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Über dieses Buch

This book discusses the design and implementation aspects of ultra-low power biosignal acquisition platforms that exploit analog-assisted and algorithmic approaches for power savings.The authors describe an approach referred to as “analog-and-algorithm-assisted” signal processing.This enables significant power consumption reductions by implementing low power biosignal acquisition systems, leveraging analog preprocessing and algorithmic approaches to reduce the data rate very early in the signal processing chain.They demonstrate savings for wearable sensor networks (WSN) and body area networks (BAN), in the sensors’ stimulation power consumption, as well in the power consumption of the digital signal processing and the radio link. Two specific implementations, an adaptive sampling electrocardiogram (ECG) acquisition and a compressive sampling (CS) photoplethysmogram (PPG) acquisition system, are demonstrated.First book to present the so called, “analog-and-algorithm-assisted” approaches for ultra-low power biosignal acquisition and processing platforms;
Covers the recent trend of “beyond Nyquist rate” signal acquisition and processing in detail, including adaptive sampling and compressive sampling paradigms;
Includes chapters on compressed domain feature extraction, as well as acquisition of photoplethysmogram, an emerging optical sensing modality, including compressive sampling based PPG readout with embedded feature extraction;
Discusses emerging trends in sensor fusion for improving the signal integrity, as well as lowering the power consumption of biosignal acquisition systems.



Chapter 1. Challenges and Opportunities in Wearable Biomedical Interfaces

This chapter provides an overview of the challenges and opportunities in wearable biomedical interfaces. Specifically, the challenges involved in acquiring biosignals with high fidelity in limited power budgets are highlighted. This chapter also introduces electrocardiogram (ECG) and photoplethysmogram (PPG) signal acquisition and processing as modalities for estimating the cardiovascular state. Assisted signal processing architectures, specifically analog and algorithmic assisted approaches, are introduced as opportunities to mitigate the challenges in low-power biosignal acquisition platforms. Finally, the organization of the rest of the chapters of the book is presented.
Venkata Rajesh Pamula, Chris Van Hoof, Marian Verhelst

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

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.
Venkata Rajesh Pamula, Chris Van Hoof, Marian Verhelst

Chapter 3. Introduction to Compressive Sampling (CS)

This chapter provides an overview of compressive sampling (CS), introducing both the signal acquisition and reconstruction protocols. A novel, computationally light, overlapped window reconstruction algorithm is introduced to circumvent the problem of edge artifacts in conventional CS reconstruction. The proposed approach is shown to reduce the central processing unit (CPU) execution time by a factor of 2.4 without degradation of reconstruction accuracy compared to a traditional longer window reconstruction approach for photoplethysmogram (PPG) signals. Finally, this chapter also presents the state-of-the-art CS implementations for biosignal acquisition and processing.
Venkata Rajesh Pamula, Chris Van Hoof, Marian Verhelst

Chapter 4. Compressed Domain Feature Extraction

This chapter introduces feature extraction techniques that extract relevant features of interest from compressively sampled biosignals directly from compressed data circumventing the computationally complex reconstruction process. State-of-the-art compressed domain feature extraction process for that leverages on Johnson–Lindenstrauss lemma is presented. It is also shown that such approach is inadequate in the context of feature extraction for compressively sampled photoplethysmogram (PPG) signals. Lomb-Scargle periodogram (LSP) is introduced as an alternate approach for extracting the spectral features from compressively sampled PPG signals, which is then used to estimate the average heart rate (HR) and heart rate variability (HRV). The efficacy of the proposed approach is demonstrated through simulations which indicate that the average HR estimated is accurate within ±5 beats per minute (bpm) while HRV exhibits a correlation coefficient of > 0.90 at 30 × compression ratio (CR) compared to time domain HR and HRV estimation performed on Nyquist sampled PPG signals.
Venkata Rajesh Pamula, Chris Van Hoof, Marian Verhelst

Chapter 5. A Low-Power Compressive Sampling (CS) Photoplethysmogram (PPG) Readout with Embedded Feature Extraction

A compressive sampling (CS) photoplethysmogram (PPG) readout ASIC with embedded feature extraction to estimate heart rate (HR) directly from compressively sampled data is presented in this chapter. The ASIC incorporates a low-power analog front end (AFE), comprising of a transimpedance amplifier (TIA), switched integrator (SI), and a 12-bit successive approximation register (SAR) analog-to-digital converter (ADC) together with a digital back end (DBE) with embedded feature extraction unit (FEU) to estimate the average heart rate (HR) over a 4 s interval directly from compressively sampled PPG data. Trade-offs involved in TIA design for PPG readouts, in terms of stability, noise, and power consumption, are discussed in detail. The implemented ASIC supports uniform sampling mode (1 × compression) as well as CS modes with compression ratios of 8 ×, 10 ×, and 30 ×. Feature extraction to estimate the average HR is performed using least-squares spectral fitting through lomb-Scargle periodogram (LSP). The ASIC, implemented in a 0.18 μm CMOS process, consumes 172 μW of power from a 1.2 V supply while reducing the relative LED driver power consumption by up to 30 times without significant loss of relevant information for accurate HR estimation.
Venkata Rajesh Pamula, Chris Van Hoof, Marian Verhelst

Chapter 6. Conclusions and Future Work

This chapter summarizes various aspects discussed in detail in this book. Specific attention is paid in highlighting the key contributions—analog and algorithm assisted signal processing architectures for ultra-low-power biosignal acquisition and processing. These aspects are demonstrated through ASIC implementations of adaptive sampling for electrocardiogram (ECG) and compressive sampling for photoplethysmogram (PPG), respectively. This chapter also presents the opportunities to further the work presented in this book in terms of motion artifact reduction in PPG acquisition and combining ultra-low-power ECG and PPG acquisition for cuffless blood pressure (BP) estimation.
Venkata Rajesh Pamula, Chris Van Hoof, Marian Verhelst


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