations result in emulated signals where the lower SNR limit (fetal to maternal) is - 25 dB. It is shown that a recursive least-squares algorithm is capable of extracting the peaks of the fetal PPG from these signals, for typical values of maternal and fetal tissues. © 2007 Elsevier Ltd. All rights reserved