Noise Reduction Of Electrocardiogram Signals Research Articles

Performance Analysis of Notch Filter in ECG Signal Noise Reduction

An electrocardiogram (ECG) is a medical test that records the normal and abnormal condition of the heart. It is an essential tool for identifying vari- ous cardiac related issues. However, ECG wave are often distorted by diverse noise sources, such as movement artifacts or electrical interference. To remove these artifacts and enhance the quality of the ECG signal, various filters are commonly used. The choice of filter type depends on the definite necessities of the application. FIR filters are used in the area that needs linear time response and accurate amplitude response. They have a stable and predictable response and are more robust to coefficient quantization errors. However, they require a larger number of coefficients to achieve the desired frequency response, which can lead to increased computational complexity. The use of digital filters, specifically notch filters, for denoising ECG signals is an effective technique. Adders are essential components in the design of notch filters, and researchers have been exploring different types of adders to improve the parameters of these filters. In this paper, the Ripple carry adder, Carry select Adder, and adder using Carry skip are compared in terms of their performance parame- ters, including area, delay, and power consumption. The RCA is a basic adder, while the C-Skip and C-Select adder are designed to reduce the delay of the addition operation. The results of the study showed that the C-Skip and C- Select adder outperformed the R-Carry adder in terms of delay and power con- sumption. The C-select adder had the lowest delay, while the C-Skip adder had the lowest power consumption. However, the area of the C-select Adder was larger compared to the other adders. Overall, the selection of adder depends on the particular need of the application. Notch filter used for ECG noise removal designed using these adders and its performance is analyzed.

Intelligent hybrid approaches for human ECG signals identification

This paper presents hybrid approaches for human identification based on electrocardiogram (ECG). The proposed approaches consist of four phases, namely data acquisition, preprocessing, feature extraction and classification. In the first phase, data acquisition phase, data sets are collected from two different databases, ECG-ID and MIT-BIH Arrhythmia database. In the second phase, noise reduction of ECG signals is performed by using wavelet transform and a series of filters used for de-noising. In the third phase, features are obtained by using three different intelligent approaches: a non-fiducial, fiducial and a fusion approach between them. In the last phase, the classification approach, three classifiers are developed to classify subjects. The first classifier is based on artificial neural network (ANN). The second classifier is based on K-nearest neighbor (KNN), relying on Euclidean distance. The last classifier is support vector machine (SVM) classification accuracy of 95% is obtained for ANN, 98 % for KNN and 99% for SVM on the ECG-ID database, while 100% is obtained for ANN, KNN, and SVM on MIT-BIH Arrhythmia database. The results show that the proposed approaches are robust and effective compared with other recent works.

Optimized Denoising scheme via Opposition based Self-adaptive learning PSO algorithm for Wavelet Based ECG Signal Noise Reduction

Electrocardiogram (ECG) signal is significant to diagnose cardiac arrhythmia among various biological signals. The accurate analysis of noisy electrocardiographic (ECG) signal is a very motivating challenge. According to this automated analysis, the noises present in electrocardiogram signal need to be removed for perfect diagnosis. Numerous investigators have been reported different techniques for denoising the electrocardiographic signal in recent years. In this paper, an efficient scheme for denoising electrocardiogram (ECG) signals is proposed based on a wavelet-based threshold mechanism. This scheme is based on an opposition-based self-adaptive learning particle swarm optimisation (OSLPSO) in dual tree complex wavelet packet scheme, in which the OSLPSO is utilised to for threshold optimisation. Different abnormal and normal electrocardiographic signals are tested to evaluate this approach from MIT/BIH arrhythmia database, by artificially adding white Gaussian noises with variation of 5 dB, 10 dB and 15 dB. Simulation results illustrate that the proposed system has good performance in various noise level and obtains better visual quality compared with other methods.

Electrocardiogram signal denoising based on a new improved wavelet thresholding

Good quality electrocardiogram (ECG) is utilized by physicians for the interpretation and identification of physiological and pathological phenomena. In general, ECG signals may mix various noises such as baseline wander, power line interference, and electromagnetic interference in gathering and recording process. As ECG signals are non-stationary physiological signals, wavelet transform is investigated to be an effective tool to discard noises from corrupted signals. A new compromising threshold function called sigmoid function-based thresholding scheme is adopted in processing ECG signals. Compared with other methods such as hard/soft thresholding or other existing thresholding functions, the new algorithm has many advantages in the noise reduction of ECG signals. It perfectly overcomes the discontinuity at ±T of hard thresholding and reduces the fixed deviation of soft thresholding. The improved wavelet thresholding denoising can be proved to be more efficient than existing algorithms in ECG signal denoising. The signal to noise ratio, mean square error, and percent root mean square difference are calculated to verify the denoising performance as quantitative tools. The experimental results reveal that the waves including P, Q, R, and S waves of ECG signals after denoising coincide with the original ECG signals by employing the new proposed method.