Electroencephalography (EEG) is a sensitive and weak biosignal that varies from person to person. It is easily affected by noise and artifacts. Hence, maintaining the signal integrity to design an EEG acquisition system is crucial. This article proposes an analog design for acquiring EEG signals. The proposed design consists of eight blocks: (1) a radio-frequency interference filter and electro-static discharge protection, (2) a preamplifier and second-order high-pass filter with feedback topology and an unblocking mechanism, (3) a driven right leg circuit, (4) two-stage main and variable amplifiers, (5) an eight-order anti-aliasing filter, (6) a six-order 50-Hz notch filter (optional), (7) an opto-isolator circuit, and (8) an isolated power supply. The maximum gain of the design is approximately 94 dB, and its bandwidth ranges from approximately 0.18 to 120 Hz. The depth of the 50-Hz notch filter is −35 dB. Using this filter is optional because it causes EEG integrity problems in frequencies ranging from 40 to 60 Hz.
Long-term prediction of mean arterial blood pressure (MAP) time series can help clinicians to select a proper treatment based on their diagnosis. In this way, this paper firstly introduces a new prediction method for time series prediction based on fuzzy functions (FF) in multi-model mode and applies it for forecasting MAP time series as a new application. The proposed model consists of three steps. First step is to estimate the missing values in MAP time series by a linear interpolation method and to denoise it by using the empirical mode decomposition (EMD) procedure. Second step is to reconstruct the phase space.
Last step is to apply a predictive model based on fuzzy functions (FFs). This model consists of two parts: 1) identifying the model structure by Gustafson-Kessel (GK) clustering and 2) estimating the output of each cluster by multivariate adaptive regression splines (MARS). Results show that, the proposed FF-based MARS model is more accurate than ANFIS and FF-based ANFIS, and its results are in the range of standard values.Beside, by using different strategies for long-term prediction, multiple FF-based MARS models has best result in comparison to recursive and multiple-recursive strategies.
Keywords-long-term prediction; fuzzy functions; multivariate adaptive regression splines(MARS); multi-model approach; blood pressure time seriesI.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.