• Vaishnavi D Joshi
• Suraj kadli

Cardiovascular disorders are identified using electrocardiogram (ECG) data. Various sounds, such as power line interference, baseline wandering, motion artifacts, and electromyogram noise, damage the electrocardiogram (ECG) data during signal acquisition. It can be challenging to eliminate these sounds from the recorded ECG signal since an ECG signal is non-stationary. Creating an automated ECG denoising pipeline that can be applied to extensive ECG data analysis. To address the absence of translation-invariance, the wavelet transform method known as the Stationary Wavelet Transform (SWT) was developed. To obtain this Stationary Wavelet Transform and denoise the ECG data using it, a Python or Octave code might be built. As a result, it gets rid of these sounds without losing the characteristics of the ECG signal. Our ability to measure diagnostic parameters including heart rate, QT interval, and ST segment deviation will be improved as a result.

DataSet, ECG, SWT, FDM, and Python Code

"Denoising of ECG Signal Using SWT and FDM ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 11, page no.d526-d532, November-2023, Available :http://www.jetir.org/papers/JETIR2311365.pdf

"Denoising of ECG Signal Using SWT and FDM ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 11, page no. ppd526-d532, November-2023, Available at : http://www.jetir.org/papers/JETIR2311365.pdf