Biomedical Signal Analysis: A Case-Study Approach IEEE Press Series on Biomedical Engineering
Preface
Background and Motivation
The establishment of the clinical electrocardiograph (ECG) by the Dutch physician Willem Einthoven in 1903 marked the beginning of a new era in medical diagnostic
techniques, including the entry of electronics into health care. Since then, electronics, and subsequently computers, have become integral components of biomedical signal
analysis systems, performing a variety of tasks from data acquisition and prepro- cessing for removal of artifacts to feature extraction and interpretation. Electronic
instrumentation and computers have been applied to investigate a host of biologi- cal and physiological systems and phenomena, such as the electrical activity of the
cardiovascular system, the brain, the neuromuscular system, and the gastric system; pressure variations in the cardiovascular system; sound and vibration signals from the cardiovascular, the musculo-skeletal, and the respiratory systems; and magnetic fields of the brain, to name a few.
The primary step in investigations of physiological systems requires the devel- opment of appropriate sensors and instrumentation to transduce the phenomenon of interest into a measurable electrical signal. The next step of analysis of the signals, however, is not always an easy task for a physician or life-sciences specialist. The clinically relevant information in the signal is often masked by noise and interference, and the signal features may not be readily comprehensible by the visual or auditory systems of a human observer. Heart sounds, for example, have most of their energy
at or below the threshold of auditory perception of most humans; the interference pat- terns of a surface electromyographic (EMG) signal are too complex to permit visual
analysis. Some repetitious or attention-demanding tasks, such as on-line monitoring of the ECG of a critically ill patient with cardiac rhythm problems, could be uninter-
esting and tiring for a human observer. Furthermore, the variability present in a given type of signal from one subject to another, and the inter-observer variability inherent
in subjective analysis performed by physicians or analysts make consistent under- standing or evaluation of any phenomenon difficult, if not impossible. These factors created the need not only for improved instrumentation, but also for the development of methods for objective analysis via signal processing algorithms implemented in electronic hardware or on computers.
Processing of biomedical signals, until a few years ago, was mainly directed toward filtering for removal of noise and power-line interference; spectral analysis to understand the frequency characteristics of signals; and modeling for feature representation and parameterization. Recent trends have been toward quantitative or
objective analysis of physiological systems and phenomena via signal analysis. The field of biomedical signal analysis has advanced to the stage of practical application of signal processing and pattern analysis techniques for efficient and improved non- invasive diagnosis, on-line monitoring of critically ill patients, and rehabilitation and sensory aids for the handicapped. Techniques developed by engineers are gaining wider acceptance by practicing clinicians, and the role of engineering in diagnosis and treatment is gaining much-deserved respect.
The major strength in the application of computers in biomedical signal analysis lies in the potential use of signal processing and modeling techniques for quantitative
or objective analysis. Analysis of signals by human observers is almost always accompanied by perceptual limitations, inter-personal variations, errors caused by fatigue, errors caused by the very low rate of incidence of a certain sign of abnormality, environmental distractions, and so on. The interpretation of a signal by an expert bears the weight of the experience and expertise of the analyst; however, such analysis is almost always subjective. Computer analysis of biomedical signals, if performed with the appropriate logic, has the potential to add objective strength to the interpretation of
the expert. It thus becomes possible to improve the diagnostic confidence or accuracy of even an expert with many years of experience. This approach to improved health
care could be labeled as computer-aided diagnosis.
Developing an algorithm for biomedical signal analysis, however, is not an easy
task; quite often, it might not even be a straightforward process. The engineer or
computer analyst is often bewildered by the variability of features in biomedical signals and systems, which is far higher than that encountered in physical systems
or observations. Benign diseases often mimic the features of malignant diseases; malignancies may exhibit a characteristic pattern, which, however, is not always guaranteed to appear. Handling all of the possibilities and degrees of freedom in a biomedical system is a major challenge in most applications. Techniques proven to work well with a certain system or set of signals may not work in another seemingly similar situation.
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Preface
Background and Motivation
The establishment of the clinical electrocardiograph (ECG) by the Dutch physician Willem Einthoven in 1903 marked the beginning of a new era in medical diagnostic
techniques, including the entry of electronics into health care. Since then, electronics, and subsequently computers, have become integral components of biomedical signal
analysis systems, performing a variety of tasks from data acquisition and prepro- cessing for removal of artifacts to feature extraction and interpretation. Electronic
instrumentation and computers have been applied to investigate a host of biologi- cal and physiological systems and phenomena, such as the electrical activity of the
cardiovascular system, the brain, the neuromuscular system, and the gastric system; pressure variations in the cardiovascular system; sound and vibration signals from the cardiovascular, the musculo-skeletal, and the respiratory systems; and magnetic fields of the brain, to name a few.
The primary step in investigations of physiological systems requires the devel- opment of appropriate sensors and instrumentation to transduce the phenomenon of interest into a measurable electrical signal. The next step of analysis of the signals, however, is not always an easy task for a physician or life-sciences specialist. The clinically relevant information in the signal is often masked by noise and interference, and the signal features may not be readily comprehensible by the visual or auditory systems of a human observer. Heart sounds, for example, have most of their energy
at or below the threshold of auditory perception of most humans; the interference pat- terns of a surface electromyographic (EMG) signal are too complex to permit visual
analysis. Some repetitious or attention-demanding tasks, such as on-line monitoring of the ECG of a critically ill patient with cardiac rhythm problems, could be uninter-
esting and tiring for a human observer. Furthermore, the variability present in a given type of signal from one subject to another, and the inter-observer variability inherent
in subjective analysis performed by physicians or analysts make consistent under- standing or evaluation of any phenomenon difficult, if not impossible. These factors created the need not only for improved instrumentation, but also for the development of methods for objective analysis via signal processing algorithms implemented in electronic hardware or on computers.
Processing of biomedical signals, until a few years ago, was mainly directed toward filtering for removal of noise and power-line interference; spectral analysis to understand the frequency characteristics of signals; and modeling for feature representation and parameterization. Recent trends have been toward quantitative or
objective analysis of physiological systems and phenomena via signal analysis. The field of biomedical signal analysis has advanced to the stage of practical application of signal processing and pattern analysis techniques for efficient and improved non- invasive diagnosis, on-line monitoring of critically ill patients, and rehabilitation and sensory aids for the handicapped. Techniques developed by engineers are gaining wider acceptance by practicing clinicians, and the role of engineering in diagnosis and treatment is gaining much-deserved respect.
The major strength in the application of computers in biomedical signal analysis lies in the potential use of signal processing and modeling techniques for quantitative
or objective analysis. Analysis of signals by human observers is almost always accompanied by perceptual limitations, inter-personal variations, errors caused by fatigue, errors caused by the very low rate of incidence of a certain sign of abnormality, environmental distractions, and so on. The interpretation of a signal by an expert bears the weight of the experience and expertise of the analyst; however, such analysis is almost always subjective. Computer analysis of biomedical signals, if performed with the appropriate logic, has the potential to add objective strength to the interpretation of
the expert. It thus becomes possible to improve the diagnostic confidence or accuracy of even an expert with many years of experience. This approach to improved health
care could be labeled as computer-aided diagnosis.
Developing an algorithm for biomedical signal analysis, however, is not an easy
task; quite often, it might not even be a straightforward process. The engineer or
computer analyst is often bewildered by the variability of features in biomedical signals and systems, which is far higher than that encountered in physical systems
or observations. Benign diseases often mimic the features of malignant diseases; malignancies may exhibit a characteristic pattern, which, however, is not always guaranteed to appear. Handling all of the possibilities and degrees of freedom in a biomedical system is a major challenge in most applications. Techniques proven to work well with a certain system or set of signals may not work in another seemingly similar situation.
Download
*