Analog-and-Algorithm-Assisted Ultra-low Power Biosignal Acquisition Systems by Venkata Rajesh Pamula & Chris Van Hoof & Marian Verhelst

Author:Venkata Rajesh Pamula & Chris Van Hoof & Marian Verhelst
Language: eng
Format: epub
ISBN: 9783030058708
Publisher: Springer International Publishing

The idea of NUS based CS for physiological signals has also been explored. One of the earliest usage of NUS for PPG acquisition is reported in [85]. The authors in [85] argued that, given the fact that PPG signals are sparse on frequency basis and also the LED driver dominates the overall power consumption of PPG acquisition systems, using NUS based CS enables the reduction in power consumption of the LED driver, proportional to the CR. This idea was demonstrated on a commercially available medical kit from TI and achieves a claimed performance of 10 × compression without sacrificing accuracy in estimating SpO2 on reconstructed signal. More recently, Rajesh et al. [77] reported CS based PPG readout with embedded feature extraction for estimating HR and HRV directly from CS data. The presented ASIC achieves up to 30 × compression and therefore 30 × reduction in the LED driver power consumption, without significant loss of accuracy in estimating the average HR. The details of the feature extraction process and the ASIC implementation are presented in Chaps. 4 and 5, respectively.

Finally, a few hardware implementations for the CS reconstruction process are described in the literature. Maechler et al. [66, 67] reported hardware implementations for CS signal recovery for audio and long-term evolution (LTE) channel estimation, respectively. Ren and Marković [65] reported an ASIC implementation for CS reconstruction of biomedical signals. The ASIC, implemented in a 40-nm process, achieves a throughput of 12–237 kS/s for ExG signals, while consuming a power ranging from 8.6 to 78 mWs. Although Ren and Marković [65] advances the state-of-the-art by 76–350 × in terms of energy efficiency, the absolute power consumption is still higher for it to be integrated into the sensor nodes. Hence, alternative approaches, that circumvent signal reconstruction by extracting the features of interest directly from CS data at low-power consumption, are required. Some of those approaches, applied to EEG and PPG signal processing, are described in Chap. 4.

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