Investigation of Adaptive Filtering for Noise Cancellation in ECG signals

Investigation of Adaptive Filtering for Noise Cancellation in ECG signals

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Investigation of Adaptive Filtering for Noise Cancellation in ECG signals Soroor Behbahani Biomedical Engineering Department, Science and Research Branch, Islamic Azad University, Tehran, Iran. Member of Young Researcher Club of Biomedical Engineering Department, Science and Research Branch, Islamic Azad University, Tehran, Iran. soroor_behbahani@yahoo.com Abstract New generation of medical treatment has been supported by computerized processes. Signals recorded from the human body provide valuable information about the biological activities of body organs. The organs’ characteristic topologies with temporal and spectral, properties, can be correlated with a normal or pathological function. In response to dynamic changes in the behavior of those organs, the signals exhibit time- varying, non-stationary responses. The signals are always contaminated by a drift and interference caused by several bioelectric phenomena, or by various types of noise, such as intrinsic noise from the recorder and noise from electrode-skin contact. In this paper we utilize Adaptive Filters for noise cancellation and analysis ECG signals. Keywords : Adaptive Filter, Noise Cancellation, ECG Signal, Matlab Program 1. Introduction Biomedical signals like heart wave commonly change their statistical properties over time tending to be non-stationary. Unfortunately there is no universal method to reduce noise because the probability distributions of noise are different [7, 13]. Noise cancellation is a special case of optimal filtering which can be applied when some information about the reference noise signal is available. The noise cancellation technique has many applications, e.g. speech processing, echo cancellation and enhancement, antenna array processing, biomedical signal and image processing and so on[1-4]. The standard methods of noise cancellation use only one primary signal [1]. However, in many applications, especially in biomedical signal processing, we are able to measure several primary signals. Often this possibility can help to improve the performance of noise cancellation procedure. The standard approach is to make use of several noisy signals by recording from the same source. This consists of using a number of noise cancellation systems in parallel with one primary input to each system [1]. The estimated signal is obtained by selecting the best one in the sense of some criterion from the multi-channel output signal [5]. Adaptive Filters are best used in cases where signal conditions or system parameters are slowly changing and the filter is to be adjusted to compensate for this change. The least mean squares (LMS) criterion is a search algorithm that can be used to provide the strategy for adjusting the filter coefficients. A number of adaptive structures have been used for different applications in adaptive filtering: 1. Noise Cancellation. Figure 1 shows the adaptive structure modified for a noise cancellation application. The desired signal
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Investigation of Adaptive Filtering for Noise Cancellation in ECG signals

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