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Meslek Yüksek Okulu

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Browsing Meslek Yüksek Okulu by Author "Ahmad, Imteyaz"

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    Citation Count: Özaydın, Selma; Ahmad, Imteyaz (2024). "Comparative Performance Analysis of Filtering Methods for Removing Baseline Wander Noise from an ECG Signal", Fluctuation and Noise Letters.
    Comparative Performance Analysis of Filtering Methods for Removing Baseline Wander Noise from an ECG Signal
    (2024) Özaydın, Selma; Ahmad, Imteyaz; 253019
    ECG signals play a vital role in the diagnosis of cardiovascular conditions. However, they often su®er from the e®ects of various noise sources, including baseline wandering, respiratory artifact noise, power line interference and electrode motion artifacts. To overcome these challenges, it is imperative to implement low-frequency signal noise reduction strategies. Such strategies aim to signi¯cantly improve the quality of ECG signals, thus promoting more accurate and reliable diagnosis of cardiovascular disorders. This paper conducts a comparative analysis to assess the e®ectiveness of commonly used ¯ltering and wavelet techniques in reducing Baseline Wander (BW) noise within ECG signals generated by the in°uence of breathing or electrode movements. It is common to observe the selection and evaluation of only one particular technique in the existing literature. In contrast, this study aims to provide a comprehensive comparative analysis, providing insight into the performance and relative merits of di®erent techniques. Our research uses both ¯ltering and Discrete Wavelet Transform (DWT) techniques in baseline noise removal. In this context, a reference point is established utilizing noise-free signals and a meticulous investigation of the wavelet-based approach that most e®ectively eliminates the resulting noise is provided. Subsequently, we assess the reference input and output signal via Signal-to-Noise Ratio (SNR) and Kolmogorov–Smirnov statistical test measurements. The most important contribution of this work to the scienti¯c community resides in the comprehensive examination of IIR/FIR-based and wavelet method-based ¯ltering methods capable of yielding the highest SNR levels across various ECG signals with various types of BW noise. Additionally, the e®ectiveness of the Chebychev-II ¯lter in BW noise removal is highlighted. Our study was conducted using the MATLAB platform and code command lines were shared to facilitate the reproduction of our study by other researchers. It is considered that this study will be an important reference in the selection of e®ective techniques for removing BW noise within ECG signals.
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    Article
    Citation Count: Ahmad, Imteyaz; Özaydın, Selma (2023). "ECG Signal Denoising with SciLab", International Journal of Intelligent Systems and Applications in Engineering, Vol. 11, No. 4, pp. 853-859.
    ECG Signal Denoising with SciLab
    (2023) Ahmad, Imteyaz; Özaydın, Selma; 253019
    This paper presents a study on de-noising electrocardiogram (ECG) signals using Scilab, an open-source software package known for its signal processing capabilities. ECG signals are often contaminated by various noise sources, which can reduce the accurate diagnosis and monitoring of heart health. In this work, digital signal processing methods such as Finite Impulse Response (FIR) and Infinite Impulse Response (IIR) filters are used to effectively suppress noise while preserving the essential features of the ECG waveform. We explore main noise sources that commonly affect ECG recordings, such as baseline wandering noise, power-line interference, and muscle artifacts, and discuss their respective challenges. The de-noising methods has been extensively evaluated and demonstrated its ability to improve signal quality and diagnostic accuracy by eliminating noise artifacts. The results highlight Scilab's potential for de-noising ECG signals and its importance in improving patient care and biomedical signal processing applications. The efficacy of the de-noising methods is thoroughly evaluated through comparative analyses with other commonly used de-noising approaches. Experimental results demonstrate its superiority in preserving the QRS complex while efficiently eliminating noise artifacts, leading to more accurate and reliable diagnostic information. In conclusion, this paper presents a comprehensive study on de-noising ECG signals using Scilab, offering a valuable contribution to the field of biomedical signal processing. Researchers and practitioners in the domain of ECG signal processing can benefit from the insights and techniques presented herein to advance their studies and further applications.