Biomedical Engineering Systems and Technologies: 4th by Bruno Sobral, Chunhong Mao, Maulik Shukla, Dan Sullivan,

By Bruno Sobral, Chunhong Mao, Maulik Shukla, Dan Sullivan, Chengdong Zhang (auth.), Ana Fred, Joaquim Filipe, Hugo Gamboa (eds.)

This booklet constitutes the completely refereed post-conference lawsuits of the 4th foreign Joint convention on Biomedical Engineering structures and applied sciences, BIOSTEC 2011, held in Rome, Italy, in January 2011. The 27 revised complete papers provided including one invited lecture have been rigorously reviewed and chosen from a complete of 538 submissions. The papers disguise quite a lot of themes and are geared up in 4 normal topical sections on biomedical electronics and units; bioinformatics types, tools and algorithms; bio-inspired platforms and sign processing; future health informatics.

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Extra info for Biomedical Engineering Systems and Technologies: 4th International Joint Conference, BIOSTEC 2011, Rome, Italy, January 26-29, 2011, Revised Selected Papers

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1. The main components of this IC are 10 identical channels and a digital block. Each channel includes an AFE, a ΣΔmodulator and a 12-bit digital-to-analog converter (DAC) for DC-offset suppression of the associated channel. The AFE’s inputs are the electrode input (VEL ), the reference signal (VREF ), signal ground (VSGND ) and DC-suppression voltage (VDC ). Whereas VEL , VREF and VSGND are familiar IA inputs, the VDC input is used to suppress the input DC-offset voltage and is driven by the DAC output (VDAC ).

American Nurses Association, Washington (1997) 13. : Nurse staffing levels and adverse events following surgery in US hospitals. Journal of Nursing Scholarship 30(4), 315–321 (1998) 14. : Fluid resuscitation in major burns. ANZ Journal of Surgery 76, 35–38 (2006) 15. : Intelligent patient monitoring and management systems: A review. IEEE Engineering in Medicine and Biology 12, 23–33 (1993) 16. : A device for automatic measurement of critical care patient’s urine output. In: 6th IEEE International Symposium on Intelligent Signal Processing, pp.

However, features such as increased noise level, limited battery and computational resources, and the requirement for on-line processing challenge the accurate, real-time detection of physiological parameters, such as HR peaks. In this paper, we consider the extraction of heart rate (HR) from a photoplethysmography (PPG) signal that originates from an oximeter sensor interfaced to a mobile device. We propose a novel approach to compute HR from the PPG signal based on a dynamically adapted, single frequency phase vocoder algorithm.

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