ECG Denoising Using Wiener Filter and Kalman Filter

Manju, B R; Sneha, M R

doi:10.1016/j.procs.2020.04.029

Cited by 50 publications

(6 citation statements)

References 15 publications

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“…In addition, an improved Duck Pack Optimization (DPO) algorithm-based Convolutional Neural network (CNN) model is used for classifying and detecting oral cancer. It employs filtering of noisy signals through the spectral property of the desired signal and present noise, which is considered a stochastic method with linear property (Manju, B. R., 2020).…”

Section: Image Pre-processingmentioning

confidence: 99%

Duck Pack Optimization With Deep Transfer Learning-Enabled Oral Squamous Cell Carcinoma Classification on Histopathological Images

Shetty

Patil

2023

International Journal of Grid and High Performance Computing

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Earlier detection and classification of squamous cell carcinoma (OSCC) is a widespread issue for efficient treatment, enhancing survival rate, and reducing the death rate. Thus, it becomes necessary to design effective diagnosis models for assisting pathologists in the OSCC examination process. In recent times, deep learning (DL) models have exhibited considerable improvement in the design of effective computer-aided diagnosis models for OSCC using histopathological images. In this view, this paper develops a novel duck pack optimization with deep transfer learning enabled oral squamous cell carcinoma classification (DPODTL-OSC3) model using histopathological images. The goal of the DPODTL-OSC3 model is to improve the classifier outcomes of OSCC using histopathological images into normal and cancerous class labels. Finally, the variational autoencoder (VAE) model is utilized for the detection and classification of OSCC. The performance validation and comparative result analysis for the DPODTL-OSC3 model are tested using a histopathological imaging database.

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Section: Image Pre-processingmentioning

confidence: 99%

Duck Pack Optimization With Deep Transfer Learning-Enabled Oral Squamous Cell Carcinoma Classification on Histopathological Images

Shetty

Patil

2023

International Journal of Grid and High Performance Computing

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“…Wiener filtering is a basic noise reduction algorithm. 11 It is assumed that there is a noisy music signal f(n). After the windowing and framing process, fi(m) is obtained, and it is processed by fast Fourier transform (FFT) 12 to determine amplitude spectrum |Fi(k)| and phase spectrum θi(k).…”

Section: Several Music Signal Noise Reduction Algorithmsmentioning

confidence: 99%

A comparative study of multiple music signal noise reduction algorithms in music processing

2023

Noise & Vibration Worldwide

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Electronic music is susceptible to noise in production, which needs to be processed. This paper analyzed several commonly used noise reduction algorithms, including wiener filtering, wavelet transform, spectral subtraction, and improved spectral subtraction, and then compared the noise reduction performance of several algorithms by producing noisy music datasets in the audio analysis tool librosa. It was found from the experimental results that the wavelet transform algorithm performed best when sym3 was used as the wavelet basis function, and the number of decomposition layers was 7. The comparison of different algorithms showed that the performance of the wiener filtering algorithm was poor in reducing noise, and the signal-to-noise ratio (SNR) and signal distortion ratio (SDR) was low; the performance of the improved spectral subtraction algorithm was the best, and the SNR and SDR were 20.36 dB and 17.94 dB, respectively, when the SNR was −8 dB, which were better than the other algorithms. The experimental results demonstrate the reliability of the improved spectral subtraction method in music signal noise reduction. The algorithm can be applied in practical music processing.

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“…The expectation-maximization (EM) algorithm is used to estimate and repeatedly update the parameters of these filters. And also Manju and Sneha (2020) [15] proposed two filters to remove the noises by Wiener filter and the Kalman filter. The simulation results suggest that the Wiener filter is an excellent filter for denoising the ECG data.…”

Section: Related Workmentioning

confidence: 99%

Optimal ECG Signal Denoising Using DWT with Enhanced African Vulture Optimization

Balasubramanian

Tewari

Naruk³

2022

IJRITCC

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Cardiovascular diseases (CVDs) are the world's leading cause of death; therefore cardiac health of the human heart has been a fascinating topic for decades. The electrocardiogram (ECG) signal is a comprehensive non-invasive method for determining cardiac health. Various health practitioners use the ECG signal to ascertain critical information about the human heart. In this paper, the noisy ECG signal is denoised based on Discrete Wavelet Transform (DWT) optimized with the Enhanced African Vulture Optimization (AVO) algorithm and adaptive switching mean filter (ASMF) is proposed. Initially, the input ECG signals are obtained from the MIT-BIH ARR dataset and white Gaussian noise is added to the obtained ECG signals. Then the corrupted ECG signals are denoised using Discrete Wavelet Transform (DWT) in which the threshold is optimized with an Enhanced African Vulture Optimization (AVO) algorithm to obtain the optimum threshold. The AVO algorithm is enhanced by Whale Optimization Algorithm (WOA). Additionally, ASMF is tuned by the Enhanced AVO algorithm. The experiments are conducted on the MIT-BIH dataset and the proposed filter built using the EAVO algorithm, attains a significant enhancement in reliable parameters, according to the testing results in terms of SNR, mean difference (MD), mean square error (MSE), normalized root mean squared error (NRMSE), peak reconstruction error (PRE), maximum error (ME), and normalized root mean error (NRME) with existing algorithms namely, PSO, AOA, MVO, etc.

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ECG Denoising Using Wiener Filter and Kalman Filter

Cited by 50 publications

References 15 publications

Duck Pack Optimization With Deep Transfer Learning-Enabled Oral Squamous Cell Carcinoma Classification on Histopathological Images

Duck Pack Optimization With Deep Transfer Learning-Enabled Oral Squamous Cell Carcinoma Classification on Histopathological Images

A comparative study of multiple music signal noise reduction algorithms in music processing

Optimal ECG Signal Denoising Using DWT with Enhanced African Vulture Optimization

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