تطبیق فیلتر کردن سیگنال داپلر ترانس کرانیال پر سر و صدا با استفاده از الگوریتم مصنوعی کلونی زنبور عسل
|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|7541||2013||8 صفحه PDF||سفارش دهید|
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Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Engineering Applications of Artificial Intelligence, Volume 26, Issue 2, February 2013, Pages 677–684
Computerized processes are supportive in the new age of medical treatment. Biomedical signals which are collected from the human body supply or important useful data that are related with the biological actions of human body organs. However, these signals may also contain some noise. Heart waves are commonly classified as biomedical signals and are non-stationary due to their statistical specifications. The probability distributions of the noise are very different, and for this reason there is no common method to remove the noise. In this study, adaptive filters are used for noise elimination and the transcranial Doppler signal is analyzed. The artificial bee colony algorithm was employed to design the adaptive IIR filters for noise elimination on the transcranial Doppler signal and the results were compared to those obtained by the methods based on popular and recently introduced evolutionary algorithms and conventional methods.
The Doppler ultrasound device provides non-invasive measurement of blood flow velocity with the aim of diagnosing vascular diseases. The Doppler shifts from red blood cells are used for computing blood velocity. In the computing of blood velocity, the Doppler-shift frequency and Doppler angle are used by the instrument. The Doppler signal displays a time-varying random character because the signal back scattered from the blood possesses a random spatial distribution (Evans, 1989). Doppler indices such as the resistance index (RI) or pulsatility index (PI) are ratios that are computed from various points on the spectrum which are computed to analyze the Doppler signals (Diniz, 2008). Traditionally, the short time Fourier transform (STFT) method has been found to be suitable for the application of the spectrogram (Behbahani, 2007, Billings and Fung, 1995, Brody and Meindl, 1974 and Evans, 1989). Doppler spectrogram indices are determined from the maximum frequency waveform of the Doppler spectrogram (Diniz, 2008 and Feder et al., 1989). The estimation resolution of the maximum frequency waveform is affected by the inner or outer noise in the system causing extra frequency. Hence, this is a significant stage which includes the denoising of the Doppler ultrasound signal for further processing (Haykin, 1996 and Haykin, 2002). When the system parameters or signal conditions change, adaptive filters are generally used and they are to be adjusted to balance this change (Behbahani, 2007). It is known that all adaptive filters capable of adapting at real-time rates experience losses in performance because their adjustments are based on statistical averages taken with limited sample sizes (Widrow, 1971). In the adaptive filtering case, the parameters of the filter which were evaluated a few moments before are used to automatically tune the parameters of the filter which are determined at the present moment, to adapt to the changing situation due to the achievement of the optimal filtering (He et al., 2008). The adaptive filter has the most important properties, because it can be effectively applied in unpredictable situations and the input signal the characteristics of which vary with time, may be tracked by using it. The adaptive filter has been applied mainly in signal processing, control, communications and many other systems for noise cancellation. The gradient based algorithms move in the negative gradient direction. So, they aim to obtain the global minimum on the error surface. However, the filter design approaches which are based on the gradient algorithms may lead to suboptimal IIR filter designs when the error surface is multi-modal (Karaboga and Basturk, 2007, Karaboga and Basturk, 2008, Karaboga, 2005, Karaboga et al., 2012 and Karaboga, 2005). The artificial bee colony (ABC) algorithm is a relatively new swarm intelligence based algorithm which can be used to find optimal or near optimal solutions to numerical and discrete problems. The ABC algorithm was first introduced by Karaboga in 2005, inspired by the foraging behavior of honeybees (Karaboga, 2009 and Karaboga, 2005). It is simple and robust optimization algorithm which can be easily implemented in widely used programming languages and has proven to be both very effective and quick for a diverse set of optimization problems (Karaboga et al., in press). Because of the nonstationary character of Doppler signals, the practical issues related to this signal must be solved using adaptive filters (Kaluzynski, 1987). In this work, a novel approach based on the ABC algorithm is proposed to denoise the Doppler signal by using adaptive IIR filter structures and also, its performance is compared to the popular algorithms such as particle swarm optimization (PSO) and differential evolution (DE), and conventional wavelet transform techniques. The paper is organized as follows. Section 2 describes the proposed artificial bee colony based adaptive noise cancellation system. Section 3 presents the application of the proposed method to the noise cancellation problem. The simulation study is outlined in Section 4 and the simulation results are discussed in Section 5.
نتیجه گیری انگلیسی
An adaptive IIR filter design method based on ABC algorithm was described for the elimination of noise on the transcranial Doppler signal. The performance of the proposed method was also compared to the conventional wavelet transform based methods and popular evolutionary algorithms. The simulation results obtained showed that an adaptive IIR filter can be efficiently designed by the proposed method using a window size of 1000 samples for denoising the disturbed transcranial Doppler signal. Consequently, the proposed method was able to improve the disturbed transcranial Doppler signal and make it acceptable for further analysis. Hence, spectrogram indexes which are widely used in clinical applications can be obtained accurately from maximum frequency curves.