Wavelet Based Interval Varying Algorithm for Optimal Non-Stationary Signal Denoising

Georgieva-Tsaneva, Galya

doi:10.1145/3345252.3345268

Cited by 13 publications

(9 citation statements)

References 10 publications

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“…The participants were from Varna, Bulgaria, and were aged 49 to 68 years, including both men and women. Preprocessing was performed on the recorded data, including decompression of the data (in case they were obtained in a compressed form), reduction in disturbances (removal of artifacts, filtering to reduce side noise) [ 53 ], determination of the maximum amplitude deviations in the ECG signal (R peaks) [ 54 ], extraction of RR intervals (time sequence of the time duration between adjacent R peaks), formation of the normal NN intervals, and formation of the HRV series. The resulting time series was interpolated using the cubic splines wavelet basis and downsampled at 2 Hz.…”

Section: Methodsmentioning

confidence: 99%

Interactive Cardio System for Healthcare Improvement

Georgieva-Tsaneva

2023

Sensors

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The paper presents an interactive cardio system that can be used to improve healthcare. The proposed system receives, processes, and analyzes cardio data using an Internet-based software platform. The system enables the acquisition of biomedical data using various means of recording cardiac signals located in remote locations around the world. The recorded discretized cardio information is transmitted to the system for processing and mathematical analysis. At the same time, the recorded cardio data can also be stored online in established databases. The article presents the algorithms for the preprocessing and mathematical analysis of cardio data (heart rate variability). The results of studies conducted on the Holter recordings of healthy individuals and individuals with cardiovascular diseases are presented. The created system can be used for the remote monitoring of patients with chronic cardiovascular diseases or patients in remote settlements (where, for example, there may be no hospitals), control and assistance in the process of treatment, and monitoring the taking of prescribed drugs to help to improve people’s quality of life. In addition, the issue of ensuring the security of cardio information and the confidentiality of the personal data of health users is considered.

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Section: Methodsmentioning

confidence: 99%

Interactive Cardio System for Healthcare Improvement

Georgieva-Tsaneva

2023

Sensors

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“…Wavelet Transform (WT) methods have been employed successfully to solve various non-stationary signal problems [16,33,34], including EEG [19]. WT is a spectral estimation method that provides another representation of the signal The output of each level are two down sampled components: Approximation Aj and Detail Dj which are represented as:…”

Section: Feature Extractionmentioning

confidence: 99%

Wavelet Based Multi-Class Seizure Type Classification System

Albaqami

Hassan²,

Datta

2022

SSRN Journal

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“…Georgieva-Tsaneva (2019) [11] reviews the WTbased denoising method and provides an effectual algorithm for denoising in non-stationary signals that uses an adaptive threshold scheme, detailed and approximate coefficients processing, and the level of decomposition. The denoising procedures allow specifying the decomposition level, wavelet basis, and testing signal size, as well as calculating the denoising process assessment features.…”

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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Wavelet Based Interval Varying Algorithm for Optimal Non-Stationary Signal Denoising

Cited by 13 publications

References 10 publications

Interactive Cardio System for Healthcare Improvement

Interactive Cardio System for Healthcare Improvement

Wavelet Based Multi-Class Seizure Type Classification System

Optimal ECG Signal Denoising Using DWT with Enhanced African Vulture Optimization

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