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.

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.

Biopotential Readout Circuits for Portable Acquisition Systems describes one of the main building blocks of such miniaturized biomedical signal acquisition systems. The focus of this book is on the implementation of low-power and high-performance integrated circuit building blocks that can be used to extract biopotential signals from conventional biopotential electrodes. New instrumentation amplifier architectures are introduced and their design is described in detail. These amplifiers are used to implement complete acquisition demonstrator systems that are a stepping stone towards practical miniaturized and low-power systems.

Biological signal sensing and processing has greatly improved our understanding about the body. With the increased number of high quality biosignals that can be sensed from the body, more efficient sensing and processing systems are detrimental to meet requirements of high bandwidth data measurement and processing in power and area limited settings. Power limitation is more and more stringent with the goal of making unobtrusive wearable/implantable devices, where the battery should be long-lasting (e.g., weeks) and as small as possible at the same time. A conventional wearable/implantable biological signal sensing system includes analog-front-end to measure a biosignal, analog-to-digital converter for conversion of the measured signal, and radio to transmit the digitized signal. The most power-hungry block among them is radio, where the power consumption increases with data bandwidth. To overcome the radio power domination, physiologically relevant information can be extracted on sensing system, which would significantly reduce the transferred data bandwidth. Notably, while achieving radio power savings, the accuracy of the on-chip processing should be high. To achieve ultra-low power and high accuracy on-chip processing in resource limiting settings, the dissertation presents two ways. The first path focuses on implementation of a high accuracy digital biological signal processing algorithm in the analog signal processing (ASP) domain. Presented ASP implementation of a high accuracy algorithm achieves high electrocardiogram (ECG) feature detection with the lowest power consumption reported. In the second path, a novel biosignal processing algorithm with physical roots is introduced for intracortical neural spike and ECG feature detection. Moreover, a physical implementation of the developed algorithm with physical computation elements is designed and validated against public and custom datasets. The algorithm with physical origins achieves better signal enhancement and feature detection than widely used ECG and intracortical neural signal enhancement algorithms. Additionally, its ultra-low power physical implementation offers real-time operation while not compromising accuracy. In the dissertation, first, algorithm-level discussions are presented, which are followed by circuit design discussions. Before going into details of algorithms, in Introduction, significance of real-time and accurate ultra-low power on-chip processing is emphasized.

This book provides, for the first time, a broad and deep treatment of the fields of both ultra low power electronics and bioelectronics. It discusses fundamental principles and circuits for ultra low power electronic design and their applications in biomedical systems. It also discusses how ultra energy efficient cellular and neural systems in biology can inspire revolutionary low power architectures in mixed-signal and RF electronics. The book presents a unique, unifying view of ultra low power analog and digital electronics and emphasizes the use of the ultra energy efficient subthreshold regime of transistor operation in both. Chapters on batteries, energy harvesting, and the future of energy provide an understanding of fundamental relationships between energy use and energy generation at small scales and at large scales. A wealth of insights and examples from brain implants, cochlear implants, bio-molecular sensing, cardiac devices, and bio-inspired systems make the book useful and engaging for students and practicing engineers.

Generally speaking, Biosignals refer to signals recorded from the human body. They can be either electrical (e. g. Electrocardiogram (ECG), Electroencephalogram (EEG), Electromyogram (EMG), etc. ) or non-electrical (e. g. breathing, movements, etc. ). The acquisition and processing of such signals play an important role in clinical routines. They are usually considered as major indicators which provide clinicians and physicians with useful information during diagnostic and monitoring processes. In some applications, the purpose is not necessarily medical. It may also be industrial. For instance, a real-time EEG system analysis can be used to control and analyze the vigilance of a car driver. In this case, the purpose of such a system basically consists of preventing crash risks. Furthermore, in certain other appli- tions,asetof biosignals (e. g. ECG,respiratorysignal,EEG,etc. ) can be used toc- trol or analyze human emotions. This is the case of the famous polygraph system, also known as the “lie detector”, the ef ciency of which remains open to debate! Thus when one is dealing with biosignals, special attention must be given to their acquisition, their analysis and their processing capabilities which constitute the nal stage preceding the clinical diagnosis. Naturally, the diagnosis is based on the information provided by the processing system.