Design and Optimization of Fuzzy-Based FIR Filters for Noise Reduction in ECG Signals Using Neural Network

Design and Optimization of Fuzzy-Based FIR Filters for Noise Reduction in ECG Signals Using Neural Network

V. V. Satyanarayana Tallapragada, Venkat Reddy D., Suresh Varma K. N. V., Bharathi D. V. N.
Copyright: © 2022 |Pages: 16
DOI: 10.4018/IJFSA.312215
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Abstract

Cardiovascular disease (CVD) has been identified as a threat to human life for decades, with the majority of individuals dying as a result of delayed diagnosis and treatment. An electrocardiogram (ECG) plays a vital role in the prognosis of such an ailment. The presence of noise and artifacts complicates the accurate detection and identification of CVD. As a result, reliable signal recovery tasks necessitate noise removal, which is an inverse problem. The main noises present in electrocardiogram (ECG) signals are EMG noise, electrode motion artifact noise. In this paper, radial basis function (RBF) and multi swarm optimization neural network (MSONN) are used to denoise the ECG signal. The cut-off frequency is calculated using a low-pass filter. By using, fuzzy FIR filtering technique baseline wander noises can be removed. Results show that MOS based approach outperforms existing approaches in terms of accuracy and is observed to be 87% even when the dataset size is small. Further, noises if any exists are also removed by the use of cascaded multiplier less Fuzzy FIR filters
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Introduction

Electrocardiogram (ECG) is a non-stationary non-linear aperiodic time signal (Chatterjee et al., 2020). ECG signal measures the muscular and electrical function of the heart. The ECG signal will vary with respect to time by the cause of contraction and relaxation of the artery and ventricle of the heart. ECG measurement is done by attaching the electrodes to the patient's body. In recent years, 10 electrodes are used and placed on the limbs and chest that capture heart rate, electrical activity and rhythm information. ECG contains both physiological and pathological information, both of which are important in the diagnosis of cardiac problems. In clinical applications, ECG monitoring will be crucial. However, ECG contains a variety of noises that muddle the morphological properties of the ECG, resulting in a misleading diagnosis and improper treatment of the patients. Empirical Mode Decomposition, Wavelet transform, and adaptive filtering are a few methods for denoising the ECG signal. The most common method of denoising is adaptive filtering. The wavelet transform is a modern technique that employs various thresholding methods.

Figure.1 shows the ECG signal with a typical time interval. The ECG signal contains 3 different components. They are

  • 1.

    P wave

  • 2.

    QRS complex

  • 3.

    T wave.

Also, it contains the components of the segments

  • 1.

    PR-segment

  • 2.

    ST-segment,

  • 3.

    PR interval,

  • 4.

    ST interval,

  • 5.

    RR interval and so on.

ECG signal can be used in many medical fields’ applications such as seizure detection, cardio respiratory monitoring, and monitoring. These signals are also employed in electrocardiographic rhythm analysis, biometrics authentication, cardiac ischemia research and heart-rate variability analysis with a smart electrocardiography patch (Islam et al., 2012), (Liang et al., 2005), (Eberhart et al., 1995), (Chinchkhede et al., 2011), (Li et al., 2008), (Omran et al., 2006), (Blackwell et al., 2006). Many signal processing (Nagajyothi et al., 2017) methods are present to reduce the noise from the ECG signals. Baseline wander, EMG noise, electrode motion artifacts and power line interference are the four main artifacts present in the ECG.

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