Devedzic, Goran

Karadaglic, Dejan

Knezevic, Sasa

Stojanovic, Radovan

English

Existing biomedical wavelet based applications exceed the computational, memory and consumption resources of low-complexity embedded systems. In order to make such systems capable to use wavelet transforms, optimization and implementation techniques are proposed. The Real Time QRS Detector and “De-noising” Filter are developed and implemented in 16-bit fixed point microcontroller achieving 800 Hz sampling rate, occupation of less than 500 bytes of data memory, 99.06% detection accuracy, and 1 mW power consumption

ResearchOnline@GCU

Optimization and implementation of the wavelet based algorithms for embedded biomedical signal processing

Existing biomedical wavelet based applications exceed the computational,
   memory and consumption resources of low-complexity embedded systems. In order
   to make such systems capable to use wavelet transforms, optimization and
   implementation techniques are proposed. The Real Time QRS Detector and
   ?De-noising? Filter are developed and implemented in 16-bit fixed point
   microcontroller achieving 800 Hz sampling rate, occupation of less than 500
   bytes of data memory, 99.06% detection accuracy, and 1 mW power consumption.
   By evaluation of the obtained results it is found that the proposed
   techniques render negligible degradation in detection accuracy of -0.41% and
   SNR of -2.8%, behind 2-4 times faster calculation, 2 times less memory usage
   and 5% energy saving. The same approach can be applied with other signals
   where the embedded implementation of wavelets can be beneficial.</jats:p

Optimization and implementation of the wavelet based algorithms for embedded biomedical signal processing

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