Showing papers in "IEEE Engineering in Medicine and Biology Magazine in 2001"
TL;DR: The history of the database, its contents, what is learned about database design and construction, and some of the later projects that have been stimulated by both the successes and the limitations of the MIT-BIH Arrhythmia Database are reviewed.
Abstract: The MIT-BIH Arrhythmia Database was the first generally available set of standard test material for evaluation of arrhythmia detectors, and it has been used for that purpose as well as for basic research into cardiac dynamics at about 500 sites worldwide since 1980. It has lived a far longer life than any of its creators ever expected. Together with the American Heart Association Database, it played an interesting role in stimulating manufacturers of arrhythmia analyzers to compete on the basis of objectively measurable performance, and much of the current appreciation of the value of common databases, both for basic research and for medical device development and evaluation, can be attributed to this experience. In this article, we briefly review the history of the database, describe its contents, discuss what we have learned about database design and construction, and take a look at some of the later projects that have been stimulated by both the successes and the limitations of the MIT-BIH Arrhythmia Database.
3,111 citations
TL;DR: What PhysioNet offers to researchers is discussed, some of the technology needed to support these functions are described, and observations gleaned from the organisation's first year of service are concluded.
Abstract: Free access to a signals archive and a signal processing/analysis software library fosters online collaboration. This article aims to introduce PhysioNet as a resource to the biomedical research community. After a capsule summary of its history and goals, we discuss what PhysioNet offers to researchers, describe some of the technology needed to support these functions, and conclude with observations gleaned from PhysioNet's first year of service.
331 citations
TL;DR: Close-loop blood glucose regulation algorithms that use the intravenous route for insulin delivery to insulin-dependent diabetic patients and classical control methods and advanced algorithms using implicit knowledge or explicit models are examined are examined.
Abstract: This article discusses closed-loop blood glucose regulation algorithms that use the intravenous route for insulin delivery to insulin-dependent diabetic patients. Classical control methods and advanced algorithms using implicit knowledge or explicit models (empirical, fundamental, or "gray-box") of the diabetic patient are examined. Current research on characterizing patient variability is presented, in the context of a model predictive controller able to adjust to changes in patient glucose and insulin sensitivity.
259 citations
TL;DR: The relative movement of the muscle under the recording electrodes when the joint angle changes for the set of muscles most frequently investigated in gait analysis is investigated.
Abstract: It is the purpose of this article (a) to show the influence of electrode location on EMG amplitude and spectral variables for simulated and real signals for different muscles of the thigh and leg, (b) to investigate the relative movement of the muscle under the recording electrodes when the joint angle changes for the set of muscles most frequently investigated in gait analysis, and (c) to illustrate how different electrode locations may lead to different interpretations of the muscle activity investigated with amplitude and spectral analysis of the surface EMG signal. The study has been carried out on the following muscles of the leg and thigh: rectus femoris, vastus lateralis, vastus medialis, biceps femoris, semitendinosus, tibialis anterior, gastrocnemius lateralis, and gastrocnemius medialis.
188 citations
TL;DR: The SIESTA project had two major goals: developing new tools for analyzing computer-based sleep recordings and creating a reference database for sleep-related features, which have been reached, although validation and fine tuning of the sleep analyzer is still on-going.
Abstract: The SIESTA project had two major goals: developing new tools for analyzing computer-based sleep recordings and creating a reference database for sleep-related features. Basically, both goals have been reached, although validation and fine tuning of the sleep analyzer is still on-going. Investigations on the Web interface will be finished soon and a documentation of the database (including a CD-ROM with all test forms and all clinical, psychometric and actigraphic data as well as all R&K-scorings) will be published. Besides its scientific impact, the SIESTA project also emphasizes two other important aspects: the need of national and international cooperation between different experts and disciplines and the importance of standardized methods in scientific and clinical research.
176 citations
TL;DR: The authors review the subcutaneous closed- and partially closed-loop strategies that have been proposed and tested in recent years for insulin-dependent diabetes therapy.
Abstract: Discusses closed-loop and partially closed-loop control strategies for insulin delivery and measuring glucose concentration. The authors review the subcutaneous closed- and partially closed-loop strategies that have been proposed and tested in recent years for insulin-dependent diabetes therapy. Focus is on control, modeling, and information technology aspects, and future directions of research are also outlined. This survey complements earlier reviews concerning control approaches and application of computers in diabetes care.
158 citations
TL;DR: Portable grasping and walking neuroprostheses, developed by the Automatic Control Laboratory at the Swiss Federal Institute of Technology Zurich and the Paraplegic Center at the University Hospital Balgrist are discussed.
Abstract: Deals with improving the quality of life in stroke/spinal cord injury subjects with rapid prototyping and portable FES systems. Portable grasping and walking neuroprostheses, developed by the Automatic Control Laboratory at the Swiss Federal Institute of Technology Zurich (ETHZ) and the Paraplegic Center at the University Hospital Balgrist (ParaCare) are discussed. Both neuroprostheses employ surface stimulation technology and are currently used by a number of subjects in daily living activities.
144 citations
TL;DR: A DNA compression algorithm based on approximate matching that gives the best compression results on standard benchmark DNA sequences is presented and the results are compared with the two most effective compression algorithms for DNA sequences (Biocompress-2 and Cfact).
Abstract: We present a DNA compression algorithm, GenCompress, based on approximate matching that gives the best compression results on standard benchmark DNA sequences. We present the design rationale of GenCompress based on approximate matching, discuss details of the algorithm, provide experimental results, and compare the results with the two most effective compression algorithms for DNA sequences (Biocompress-2 and Cfact).
102 citations
TL;DR: The first steps toward the development of an autonomous anesthesia system at the authors' laboratory are presented and the controllers are implemented on a real-time platform and tested on humans to quantify the benefits that may result from their use in routine practice.
Abstract: Automatic controllers capable of regulating multiple patient outputs for higher-quality anesthesia treatment are described. The first steps toward the development of an autonomous anesthesia system at the authors' laboratory are presented. The controllers are implemented on a real-time platform and tested on humans to quantify the benefits that may result from their use in routine practice. To date, the staff at the University Hospital in Bern can rely on controllers that regulate six different patient outputs. Overall, more than 150 patients were treated with closed-loop controllers during general anesthesia. These controllers regulate O/sub 2/, CO/sub 2/, and inspired and expired anesthetic gas concentrations in the breathing system, as well as mean arterial pressure and depth of hypnosis through bispectral index.
101 citations
TL;DR: In this article the characteristics of traditional and novel algorithms for the detection of the onset (and offset) of muscle contraction using EMG signals have been briefly summarized.
Abstract: In this article the characteristics of traditional and novel algorithms for the detection of the onset (and offset) of muscle contraction using EMG signals have been briefly summarized. As is evident from these descriptions, many studies have been carried out in the last few years in order to improve the accuracy of the detection and to make the performance of the algorithm less dependent on the skill of the operator.
95 citations
TL;DR: The autoregressive multivariate techniques described are based on simplified models of the entire cardiovascular system and satisfy both the requirements of not perturbing the system and providing a closed-loop evaluation of the interactions between R-R interval and arterial blood pressure.
Abstract: The autoregressive multivariate techniques described are based on simplified models of the entire cardiovascular system and satisfy both the requirements of not perturbing the system and providing a closed-loop evaluation of the interactions between R-R interval and arterial blood pressure. The closed-loop analysis is also able to calculate some of the indices previously used for baroreflex quantification, allowing for a direct comparison of the different approaches.
TL;DR: A new robotic system for assisting surgeons in performing minimally invasive surgical procedures designed for collaborative operation between the surgeon and the robot and has the following attributes: quick interchangeable end tools, programmable collaboration with the surgeon, and coordinated motion of multiple robotic devices for performing complex procedures.
Abstract: Presents the design and implementation of a new robotic system for assisting surgeons in performing minimally invasive surgical procedures. This system is designed for collaborative operation between the surgeon and the robot. In addition, it has the following attributes: quick interchangeable end tools, programmable collaboration with the surgeon, and coordinated motion of multiple robotic devices for performing complex procedures. Two such devices have been built, with a variety of end-tools. The system has demonstrated direct manual operation and surgeon-supervised autonomous stitching and knot tying. In addition to the mechanical design and kinematic analysis, several autonomous knot-tying algorithms and the associated kinematic requirements are discussed. Note that, as the focus here is on the robot design and control, other important aspects on the clinical deployment of surgical robots such as safety, fault detection and amelioration, man-machine interface design, etc., are not addressed here.
TL;DR: The advanced EMG processing was next applied to dynamic EMG-torque estimation about the elbow joint, and results showed that improved EMG amplitude estimates led to improved EMg-torques estimates.
Abstract: Processing nonstationary EMG signals for applications in prosthesis control, biofeedback, and joint torque estimation, is discussed.
TL;DR: It is shown how one can apply the region-based level-set technique for segmenting the brain using fast techniques, and the system used the fuzzy clustering method for computing the fuzzy membership values, which were used in the regional speed computation.
Abstract: Region-based level-set snakes are a very powerful technique for segmenting white matter/grey matter in MR slices of human brain. We showed how one can apply the region-based level-set technique for segmenting the brain using fast techniques. The system used the fuzzy clustering method for computing the fuzzy membership values, which were used in the regional speed computation. Recently, the authors have developed a mathematical morphology-based speed control function that acts as a regularizer for making the propagation more robust and leak free. It would also be worth exploring how either the neural network or learning models would do in terms of the performance evaluation if clustering was to be replaced.
TL;DR: The authors describe a method that uses an applied inductive learning (IL) technique for determining synergies between movement of joints while reaching that relies on an hierarchical mutual information classifier algorithm.
Abstract: The control method proposed in this article is based on the following hypotheses: once the task and preferred strategy for movement are selected, then the voluntary (natural) control drives the proximal segment (shoulder joint), and the synergistic (artificial) control drives the distal segment (elbow joint). This concept relies on a simulation of the natural control of the extremities. The literature suggests, and the authors confirmed in experiments, that reproducible synergies between shoulder and elbow joint movement exist. The authors describe a method that uses an applied inductive learning (IL) technique for determining synergies between movement of joints while reaching. This method relies on an hierarchical mutual information classifier algorithm. The synergy is shown in a map of flexion/extension (F/E) angular velocities at the shoulder and elbow joints. Two other shoulder joint rotations (abduction/adduction and humeral rotation) are independent of the F/E synergy, thus indicating that the results of this study are applicable to three-dimensional (3-D) movement.
TL;DR: The methodological basis of the sequence technique, based on the computerized scanning of beat-to-beat series of systolic (S)BP and RRI values in search of spontaneous sequences of three or more consecutive heart beats, is focused on.
Abstract: Among the modern approaches for evaluating the baroreflex sensitivity (BRS), one of the most frequently employed is the sequence technique. This method is based on the computerized scanning of beat-to-beat series of systolic (S)BP and RRI values in search of spontaneous sequences of three or more consecutive heart beats in which SBP progressively increases and, usually with a one-beat delay, RRI progressively lengthens (RRI+/SBP+ sequences) or, vice-versa, SBP progressively decreases and RRI shortens (RRI-/SBP- sequences). The slope of the regression line between the SBP and RRI values included in each sequence is traditionally taken as a measure of BRS. A large number of these baroreflex sequences spontaneously occurs during daily life activities, thus providing a detailed profile of BRS modulation over time. In this article, we focus on the methodological basis of this technique. In particular, by taking advantage of experimental data collected over the years, we explore the characteristics of the SBP and RRI patterns within each spontaneous sequence. This analysis is followed by methodological considerations aimed at clarifying the perspective from which the sequence technique evaluates BRS, as compared with other techniques. Finally, the latest enhancements of the sequence technique, leading to the quantification of other aspects of baroreflex function apart from BRS, are also described.
TL;DR: In this article, a high-resolution reduced interference time-frequency distribution referred to as the B-distribution (BD) was used for seizure detection in newborns using electroencephalogram (EEG).
Abstract: Techniques previously designed for seizure detection in newborns using the electroencephalogram (EEG) have been relatively inefficient due to their assumption of local stationarity of the EEG. To overcome the problem raised by the nonstationarity of the EEG signal, current methods are extended to a time-frequency approach. This allows the analysis and characterization of the different newborn EEG patterns that are intended to be the first step toward an automatic time-frequency seizure detection and classification. An in-depth analysis of both the autocorrelation and spectrum seizure detection techniques identified the detection criteria that can be extended to the time-frequency domain. The selected method uses a high-resolution reduced interference time-frequency distribution referred to as the B-distribution (BD). Here, the authors present the various patterns of observed time-frequency seizure signals and relate them to current knowledge of seizures. In particular, initial results indicate that a quasilinear instantaneous frequency (IF) can be used as a critical feature of the EEG seizure characteristics.
TL;DR: The present technique of determining MNF has the advantage that it is possible to determine the frequency content of the ME signal during short and nonstationary contractions, and confirmed earlier studies that MNF is independent of angular velocity.
Abstract: A time-frequency approach using wavelets to study movements at different angular velocities is considered. The authors summarized the application of the continuous wavelet transform (CWT) to the analysis of the surface myoelectric (ME) signal. The present technique of determining MNF has the advantage that it is possible to determine the frequency content of the ME signal during short and nonstationary contractions. In addition, the CWT method is very reliable for the analysis of nonstationary biological signals and does not require any smoothing function as do methods based on Wigner-Ville. However, using time-frequency methods involves two main tradeoffs: i.e., potential increases in performance for a given application versus computational complexity and storage requirements. Our results confirmed earlier studies that MNF is independent of angular velocity.
TL;DR: In this paper, the authors describe the use of adaptive signal processing techniques for removing artifacts from newborn electroencephalogram (EEG) signals, which is necessary before attempting a fine time-frequency analysis of EEG rhythmical activities, such as electrical seizures, corrupted by high amplitude signals.
Abstract: Neurological disease or dysfunction in newborn infants is often first manifested by seizures. Prolonged seizures can result in impaired neurodevelopment or even death. In adults, the clinical signs of seizures are well defined and easily recognized. In newborns, however, the clinical signs are subtle and may be absent or easily missed without constant close observation. This article describes the use of adaptive signal processing techniques for removing artifacts from newborn electroencephalogram (EEG) signals. Three adaptive algorithms have been designed in the context of EEG signals. This preprocessing is necessary before attempting a fine time-frequency analysis of EEG rhythmical activities, such as electrical seizures, corrupted by high amplitude signals. After an overview of newborn EEG signals, the authors describe the data acquisition set-up. They then introduce the basic physiological concepts related to normal and abnormal newborn EEGs and discuss the three adaptive algorithms for artifact removal. They also present time-frequency representations (TFRs) of seizure signals and discuss the estimation and modeling of the instantaneous frequency related to the main ridge of the TFR.
TL;DR: After 3 decades of controversial reports on the use of thermal imaging in the diagnosis of breast cancer, the authors have shown that dynamic infrared imaging, using a plausible pathophysiological model and up-to-date infrared equipment, can distinguish between noncancerous and cancerous breasts with a highly impressive sensitivity and specificity.
Abstract: This study demonstrated the following 3 new important concepts. 1) Dynamic imaging can yield meaningful clinical diagnostic information objectively, without need for human expertise. The same methodology can be applicable to any quantitative pathophysiological assessment. 2) Using classical FFT and elementary statistics (Student's t tests and z-value statistics, or alpha and beta statistics), the authors have shown how to reduce thousands of observations to a single quantitative clinical diagnostic parameter. This approach should be applicable to any multiparametric diagnostic technique. 3) After 3 decades of controversial reports on the use of thermal imaging in the diagnosis of breast cancer, the authors have shown that dynamic infrared imaging (in contrast to static thermal imaging), using a plausible pathophysiological model and up-to-date infrared equipment, can distinguish between noncancerous and cancerous breasts with a highly impressive sensitivity and specificity.
TL;DR: Preliminary results suggest that the information-theoretic approach suitable for studying synchronization phenomena in experimental time series has a promising potential for localization of epileptic foci and anticipation of approaching seizures.
Abstract: An information-theoretic approach suitable for studying synchronization phenomena in experimental time series has been applied in analysis of EEG recordings of epileptic patients. Transient phenomena leading to seizures have been characterized by increased synchronization (local and between areas) and asymmetry in information flow (the area of the epileptogenic focus drives and synchronizes adjacent areas). Although the results should be regarded as preliminary, they suggest that the method has a promising potential for localization of epileptic foci and anticipation of approaching seizures.
TL;DR: The work is based on experiences from the SIESTA project which identified sleep disorders and aspects to consider when building a database are presented.
Abstract: Aspects to consider when building a database are presented The work is based on experiences from the SIESTA project which identified sleep disorders
TL;DR: A new fully automatic estimation technique adaptively working on SEMG signal characteristics is presented, which could represent an indirect assessment of muscular force obtained without using invasive measurement techniques.
Abstract: Electrical signals recorded by means of surface electromyography (SEMG) contain some useful information for a better understanding of strategies underlying human movement. In particular, a great contribution to biomechanic studies may be provided by a correct estimation of the amplitude of SEMG signals that is related to the force exerted by muscles. This information could, in fact, represent an indirect assessment of muscular force obtained without using invasive measurement techniques. This article presents a new fully automatic estimation technique adaptively working on SEMG signal characteristics. The discussion of the theoretical background of the estimator together with a feasibility study demonstrates the usefulness of its application. An example of the application to signals recorded during dynamic protocols is also shown.
TL;DR: Several examples of machine learning techniques used in a genomic context are given, including clustering methods to organize microarray expression data, support vector machines to predict protein function, Bayesian networks to predict subcellular localization, and decision trees to optimize target selection for high-throughput proteomics.
Abstract: Biological research is becoming increasingly database driven, motivated, in part, by the advent of large-scale functional genomics and proteomics experiments such as those comprehensively measuring gene expression. These provide a wealth of information on each of the thousands of proteins encoded by a genome. Consequently, a challenge in bioinformatics is integrating databases to connect this disparate information as well as performing large-scale studies to collectively analyze many different data sets. This approach represents a paradigm shift away from traditional single-gene biology, and it often involves statistical analyses focusing on the occurrence of particular features (e.g., folds, functions, interactions, pseudogenes, or localization) in a large population of proteins. Moreover, the explicit application of machine learning techniques can be used to discover trends and patterns in the underlying data. In this article, we give several examples of these techniques in a genomic context: clustering methods to organize microarray expression data, support vector machines to predict protein function, Bayesian networks to predict subcellular localization, and decision trees to optimize target selection for high-throughput proteomics.
TL;DR: The authors briefly discuss the need for a generally applicable method for a mathematical description (parameterization) of the signal, which would be directly related to the heritage of the traditional EEG analysis, and discuss application of the MP algorithm.
Abstract: Seventy years since the first recording of the human electroencephalogram (EEG), visual analysis of raw EEG traces is still the major clinical tool and point of reference for other methods, in spite of its inherent limitations: low repeatability and high cost. Seven years since the introduction of the matching pursuit (MP) algorithm, the authors have collected evidence suggesting that adaptive time-frequency approximation is a good candidate for a universal high-resolution parameterization of EEG data, compatible with the visual and spectral analysis, and applicable to a large class of problems. Here, the authors briefly discuss the need for a generally applicable method for a mathematical description (parameterization) of the signal, which would be directly related to the heritage of the traditional EEG analysis. In this context the authors discuss application of the MP algorithm. They present recent advances in analysis of sleep EEGs and discuss earlier works on event-related potentials and epileptic recordings.
TL;DR: Some requirements imposed on data format specifications derived from different biosignal recording environments are presented and the merits of different formats are discussed and some views about future developments are given.
Abstract: This article presents some requirements imposed on data format specifications derived from different biosignal recording environments. This is followed by a review of four particularly interesting data formats and some notes about other specifications. Finally, the merits of different formats are discussed and some views about future developments are given.
TL;DR: The applicability and reproducibility of the baroreflex gain estimated by the use of power spectral analysis with traditional invasive techniques that induce ramp changes in mean arterial pressure in conscious rabbits is assessed.
Abstract: One of the new methods to evaluate the sensitivity of the baroreceptor-heart rate (HR) reflex involves the use of power spectral analysis to calculate the transfer function between blood pressure and HR. We assess the applicability and reproducibility of the baroreflex gain estimated by this method with traditional invasive techniques that induce ramp changes in mean arterial pressure (MAP) in conscious rabbits. Renal sympathetic nerve activity recordings are used to identify the mid-frequency band, and we also identify coherent fluctuations of MAP and HR with a 1.8 s phase delay, consistent with a baroreflex relationship and therefore appropriate to estimate the cross spectral transfer function.
TL;DR: This review presents current knowledge about the effects of fluid mechanical shearing stress on receptor-mediated cellular interactions relevant to the processes of inflammation and cancer metastasis, two of the most critical pathological disorders affecting humankind today.
Abstract: This review presents current knowledge about the effects of fluid mechanical shearing stress on receptor-mediated cellular interactions relevant to the processes of inflammation and cancer metastasis, two of the most critical pathological disorders affecting humankind today. Specifically, the multistep sequential models describing leukocyte-endothelium/platelet and tumor cell-endothelium/platelet interactions are discussed, with an emphasis on the similarities and differences amongst these processes. Elucidation of the complex interplay among blood flow, cell adhesion, and vascular biology at the molecular level will have an impact in the search for cellular/tissue engineering based therapeutic interventions against inflammatory and metastastic disorders.
TL;DR: A model to represent the LF and VLF components in an appropriate manner, compatible with the relevant physiology, is found and its performance within a comprehensive model of the cardiovascular regulatory system is considered.
Abstract: The observed fluctuations in heart rate and blood pressure are meaningful rhythmical fluctuations that reflect useful information about autonomic regulation. These rhythmical fluctuations, known as heart rate variability (HRV) and blood pressure variation (BPV), are normally grouped into three major components: (i) the HF component, around 0.25 Hz, in synchrony with respiratory rate; (ii) the LF component, generally centered around 0.1 Hz, which is attributed to the sympathetic activity and the closed-loop controlling action of cardiovascular regulation; (iii) the VLF component, around 0.04 Hz, which is probably due to the vasorhythmicity thermoregulatory system or to humoral regulations. Unlike the HF component, there is still considerable controversy with regard to the origin of LF and VLF components, the so-called Mayer waves. Finding a model to represent the LF and VLF components in an appropriate manner, compatible with the relevant physiology, is the objective of this article. For this purpose, we first briefly review the underlying physiological mechanisms. Then, an appropriate mathematical representation for each mechanism is demonstrated and its performance within a comprehensive model of the cardiovascular regulatory system is considered. Finally, by comparing with the experimental results, we evaluate the closeness of the proposed representations to the actual observations.