Sanghamitra Subhadarsini Dash

I am a signal-processing enthusiast!

"The worst thing you can do to yourself is

not become the person that you could be in this lifetime."

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I am Sanghamitra Subhadarsini Dash, a fourth-year undergraduate student in the Department of Electronics and Communication Engineering (ECE) at the National Institute of Technology Puducherry (NITPY), Puducherry, India, with an anticipated graduation in 2026.

My research area mainly focuses on biomedical signal and image processing, machine learning, and deep learning. My goal is to pursue a research career and contribute constructively to the scientific community by converting my ideas into meaningful projects and research publications

Area of research

Signal Processing - Biomedical signal analysis - Deep learning and neural networks

Résumé

Projects and Internships

Extraction of FECG signals from AECG using EMD and Wavelet Transform (Biomedical Signal Processing)

NIT Puducherry, Karaikal

November 2023 ‑ April 2024

The extraction of a clean fECG signal through a non‑invasive method is essential due to various interfering noises. This study aims to provide a robust methodology for extracting clear and accurate fECG from abdominal ECG (aECG), including fECG, mECG, and noises. The proposed methodology consists of empirical mode decomposition (EMD) and wavelet decomposition (WD) to identify different signal frequency components.

Identification of FECG from AECG Recordings using ICA over EMD (Biomedical Signal Processing) (Published Work)

NIT Puducherry, Karaikal

June 2023 ‑ January 2024

Extraction of fetal ECG (fECG) signal is crucial for monitoring the fetus’s health during pregnancy and for early diagnosis of heart abnormalities, which can lead to increased infant mortality rate and post‑natal complications. This paper focuses on a robust approach for fECG extraction using empirical mode decomposition (EMD), independent component analysis (ICA), and FIR filtering. The technique has been validated on simulated signals and applied to fECG synthetic data and aECG data collected from PhysioBank ATM.

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