Robust Feature Space Optimization and Noise Removal from PCG - Ukil, Arijit;Roy, Uttam Kumar
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In this book, we have proposed a novel noise removal method of PCG signals using robust feature space optimization. The proposed method identifies the noisy PCG signals and eliminates them to ensure effective clinical analytics. We depict more than 85% accuracy and high specificity of identifying noisy PCG signals while experimenting over annotated PCG datasets from large publicly available MIT-Physionet database. We have also shown that propose noise removal has the capability to significantly improve the clinical utility like detection of cardiac normal/ abnormal condition from heart sound or PCG signals.…mehr

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Produktbeschreibung
In this book, we have proposed a novel noise removal method of PCG signals using robust feature space optimization. The proposed method identifies the noisy PCG signals and eliminates them to ensure effective clinical analytics. We depict more than 85% accuracy and high specificity of identifying noisy PCG signals while experimenting over annotated PCG datasets from large publicly available MIT-Physionet database. We have also shown that propose noise removal has the capability to significantly improve the clinical utility like detection of cardiac normal/ abnormal condition from heart sound or PCG signals.
Autorenporträt
Arijit Ukil has more than 15 years of research experience, worked as Scientist in DRDO, India. Currently he is Scientist in Tata Consultancy Services, India. He has published more than 50 research papers, 4 book chapters, 40 patents with 15 grants. He holds Masters in Engineering from Jadavpur University, India. He is a Senior Member, IEEE.