Showing papers in "IEEE Engineering in Medicine and Biology Magazine in 2002"

The principles of software QRS detection

Abstract: The QRS complex is the most striking waveform within the electrocardiogram (ECG). Since it reflects the electrical activity within the heart during the ventricular contraction, the time of its occurrence as well as its shape provide much information about the current state of the heart. Due to its characteristic shape it serves as the basis for the automated determination of the heart rate, as an entry point for classification schemes of the cardiac cycle, and often it is also used in ECG data compression algorithms. In that sense, QRS detection provides the fundamentals for almost all automated ECG analysis algorithms. Software QRS detection has been a research topic for more than 30 years. The evolution of these algorithms clearly reflects the great advances in computer technology. Within the last decade many new approaches to QRS detection have been proposed; for example, algorithms from the field of artificial neural networks genetic algorithms wavelet transforms, filter banks as well as heuristic methods mostly based on nonlinear transforms. The authors provide an overview of these recent developments as well as of formerly proposed algorithms.

Digital infrared thermal imaging of human skin

Abstract: Describes computational approaches to image processing for improved interpretation and analysis. The authors discuss the physics of image formation, developments in uncooled camera technology, and the use of image processing to interpret 2-D maps of temperatures and to analyze thermal images for assessing physiological changes and responses to physical provocations and stresses such as brief exposure to low temperatures.

Thermal image analysis for polygraph testing

Abstract: We have designed, developed, and tested a very promising thermal image analysis method for polygraph testing. The method achieved a correct classification rate of CCR= 84% on the test population to our avail. This method, once refined, can serve as an additional channel for increasing the reliability and accuracy of traditional polygraph examination. We extract subtle facial temperature fluctuation patterns through nonlinear heat transfer modeling. The modeling transforms raw thermal data to blood flow rate information. Then, we use the slope of the average periorbital blood flow rate as the feature of a binary classification scheme. The results come to support our previous laboratory findings about the importance of periorbital blood flow in anxious states.

Neurovisceral integration in cardiac and emotional regulation

Abstract: The utility of heart-rate variability (HRV) to index important aspects of neural control of the heart has made it one of the most valuable tools for researchers interested in health and behavior. However, most of the literature and extant data on the central origins of HRV have been derived from animal models. As yet little is known about the central nervous system (CNS) origins and concomitants in humans. In the following we will briefly detail a set of neural structures that have been associated with important CNS functions including cardiac regulation and emotional regulation. We will briefly summarize recent data using pharmacological, neuroimaging, and psychophysiological techniques that have examined CNS concomitants of HRV especially during emotion. Based on these and other data will we report the results of a computational model that examined the interactions of several neural structures and their effect on emotion-related HRV.

Estimating respiratory frequency from autoregressive spectral analysis of heart period

Abstract: In this article we report the results of a pilot study and a larger investigation that examined the relationship between respiration frequency assessed using the traditional mercury strain gauge and using the central frequency of the HF component derived from autoregressive spectral analysis.

Infrared imaging: making progress in fulfilling its medical promise

Abstract: Discusses past, present, and future applications of infrared imaging in medicine. The topics mentioned include: breast cancer; helmet-mounted infrared for battlefield medical application; burns; image analysis and algorithm development.

A digital signal processor for Doppler radar sensing of vital signs

Abstract: A signal processor for the determination of respiration and heart rates in Doppler radar measurements is described. The processor can reliably calculate both rates for a subject at distances as large as 2 m. The rate determination is based on autocorrelation and uses several enhancement techniques, including a center clipper. Several representative results are included to show the future potential of using the processor for this purpose. Calculated heart rates agree for over 88% of the time with the reference rate, within a 2% margin, for all datasets. These results indicate excellent prospects for remote monitoring of vital signs through noncontact radar techniques.

Golden accomplishments in biomedical engineering

Abstract: The 50th anniversary of the IEEE Engineering in Medicine and Biology Society (EMBS) is an appropriate time to look back at the origins and growth of both the field of biomedical engineering and the EMBS. The present account gives most attention to the aspects of biomedical engineering to which IEEE members (and, earlier, American Institute of Electrical Engineers (AIEE) members and Institute of Radio Engineers (IRE) members) contributed, which is to say that this account emphasizes the electrical, electronic, and computing aspects of biomedical engineering. Topics covered include: history of the technologies; the roots of biomedical engineering; prehistory and history of the profession; biomedical applications of the computer; health care; ultrasound technology; new means of medical imaging; endoscopy, lasers, and fear of electromagnetic fields; study of human metabolism; the Human Genome Project, robotics, and internationalization; and forecasting progress in biomedical engineering.

Intention detection using a neuro-fuzzy EMG classifier

Abstract: One of the most important factors in prosthetic and orthotic controllers is the ability to detect the intention of the person to perform a certain activity such as standing up, quiet standing, walking, and sitting down. For these applications, detecting the intention of the person to perform an activity relieves them from the burden of conscious effort in operating the system. Electromyography (EMG) has been used extensively for intention detection and can be considered a bandlimited stochastic process with Gaussian distribution and zero mean, which has varying spectral characteristics in time. Various EMG features have been used for intention detection including the number of zero crossings, the EMG frequency characteristics, and the mean absolute values. There are a number of drawbacks that have been associated with these methods such as the high electrode sensitivity to electrode displacement, low recognition rate, and a perceivable delay in control. In this article we discuss a technique for EMG applications that decreases global delay time and improves time spectral analysis. The technique is aimed at improving the Gabor matching pursuit (GMP) algorithm through the use of genetic algorithms. The key stage of this design feeds EMG features to a neuro-fuzzy classifier that can be designed to detect the intention of the patient.

Electric field interactions between closely abutting excitable cells

Abstract: This review article summarizes some of the electrophysical evidence and morphological evidence against the hypothesis that the myocytes of cardiac muscles and visceral smooth muscles are profusely interconnected by low-resistance pathways (e.g., tunnels or gap-junction channels), which would give rise to a long length constant. Instead, propagation of the action potential (AP) is discontinuous, with a substantial junctional delay time at the cell junctions. Since the entire surface membrane of each cell becomes excited nearly simultaneously, a plot of propagation time versus distance (along a strand of cells) exhibits a typical staircase shape. This article demonstrates that the electric field that develops in the narrow junctional cleft (negative cleft potential) when the prejunctional membrane (pre-JM) fires an AP acts to depolarize the post-JM to its threshold. This mechanism, by itself, can account for transmission of excitation from cell to cell, but accessory mechanisms that act additively include K/sup +/ accumulation in the junctional clefts, gap-junction channels, and capacitive coupling.

Custom implant design for patients with cranial defects

Abstract: Three-dimensional image reconstruction and rapid prototyping models improve defect evaluation, treatment planning, implant design, and surgeon accuracy. It is found that both 3-D imaging and physical models are helpful for the evaluation of cranial defects, treatment planning, and custom implant design. While 3-D imaging can be used on a routine basis, the physical skull models could be a useful additional tool, especially when the cranial defects are rare, unusual, or difficult. In this case, the introduction of a rapid prototyping stereolithographic medical model for manufacturing a large cranioplasty implant is considered to be an improvement over other traditional techniques. Because of the accuracy of the physical model, the surgeon has a good understanding of the cranial defect and precise fitting implants can be fabricated in order to re-establish skull contours for the patient. In addition, the excellent fitting and fixation techniques of cranioplasty have reduced operating time significantly.

Frequency dependence of impedances at the acupuncture point Quze (PC3)

Abstract: Investigates an equivalence circuit model for characterizing a person's electrical properties. It is not straightforward to compare skin-impedance data of various research groups because the impedance depends very sensitively on measurement conditions such as the shape, size, material, and contact pressure of the electrodes, the skin humidity due to sweat, the applied current, the shape of the wave, and even the psychological condition of the subject. With these subtleties in mind, we show the impedance data taken by various groups and our data (shown with curves). Data of other groups are scattered around our curve.

Infrared functional imaging applied to Raynaud's phenomenon

Abstract: Presents an approach that allows simultaneous assessment of thermal properties of multiple fingers on both hands. It is a novel approach to the estimation of Raynaud's phenomenon based on infrared functional imaging assessments. Q, which represents the total amount of stored heat by the finger during the rewarming processes and is evaluated by means of the area under the time-temperature curve, seems to be particularly effective in order to describe the thermal recovery capabilities of the finger, also in terms of thermal efficiency. Q clearly highlights the difference between primary Raynaud's phenomenon and secondary scleroderma patients, and between patients and normal as well, and provides useful information about the abnormalities of their thermoregulatory finger properties.

Validity concerns of common heart-rate variability indices

Abstract: Time- and frequency-domain analyses of HRV have provided researchers with important measures of cardiac vagal activity. Stationarity is of theoretical importance for such analyses in the frequency domain but may not be of practical significance in any particular data set. It has been argued that if a stationarity test is available, it should be used. On the other hand, it is also possible that the RSA is quite robust to nonstationarity; the spectral data support that contention. Furthermore, procedures that correct for nonstationary data segments may compromise the representativeness of the data set. With regard to the time domain, MSD is advantageous in that it is conceptually and computationally simple, does not require respiration data, and under many conditions may be a reasonable alternative to HF spectral power or RSA. All told, the selection of cardiac vagal control indices and use of correction procedures should be based upon experimental situation and availability to the researcher, rather than orthodox adherence to idealized standards.

Hidden Markov model classification of myoelectric signals in speech

Abstract: It has been demonstrated that myoelectric signal (MES) automatic speech recognition (ASR) using an hidden Markov model (HMM) classifier is resilient to temporal variance, which offers improved robustness compared to the linear discriminant analysis (LDA) classifier. The overall performance of the MES ASR can be further enhanced by optimizing the features and structure of the HMM classifier to improve classification rate. Nevertheless, the HMM classifier has already shown that it would effectively complement an acoustic classifier in a multimodal ASR system.

Finding coordinated atrial activity during ventricular fibrillation using wavelet decomposition

Abstract: Until recently, the ECG recorded during ventricular fibrillation was thought to represent disorganized and unstructured electrical activity of the heart. Using a new signal analysis technique based on wavelet decomposition, we have begun to reveal previously unreported structure within the ECG tracing. We report preliminary findings that provide the first evidence linking this structure to unexpected mechanical phenomena occurring in the heart.

Poincare mapping for detecting abnormal dynamics of cardiac repolarization

Abstract: This article concentrates on analysis of subtle ECG signal features associated with characteristic temporal variations in the repolarization phase of cardiac electrical activation (i.e., variations that are embedded within the T-wave). These variations are termed T-wave alternans (TWA) to connote larger similarity between T-wave shapes in every other beat than that in the adjacent beats that occur during regular heart rhythm of an increased rate. In an early work the concept of associating TWA with period-doubling bifurcation of the cardiac oscillator has been put forward and demonstrated on canine ECG traces. In this article we extend this idea and use it for quantification of TWA in human ECGs recorded from postinfarction patients. The purpose of this work is to show that: a statistically significant correlation exists for the analyzed ECGs between the TWA level computed by means of an inter-cycle synchronized sampling technique known as Poincare mapping (PM) and a widely used Fourier spectrum (FS) method, and that the PM method outperforms the widely used FS method for TWA analysis in a number of ways.

Imaging of skin thermal properties with estimation of ambient radiation temperature

Abstract: Our latest system for the imaging of temperature and other thermal properties of the skin provides not only precise temperature distribution of the skin but also high-speed measurement of emissivity and inertia distribution. The method enabled us to measure the emissivity and thermal inertia of the skin even when the temperature distribution of the inner surface of the hood was not uniform. Two-dimensional measurement with hood switching, and a single-hood measurement system were considered.

Characteristics and capabilities of the hyperspectral imaging microscope

Abstract: Single-pass multispectral imaging enables applications ranging from whole-chromosome painting to gene mapping We have developed a prototype Hyperspectral Imaging Microscope (HIM) Workstation to take advantage of multispectral imaging analysis on a single pass We trade a full specimen image on a single pass for a full emission spectrum on a single pass The prototype HI microscope collects the visible emission spectrum from a microscope slide by multiple exposures of the CCD camera as it steps across the slide On each step, one dimension is space and the other is wavelength The software correlates spatial and spectral information with minimal or no use of optical filters, offering important advantages over a standard epi-fluorescence microscope

An anthrax "smoke" detector

Abstract: We report a method for automated monitoring of airborne endospores, which combines an aerosol capture technique with endospore detection based on terbium luminescence turn-on. We have demonstrated quantification of aerosolized bacterial spores with a response time of /spl sim/15 min, a sensitivity of 10/sup 4/ spores/ml, and a dynamic range of 4 orders of magnitude using a bioaerosol sampler, a microwave, and a lifetime-gated fluorimeter. Ultimately, the most attractive feature we have demonstrated is the unattended monitoring of aerosolized bacterial spores for the duration of a workday (/spl sim/8 hours).

Classification of sleep stages in infants: a neuro fuzzy approach

Abstract: An ANFIS based neuro-fuzzy classifier with a pruning algorithm was implemented and applied to the classification of sleep-waking states-stages in infants, using the sleep pattern detection system of P.A. Estevez (2002) to generate the inputs. Including artifacted pages, an average of 88.2% of expert agreement was achieved for testing data. As a result of the training process and pruning, rules and parameters that defined a fuzzy classification system were also determined. Analyzing the rules obtained for sleep-stage NREM-1, it was found that the main rule matched the expert rule to classify NREM-1. Additional rules were discovered that complement the classification and may provide additional information about the characteristics of this sleep stage. This is a promissory result, and further research is needed in this topic. Future work includes implementation of a clustering algorithm to determine the initial parameters of the system, training the system with a different objective function, such as the max-type error function described in J.S.R. Jang and C.T. Sun (1993), and evaluating the performance of different T-norms at layer 2 in Figure 1. The development of a general methodology for rule discovery and interpretation is also of interest.

Cellular neural networks for real-time DNA microarray analysis

Abstract: A new methodology for real-time processing of DNA chip images is proposed. The idea developed here is to use the cellular neural network (CNN) array to analyze the DNA microarray. A CNN is an analog dynamic processor array that reflects this property: the processing elements interact directly within a finite local neighborhood. Due to its architecture, a two-dimensional CNN array is widely used to solve image processing and pattern recognition problems; moreover, the parallelism characteristic of this structure allows one to perform the most computationally expensive image analysis tasks three orders of magnitude faster than a classical CPU-based computer. This approach, thanks to the supercomputing capabilities of the CNN architecture, makes the whole DNA chip methodology fully parallel and also makes the processing phase, until now very time consuming, a real-time step. We discuss the results of testing an algorithm based on the CNN universal machine (CNN-UM) that has been designed to classify the image data. The algorithm is implemented in an analogic (analog and logic) microprocessor.

Examining changes in HRV in response to varying ambient temperature

Abstract: The present study is designed to examine changes in HRV in response to ambient temperature change in college-aged men and women. We hypothesize that the VLFP (very-low-frequency power) component would be increased in the cold environment whereas the HFP (high-frequency power) component would be reduced in the hot environment with no differences between baseline and the cold condition.

Improving stereotactic surgery using 3-D reconstruction

Abstract: In brain surgery, placing a microelectrode from the outer brain to the specific target becomes a new medical resource-reduced technology to alleviate Parkinson's disease. During the operation, the brain atlas is an important and auxiliary tool that cannot completely describe the real brain structure of the patient. Thus, a neurosurgeon usually conjectures the surgical target through experience under the condition that no individual brain atlas is available. Hence, the danger of the surgical operation is high and the automation of the whole process is hard to achieve. In this article, we utilize the patient's computed tomography (CT) data, Schaltenbrand-Wahren (SW) brain atlas, and image processing techniques to develop a novel stereotactic surgical planning system, named Brain Deep Tracer. This system can help a neurosurgeon perform minimally invasive brain surgery (e.g., functional stereotaxy) and diagnose brain disease such as Parkinson's. Generally speaking, this system includes versatile functions such as three-dimensional (3-D) reconstruction of the brain atlas, 3-D localization of the patient's brain CT data, registration of the brain atlas and CT data, fusion of the brain atlas and CT data, and surgical trajectory planning. More importantly, this system can correct the distortion due to the tilt of the head frame and make the localization result more accurate than other systems.

Human thermal models for evaluating infrared images

Abstract: Discusses comparing infrared images under various thermal environmental conditions through normalization of skin surface temperature. To evaluate IR images obtained under various thermal environmental conditions, we proposed a human thermal model with which IR images obtained under certain thermal environmental conditions can be converted into images under other conditions. The model was based on a numerical calculation of the bio-heat transfer equations that express heat transfer phenomena within the human body. A 16-cylinder-segment model was used as the geometry of the human body. Comparisons of IR images with their converted images indicate that this method is effective in eliminating the influence of the thermal environmental conditions. However, the difference between the converted images and the original ones varies among segments. In future work, we will use this method to investigate the IR images of several subjects under various thermal environments.

Cortical architecture and self-referential control for brain-like computation

Abstract: Discusses a new approach to understanding how the brain organizes computation. Progress in understanding the brain function under constant interactions with the sensory environment is hampered by inadequate models and theories. Obviously, current models and theories of brain computing still appear irrelevant when they are confronted with real-world problems. We argue that architecture in the brain does not reflect the result of thinking, the ready-made algorithm for solving a problem. Rather it should reflect the control that generates the constraints to select a proper algorithm for a specific problem that is posed by the input-or to create a new one if the application of the previously acquired ones does not provide a sufficient solution. We propose that a value system (based on a genetically imprinted a priori knowledge on coarse behavioral evaluation of sensory input) and neocortical columnar architecture are crucial elements of future artificial neural systems that are expected to emulate the performance of the brain. This should be the case especially for those cognitive tasks that appear easy for animals in their everyday life but turn out to be hopelessly tricky for the current generation of computers. In order to advance beyond the well known paradigms of current computational theory, we need a more functional understanding of brain-type computation.

Interpreting ECG data by integrating statistical and artificial intelligence tools

Abstract: The use of an automated system integrating data conditioning, statistical methods, and artificial intelligence tools to summarize and interpret high-frequency physiological data such as the electrocardiogram is investigated. The development of a methodology and its associated tools for real-time patient monitoring and diagnosis is accomplished by using the commercial programming environments MATLAB and G2, a real-time knowledge-based system (KBS) development shell. Data interpretation and classification is performed by integrating statistical classification methods and knowledge-based techniques with a graphical user interface that provides quick access to the analysis results as well as the original data. A KBS was developed that incorporates various statistical methods with a rule-based decision system to detect abnormal situations, provide preliminary interpretation and diagnosis, and to report these findings to the healthcare provider.

Quantifying the relevance and stage of disease with the Tau image technique

Abstract: Discusses a complementary diagnostic imaging technique based on infrared functional imaging. It is concluded that the Tau images obtained with our method provide a large amount of information. The method, in fact, correctly depicts the ill and symptomatic situation; moreover, it is able to identify the surface projection onto the skin of the damaged areas and, by means of the evaluated value of the Tau time, the stage of the disease. Additionally, it is possible to evaluate the evolution of the clinical situation following a rehabilitation therapy and to quantitatively compare the different Tau images recorded at different times.

Modeling electric field transfer of excitation at cell junctions

Abstract: The electric field (EF) model was first developed on a "breadboard" using physical electric components (e.g., resistors, capacitors, batteries) and was then modeled mathematically by a series of differential equations and matrix equations and simulated on a large computer (CDC-6400). The results obtained by the two methods agreed very closely. However, these two methods of analysis are quite cumbersome. Therefore, in order to simplify the EF simulation, we wanted to model it on the PSpice program. In this article we discuss how we succeeded in demonstrating transmission of excitation from cell to cell in cardiac muscle and smooth muscle based on EF transmission at the cell junctions.

Discriminating between supraventricular and ventricular tachycardias from EGM onset analysis

Abstract: We hypothesize that the analysis of the ventricular electrogram onset (EGM onset) can discriminate between SVT and VT to obtain a simultaneous increase in sensitivity and specificity. We discuss our analysis of EGMs obtained during SVT and VT together with their preceding SRs in 38 SVT and 68 VT far field records from 16 patients. The, algorithmic implementation and the preprocessing tasks were performed through the support vector method (SVM), avoiding the overfitting by means of the statistical bootstrap resampling. To improve the safety for an individual patient, two new methods of incremental learning, based on the SVM, are proposed and tested on an independent set of spontaneous arrhythmia episodes.