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001 978-1-4020-9073-8
003 DE-He213
005 20141014113441.0
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008 100301s2009 ne | s |||| 0|eng d
020 _a9781402090738
_9978-1-4020-9073-8
024 7 _a10.1007/978-1-4020-9073-8
_2doi
041 _aeng
050 4 _aTK5102.9
050 4 _aTA1637-1638
050 4 _aTK7882.S65
072 7 _aTTBM
_2bicssc
072 7 _aUYS
_2bicssc
072 7 _aTEC008000
_2bisacsh
072 7 _aCOM073000
_2bisacsh
082 0 4 _a621.382
_223
100 1 _aHaddad, Sandro A. P.
_eauthor.
245 1 0 _aUltra Low-Power Biomedical Signal Processing
_h[electronic resource] :
_bAn Analog Wavelet Filter Approach for Pacemakers /
_cby Sandro A. P. Haddad, Wouter A. Serdijn.
260 1 _aDordrecht :
_bSpringer Netherlands,
_c2009.
264 1 _aDordrecht :
_bSpringer Netherlands,
_c2009.
300 _bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aAnalog Circuits and Signal Processing
505 0 _aThe Evolution of Pacemakers: An Electronics Perspective -- Wavelet versus Fourier Analysis -- Analog Wavelet Filters: The Need for Approximation -- Optimal State Space Descriptions -- Ultra Low-Power Integrator Designs -- Ultra Low-Power Biomedical System Designs -- Conclusions and Future Research.
520 _aUltra Low-Power Biomedical Signal Processing describes signal processing methodologies and analog integrated circuit techniques for low-power biomedical systems. Physiological signals, such as the electrocardiogram (ECG), the electrocorticogram (ECoG), the electroencephalogram (EEG) and the electromyogram (EMG) are mostly non-stationary. The main difficulty in dealing with biomedical signal processing is that the information of interest is often a combination of features that are well localized temporally (e.g., spikes) and others that are more diffuse (e.g., small oscillations). This requires the use of analysis methods sufficiently versatile to handle events that can be at opposite extremes in terms of their time-frequency localization. Wavelet Transform (WT) has been extensively used in biomedical signal processing, mainly due to the versatility of the wavelet tools. The WT has been shown to be a very efficient tool for local analysis of non-stationary and fast transient signals due to its good estimation of time and frequency (scale) localizations. Being a multi-scale analysis technique, it offers the possibility of selective noise filtering and reliable parameter estimation. Often WT systems employ the discrete wavelet transform, implemented on a digital signal processor. However, in ultra low-power applications such as biomedical implantable devices, it is not suitable to implement the WT by means of digital circuitry due to the relatively high power consumption associated with the required A/D converter. Low-power analog realization of the wavelet transform enables its application in vivo, e.g. in pacemakers, where the wavelet transform provides a means to extremely reliable cardiac signal detection. In Ultra Low-Power Biomedical Signal Processing we present a novel method for implementing signal processing based on WT in an analog way. The methodology presented focuses on the development of ultra low-power analog integrated circuits that implement the required signal processing, taking into account the limitations imposed by an implantable device.
650 0 _aEngineering.
650 0 _aBiomedical engineering.
650 1 4 _aEngineering.
650 2 4 _aSignal, Image and Speech Processing.
650 2 4 _aBiomedical Engineering.
700 1 _aSerdijn, Wouter A.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9781402090721
830 0 _aAnalog Circuits and Signal Processing
856 4 0 _uhttp://dx.doi.org/10.1007/978-1-4020-9073-8
912 _aZDB-2-ENG
942 _cEB
999 _c1337
_d1337