The electrocardiogram (ECG) is a diagnostic tool that provides insights into the hearts electrical activity and overall health. However, internal and external noises complicate accurate heart issue diagnosis. Noise in the ECG signal distorts and introduces artifacts, making it difficult to detect subtle abnormalities. To ensure an accurate evaluation, noise-free ECG signals are crucial. This study introduces the empirical wavelet transform (EWT), a contemporary denoising method. EWT decomposes the signal into frequency components, allowing detailed analysis by constructing a customized wavelet basis. Researchers and practitioners can enhance signal analysis by separating the desired components from unwanted noise. The EWT approach effectively eliminates noise while maintaining signal information. The study applies DWT-ADTF, FST, Kalman, LiouvilleWeyl fractional compound integral filter LW, Weiner, and EWT denoising methods to two ECG databases from MIT-BIH, which encompass a wide range of cardiac signals and noise levels. The comparative analysis highlights EWTs strengths through improved signal quality and objective performance metrics. This adaptive transform proves promising for denoising ECG signals and facilitating accurate analysis in clinical and research settings.

We have investigated the resistivity of a 2D hole system in GaAs in the temperature range 200 mK < T < 800 mK at zero magnetic field and low hole densities when the system is near the metal–insulator transition in the insulating side. We have found that the resistivity follows the Efros–Shklovskii variable range hopping (ES-VRH) law, this behaviour is consistent with the existence of a Coulomb gap. The resistivity scales with temperature and the prefactor has been found independent of temperature and density, thus confirming the dominance of hole–hole interaction.