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Erschienen in:
Challenges and Opportunities in Wearable Biomedical Interfaces Kapitel lesen Erstes Kapitel lesen

2019 | OriginalPaper | Buchkapitel

2. Adaptive Sampling for Ultra-Low-Power Electrocardiogram (ECG) Readouts

verfasst von : Venkata Rajesh Pamula, Chris Van Hoof, Marian Verhelst

Erschienen in: Analog-and-Algorithm-Assisted Ultra-low Power Biosignal Acquisition Systems

Verlag: Springer International Publishing

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Abstract

Adaptive sampling is introduced as an early data rate reduction technique for ultra-low-power electrocardiogram (ECG) readouts in this chapter. The proposed adaptive sampling technique relies on detection of local bandwidth (BW) of the ECG signal and altering the sampling frequency. Compared to other ECG data rate reduction approaches such as discrete wavelet transform (DWT) and compressive sampling (CS), adaptive sampling is shown to introduce less distortion as accessed through percentage root mean square distortion (PRD) for similar compression ratio (CR). Efficient task partitioning between analog and digital domains, from energetics stand point, in realizing the adaptive sampling controller (ASC) is presented. Systematic design approaches are introduced in realizing the ultra-low-power analog ASC that dissipates only 30.6 nW of power and achieving a dynamic range (DR) of 47.2 dB. The implemented ASC chip enables up to 8 × compression of the ECG signal, while completely preserving the morphology of the QRS complexes which is crucial for accurate heart rate (HR) estimation.

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Vorheriges Kapitel Challenges and Opportunities in Wearable Biomedical Interfaces
Nächstes Kapitel Introduction to Compressive Sampling (CS)
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Metadaten
Titel
Adaptive Sampling for Ultra-Low-Power Electrocardiogram (ECG) Readouts
verfasst von
Venkata Rajesh Pamula
Chris Van Hoof
Marian Verhelst
Copyright-Jahr
2019
Buch
Analog-and-Algorithm-Assisted Ultra-low Power Biosignal Acquisition Systems

Print ISBN: 978-3-030-05869-2

Electronic ISBN: 978-3-030-05870-8

Copyright-Jahr: 2019

DOI
https://doi.org/10.1007/978-3-030-05870-8_2

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