Ultra Low-Power Biomedical Signal Processing
Introduction
Around 40% of all human deaths are attributed to cardiovascular diseases.
A practical way to decrease the overall cardiac mortality and morbidity is to
supply patients at risk with an implantable device, known as artificial pacemaker,
that is designed to monitor the cardiac status and to regulate the
beating of the heart. Cardiac pacing has become a therapeutic tool used
worldwide with over 250,000 pacemaker implants every year.
Cardiac pacemakers include real-time sensing capacities reflecting the state
of the heart. Current pacemaker detection circuitry can be interpreted as a
cardiac electrical signal compression algorithm squeezing the time signal information
into a single event representing the cardiac activity. Future cardiac
pacing algorithms, however, are believed to take advantage of the morphological
aspects of the sensed cardiac signal, improving the analysis and the
recording of relevant cardiac activity data via implantable sensors. This will
provide, for instance, a new opportunity for monitoring and managing infarctthreatened
patients and post-infarction patients outside the hospital.
In implantable medical devices, such as pacemakers, power consumption
is critical, due to the limited power density and the longevity of currently
available portable batteries. This implies that the design of such devices has
to be optimized for very low power dissipation.
The purpose of this book is to detail the significant advances in cardiac
pacing systems and to develop novel signal processing methodologies and analog
integrated circuit techniques for low-power biomedical systems.
Biomedical signal processing
Biomedical signal processing centers on the acquisition of vital signals extracted
from biologic and physiologic systems. These signals permit us to
obtain information about the state of living systems, and therefore, their monitoring
and interpretation have significant diagnostic value for clinicians and
researchers to obtain information related to human health and diseases.
The processing of biomedical signals strongly depends on the knowledge
about the origin and the nature of the signal and poses many special properties,
which usually presents some unique problems. The reason for this is mainly
due to the complexity of the underlying biologic structures and their signals,
and the need to perform indirect, non-invasive measurements. In addition, the
detected signals are commonly corrupted with noise, and thus, the relevant
information is not “visible” and cannot be readily extracted from the raw
signal. For such reasons, advanced signal processing is usually required.
Another important aspect of biomedical signals is that the information of
interest is often a combination of features that are well localized temporally
(e.g., spikes) and others that are more diffuse (e.g., small oscillations) [1]. This
requires the use of analysis methods sufficiently versatile to handle events that
can be at opposite extremes in terms of their time–frequency localization. In
this book, we will investigate the ability of the wavelet analysis to extract
information from a biomedical signal.
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*
Introduction
Around 40% of all human deaths are attributed to cardiovascular diseases.
A practical way to decrease the overall cardiac mortality and morbidity is to
supply patients at risk with an implantable device, known as artificial pacemaker,
that is designed to monitor the cardiac status and to regulate the
beating of the heart. Cardiac pacing has become a therapeutic tool used
worldwide with over 250,000 pacemaker implants every year.
Cardiac pacemakers include real-time sensing capacities reflecting the state
of the heart. Current pacemaker detection circuitry can be interpreted as a
cardiac electrical signal compression algorithm squeezing the time signal information
into a single event representing the cardiac activity. Future cardiac
pacing algorithms, however, are believed to take advantage of the morphological
aspects of the sensed cardiac signal, improving the analysis and the
recording of relevant cardiac activity data via implantable sensors. This will
provide, for instance, a new opportunity for monitoring and managing infarctthreatened
patients and post-infarction patients outside the hospital.
In implantable medical devices, such as pacemakers, power consumption
is critical, due to the limited power density and the longevity of currently
available portable batteries. This implies that the design of such devices has
to be optimized for very low power dissipation.
The purpose of this book is to detail the significant advances in cardiac
pacing systems and to develop novel signal processing methodologies and analog
integrated circuit techniques for low-power biomedical systems.
Biomedical signal processing
Biomedical signal processing centers on the acquisition of vital signals extracted
from biologic and physiologic systems. These signals permit us to
obtain information about the state of living systems, and therefore, their monitoring
and interpretation have significant diagnostic value for clinicians and
researchers to obtain information related to human health and diseases.
The processing of biomedical signals strongly depends on the knowledge
about the origin and the nature of the signal and poses many special properties,
which usually presents some unique problems. The reason for this is mainly
due to the complexity of the underlying biologic structures and their signals,
and the need to perform indirect, non-invasive measurements. In addition, the
detected signals are commonly corrupted with noise, and thus, the relevant
information is not “visible” and cannot be readily extracted from the raw
signal. For such reasons, advanced signal processing is usually required.
Another important aspect of biomedical signals is that the information of
interest is often a combination of features that are well localized temporally
(e.g., spikes) and others that are more diffuse (e.g., small oscillations) [1]. This
requires the use of analysis methods sufficiently versatile to handle events that
can be at opposite extremes in terms of their time–frequency localization. In
this book, we will investigate the ability of the wavelet analysis to extract
information from a biomedical signal.
Download
*