Publications by authors named "Gayadhar Pradhan"

Article Synopsis
  • The paper presents the design of high-speed second-order infinite impulse response (IIR) notch and anti-notch filters, utilizing a concept called re-timing to enhance speed.
  • An innovative detection method for identifying protein hot-spot locations is introduced, which outperforms traditional IIR Chebyshev filter techniques and biological methods.
  • The filters are implemented and tested using Xilinx Vivado 18.3 software on a Zynq-7000 Series FPGA, resulting in consistent hot-spot predictions and the discovery of new potential hot-spots.
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Heartbeat classification is central to the detection of the arrhythmia. For the effective heartbeat classification, the noise-robust features are very significant. In this work, we have proposed a noise-robust support vector machine (SVM) based heartbeat classifier.

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This paper presents a novel Electrocardiogram (ECG) denoising approach based on the generative adversarial network (GAN). Noise is often associated with the ECG signal recording process. Denoising is central to most of the ECG signal processing tasks.

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This paper presents a novel electrocardiogram (ECG) denoising approach based on variational mode decomposition (VMD). This work also incorporates the efficacy of the non-local means (NLM) estimation and the discrete wavelet transform (DWT) filtering technique. Current ECG denoising methods fail to remove noise from the entire frequency range of the ECG signal.

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The primary objective of the presented work is to exploit the power of modified empirical mode decomposition (M-EMD) for the denoising of ECG signals. It is well known that the ECG signals get corrupted by a number of noises during the recording process. Especially, during wireless ECG recording and ambulatory patient monitoring, the signal gets corrupted by additive white Gaussian noise (AWGN).

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