Smart combination of ECG and PPG signals: an innovative approach towards an electronic device for vital signs monitoring-Reference-Cited by-同舟云学术

Smart combination of ECG and PPG signals: an innovative approach towards an electronic device for vital signs monitoring

Published:2023-06-08 Issue: Volume: Page:
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Container-title:2023 9th International Workshop on Advances in Sensors and Interfaces (IWASI)
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Author:

Botrugno Chiara¹,Leogrande Elisabetta¹,Dell’Olio Francesco¹,Natale Teresa¹

Affiliation:

1. Polytechnic University of Bari,Department of Electrical and Information Engineering,Bari,Italy

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10164122/10164290/10164373.pdf?arnumber=10164373

Reference15 articles.

1. Portable health monitoring kit using photolethysmogram signal

2. Principle component analysis on photoplethysmograms: Blood oxygen saturation estimation and signal segmentation;li;2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society,2011

3. A Real-Time QRS Detection Algorithm;pan;IEEE Transaction on Biomedical Engineering,1985

4. Continuous noninvasive blood pressure measurement by pulse transit time;fung;The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society,2004

5. Guidelines for SpO2 measurement using the MAXIM MAX32664 sensor hub;Maxim Integrated,2023

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