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Showing papers in "Physiological Measurement in 2008"


Journal ArticleDOI
TL;DR: The robust HR estimation method is described, which provides an accurate HR estimate even in the presence of high levels of persistent noise and artifact, and during episodes of extreme bradycardia and tachycardia.
Abstract: Physiological signals such as the electrocardiogram (ECG) and arterial blood pressure (ABP) in the intensive care unit (ICU) are often severely corrupted by noise, artifact and missing data, which lead to large errors in the estimation of the heart rate (HR) and ABP. A robust HR estimation method is described that compensates for these problems. The method is based upon the concept of fusing multiple signal quality indices (SQIs) and HR estimates derived from multiple electrocardiogram (ECG) leads and an invasive ABP waveform recorded from ICU patients. Physiological SQIs were obtained by analyzing the statistical characteristics of each waveform and their relationships to each other. HR estimates from the ECG and ABP are tracked with separate Kalman filters, using a modified update sequence based upon the individual SQIs. Data fusion of each HR estimate was then performed by weighting each estimate by the Kalman filters' SQI-modified innovations. This method was evaluated on over 6000 h of simultaneously acquired ECG and ABP from a 437 patient subset of ICU data by adding real ECG and realistic artificial ABP noise. The method provides an accurate HR estimate even in the presence of high levels of persistent noise and artifact, and during episodes of extreme bradycardia and tachycardia.

396 citations


Journal ArticleDOI
TL;DR: A comprehensive review of recent developments in wireless sensor technology for monitoring behaviour related to human physiological responses is provided and background information on the use of wireless technology and sensors to develop a wireless physiological measurement system is presented.
Abstract: Current wireless technologies, such as wireless body area networks and wireless personal area networks, provide promising applications in medical monitoring systems to measure specified physiological data and also provide location-based information, if required. With the increasing sophistication of wearable and implantable medical devices and their integration with wireless sensors, an ever-expanding range of therapeutic and diagnostic applications is being pursued by research and commercial organizations. This paper aims to provide a comprehensive review of recent developments in wireless sensor technology for monitoring behaviour related to human physiological responses. It presents background information on the use of wireless technology and sensors to develop a wireless physiological measurement system. A generic miniature platform and other available technologies for wireless sensors have been studied in terms of hardware and software structural requirements for a low-cost, low-power, non-invasive and unobtrusive system.

395 citations


Journal ArticleDOI
TL;DR: This paper reviews MREIT from the basics to the most recent research outcomes, focusing on measurement techniques and experimental methods rather than mathematical issues, and summarizes what has been done and what needs to be done.
Abstract: Cross-sectional imaging of an electrical conductivity distribution inside the human body has been an active research goal in impedance imaging. By injecting current into an electrically conducting object through surface electrodes, we induce current density and voltage distributions. Based on the fact that these are determined by the conductivity distribution as well as the geometry of the object and the adopted electrode configuration, electrical impedance tomography (EIT) reconstructs cross-sectional conductivity images using measured current-voltage data on the surface. Unfortunately, there exist inherent technical difficulties in EIT. First, the relationship between the boundary current-voltage data and the internal conductivity distribution bears a nonlinearity and low sensitivity, and hence the inverse problem of recovering the conductivity distribution is ill posed. Second, it is difficult to obtain accurate information on the boundary geometry and electrode positions in practice, and the inverse problem is sensitive to these modeling errors as well as measurement artifacts and noise. These result in EIT images with a poor spatial resolution. In order to produce high-resolution conductivity images, magnetic resonance electrical impedance tomography (MREIT) has been lately developed. Noting that injection current produces a magnetic as well as electric field inside the imaging object, we can measure the induced internal magnetic flux density data using an MRI scanner. Utilization of the internal magnetic flux density is the key idea of MREIT to overcome the technical difficulties in EIT. Following original ideas on MREIT in early 1990s, there has been a rapid progress in its theory, algorithm and experimental techniques. The technique has now advanced to the stage of human experiments. Though it is still a few steps away from routine clinical use, its potential is high as a new impedance imaging modality providing conductivity images with a spatial resolution of a few millimeters or less. This paper reviews MREIT from the basics to the most recent research outcomes. Focusing on measurement techniques and experimental methods rather than mathematical issues, we summarize what has been done and what needs to be done. Suggestions for future research directions, possible applications in biomedicine, biology, chemistry and material science are discussed.

211 citations


Journal ArticleDOI
TL;DR: The results suggest that the IRT technique may represent an objective quantifiable indicator of autonomic disturbances although there are considerable temporal variations in the measured values which are due to both technical factors such as equipment accuracy, measurement environment and technique, and physiological variability of the blood flow, and these factors should be taken into account.
Abstract: The aim of this study was to investigate the reproducibility of skin surface infrared thermography (IRT) measurements and determine the factors influencing the variability of the measured values. While IRT has been widely utilized in different clinical conditions, there are few available data on the values of the skin temperature patterns of healthy subjects and their reproducibility. We recorded the whole body skin temperatures of sixteen healthy young men with two observers on two consecutive days. The results were compared using intra-class correlations analyses (ICC). The inter-examiner reproducibility of the IRT measurements was high: mean ICC 0.88 (0.73-0.99). The day-to-day stability of thermal patterns varied depending on the measured area: it was high in the core and poor in distal areas. The reproducibility of the side-to-side temperature differences (deltaT) was moderately good between the two observers (mean ICC 0.68) but it was reduced with time, especially in the extremities, mean ICC 0.4 (-0.01-0.83). The results suggest that the IRT technique may represent an objective quantifiable indicator of autonomic disturbances although there are considerable temporal variations in the measured values which are due to both technical factors such as equipment accuracy, measurement environment and technique, and physiological variability of the blood flow, and these factors should be taken into account.

183 citations


Journal ArticleDOI
TL;DR: A methodology of studying of ingestive behavior by non-invasive monitoring of swallowing (deglutition) and chewing (mastication) has been developed and a protocol is developed for manual scoring of chewing and swallowing for use as a gold standard.
Abstract: A methodology of studying of ingestive behavior by non-invasive monitoring of swallowing (deglutition) and chewing (mastication) has been developed. The target application for the developed methodology is to study the behavioral patterns of food consumption and producing volumetric and weight estimates of energy intake. Monitoring is non-invasive based on detecting swallowing by a sound sensor located over laryngopharynx or by a bone-conduction microphone and detecting chewing through a below-the-ear strain sensor. Proposed sensors may be implemented in a wearable monitoring device, thus enabling monitoring of ingestive behavior in free-living individuals. In this paper, the goals in the development of this methodology are two-fold. First, a system comprising sensors, related hardware and software for multi-modal data capture is designed for data collection in a controlled environment. Second, a protocol is developed for manual scoring of chewing and swallowing for use as a gold standard. The multi-modal data capture was tested by measuring chewing and swallowing in 21 volunteers during periods of food intake and quiet sitting (no food intake). Video footage and sensor signals were manually scored by trained raters. Inter-rater reliability study for three raters conducted on the sample set of five subjects resulted in high average intra-class correlation coefficients of 0.996 for bites, 0.988 for chews and 0.98 for swallows. The collected sensor signals and the resulting manual scores will be used in future research as a gold standard for further assessment of sensor design, development of automatic pattern recognition routines and study of the relationship between swallowing/chewing and ingestive behavior.

146 citations


Journal ArticleDOI
TL;DR: A relationship between flow rates and vessel areas is derived from phase-contrast magnetic resonance measurements in the internal carotid arteries and vertebral arteries of normal subjects.
Abstract: Subject-specific computational and experimental models of hemodynamics in cerebral aneurysms require the specification of physiologic flow conditions. Because patient-specific flow data are not always available, researchers have used 'typical' or population average flow rates and waveforms. However, in order to be able to compare the magnitude of hemodynamic variables between different aneurysms or groups of aneurysms (e.g. ruptured versus unruptured) it is necessary to scale the flow rates to the area of the inflow artery. In this work, a relationship between flow rates and vessel areas is derived from phase-contrast magnetic resonance measurements in the internal carotid arteries and vertebral arteries of normal subjects.

139 citations


Journal ArticleDOI
TL;DR: The study has proven that optimal VA cancelation is achieved when a number between 20 and 30 complexes is selected following a correlation-based strategy and provides a more accurate beat-to-beat ventricular QRST representation than traditional techniques.
Abstract: The proper analysis and characterization of atrial fibrillation (AF) from surface electrocardiographic (ECG) recordings requires to cancel out the ventricular activity (VA), which is composed of the QRS complex and the T wave. Historically, for single-lead ECGs, the averaged beat subtraction (ABS) has been the most widely used technique. However, this method is very sensitive to QRST wave variations and, moreover, high-quality cancelation templates may be difficult to obtain when only short length and single-lead recordings are available. In order to overcome these limitations, a new QRST cancelation method based on adaptive singular value cancelation (ASVC) applied to each single beat is proposed. In addition, an exhaustive study about the optimal set of complexes for better cancelation of every beat is also presented for the first time. The whole study has been carried out with both simulated and real AF signals. For simulated AF, the cancelation performance was evaluated making use of a cross-correlation index and the normalized mean square error (nmse) between the estimated and the original atrial activity (AA). For real AF signals, two additional new parameters were proposed. First, the ventricular residue (VR) index estimated the presence of ventricular activity in the extracted AA. Second, the similarity (S) evaluated how the algorithm preserved the AA segments out of the QRST interval. Results indicated that for simulated AF signals, mean correlation, nmse, VR and S values were 0.945 ± 0.024, 0.332 ± 0.073, 1.552 ± 0.386 and 0.986 ± 0.012, respectively, for the ASVC method and 0.866 ± 0.042, 0.424 ± 0.120, 2.161 ± 0.564 and 0.922 ± 0.051 for ABS. In the case of real signals, the mean VR and S values were 1.725 ± 0.826 and 0.983 ± 0.038, respectively, for ASVC and 3.159 ± 1.097 and 0.951 ± 0.049 for ABS. Thus, ASVC provides a more accurate beat-to-beat ventricular QRST representation than traditional techniques. As a consequence, VA cancelation is optimized and the AA can be extracted more precisely. Finally, the study has proven that optimal VA cancelation is achieved when a number between 20 and 30 complexes is selected following a correlation-based strategy.

133 citations


Journal ArticleDOI
TL;DR: The results suggest that the proposed EHG signal analysis provides an accurate estimate of the IUP, which is compared to root mean squared analysis and optimal linear filtering and validated by simultaneous measurement of theIUP on nine women during labor.
Abstract: Monitoring the uterine contraction provides important prognostic information during pregnancy and parturition. The existing methods employed in clinical practice impose a compromise between reliability and invasiveness. A promising technique for uterine contraction monitoring is electrohysterography (EHG). The EHG signal measures the electrical activity which triggers the contraction of the uterine muscle. In this paper, a non-invasive method for intrauterine pressure (IUP) estimation by EHG signal analysis is proposed. The EHG signal is regarded as a non-stationary signal whose frequency and amplitude characteristics are related to the IUP. After acquisition in a multi-channel configuration, the EHG signal is therefore analyzed in the time–frequency domain. A first estimation of the IUP is then derived by calculation of the unnormalized first statistical moment of the frequency spectrum. The estimation accuracy is finally increased by identification of a second-order polynomial model. The proposed method is compared to root mean squared analysis and optimal linear filtering and validated by simultaneous measurement of the IUP on nine women during labor. The results suggest that the proposed EHG signal analysis provides an accurate estimate of the IUP.

111 citations


Journal ArticleDOI
TL;DR: A new dynamic filtering method based on estimating template functions for the pulmonary and cardiac components by means of principal component analysis and frequency domain filtering is developed, which enables an observer to examine the variation of the cardiac signal beat-by-beat after a one-time setup period of 20 s.
Abstract: In spontaneously breathing or ventilated subjects, it is difficult to image cardiac-related conductivity changes using electrical impedance tomography (EIT) due to the high amplitude of the ventilation component. Previous attempts to separate these components included either electrocardiogram-gated averaging, frequency domain filtering or holding the breath while performing the measurements. However, such methods are either not able to produce continuous real-time images or to fully separate cardiac and pulmonary changes. The aim of this work was to develop a new dynamic filtering method for the online separation of pulmonary and cardiac changes avoiding the drawbacks of the previous attempts. The approach is based on estimating template functions for the pulmonary and cardiac components by means of principal component analysis and frequency domain filtering. Then, these templates are fitted into the input signals. The new method enables an observer to examine the variation of the cardiac signal beat-by-beat after a one-time setup period of 20 s. Preliminary in vivo results of two healthy subjects are presented. The results are superior to frequency domain filtering and in good agreement with signals averaged over several cardiac cycles. The method does not depend on ECG or other a priori knowledge. The apparent validity of the method's ability to separate cardiac and pulmonary changes in EIT images was shown and has to be confirmed in future studies. The algorithm opens up new possibilities for future clinical trials on continuous monitoring by means of EIT and for the examination of the relation between the cardiac component and lung perfusion.

107 citations


Journal ArticleDOI
TL;DR: An improved fdEIT image reconstruction algorithm that properly handles the interplay of conductivity and permittivity upon measured boundary voltage data is proposed and demonstrated by using computer simulations to validate its feasibility in future experimental studies.
Abstract: Frequency-difference electrical impedance tomography (fdEIT) has been proposed to deal with technical difficulties of a conventional static EIT imaging method caused by unknown boundary geometry, uncertainty in electrode positions and other systematic measurement artifacts. In fdEIT, we try to produce images showing changes of a complex conductivity distribution with respect to frequency. Simultaneously injecting currents with at least two frequencies, we find differences of measured boundary voltages between those frequencies. In most previous studies, real parts of frequency-difference voltage data were used to reconstruct conductivity changes and imaginary parts to reconstruct permittivity changes. This conventional approach neglects the interplay of conductivity and permittivity upon measured boundary voltage data. In this paper, we propose an improved fdEIT image reconstruction algorithm that properly handles the interaction. It uses weighted frequency differences of complex voltage data and a complex sensitivity matrix to reconstruct frequency-difference images of complex conductivity distributions. We found that there are two major sources of image contrast in fdEIT. The first is a contrast in complex conductivity values between an anomaly and background. The second is a frequency dependence of a complex conductivity distribution to be imaged. We note that even for the case where conductivity and permittivity do not change with frequency, the fdEIT algorithm may show a contrast in frequency-difference images of complex conductivity distributions. On the other hand, even if conductivity and permittivity values significantly change with frequency, there is an example where we cannot find any contrast. The performance of the proposed method is demonstrated by using computer simulations to validate its feasibility in future experimental studies.

105 citations


Journal ArticleDOI
TL;DR: A novel glucoregulatory model has been developed to create a virtual population of critically ill facilitating in silico testing of glucose controllers at the intensive care unit and a validation process to validate virtual patients developed for the purpose of testing glucose controllers is proposed.
Abstract: Focused research is underway to improve the delivery of tight glycaemic control at the intensive care unit. A major component is the development of safe, efficacious and effective insulin titration algorithms, which are normally evaluated in time-consuming resource-demanding clinical studies. Simulation studies with virtual critically ill patients can substantially accelerate the development process. For this purpose, we created a model of glucoregulation in the critically ill. The model includes five submodels: a submodel of endogenous insulin secretion, a submodel of insulin kinetics, a submodel of enteral glucose absorption, a submodel of insulin action and a submodel of glucose kinetics. Model parameters are estimated utilizing prior knowledge and data collected routinely at the intensive care unit to represent the high intersubject and temporal variation in insulin needs in the critically ill. Bayesian estimation combined with the regularization method is used to estimate (i) time-invariant model parameters and (ii) a time-varying parameter, the basal insulin concentration, which represents the temporal variation in insulin sensitivity. We propose a validation process to validate virtual patients developed for the purpose of testing glucose controllers. The parameter estimation and the validation are exemplified using data collected in six critically ill patients treated at a medical intensive care unit. In conclusion, a novel glucoregulatory model has been developed to create a virtual population of critically ill facilitating in silico testing of glucose controllers at the intensive care unit.

Journal ArticleDOI
TL;DR: This work presents a relatively simple analog front-end that adapts AD5933 to a four-electrode strategy, allowing its use in biomedical applications for the first time.
Abstract: The increasing number of applications of electrical bioimpedance measurement in biomedical application together with the continuous advances in the textile technology applied in the design and development of biopotential electrodes has encouraged several researchers to do the first attempts to develop portable electrical bioimpedance measurement systems and even wearable. The main aim of these systems is mainly home-monitoring. Analog Devices has provided us with the AD5933, a new system-on-chip fully integrated electrical impedance spectrometer which might allow the implementation of minimal size instrumentation for electrical bioimpedance measurements. This system, however, performs a 2-Electrode measurement and thus is not suitable for most of the spectroscopy applications of electrical bioimpedance. In this work we present a relatively simple analog front-end that adapts the AD5933 to a 4-Electrode strategy, allowing its use in biomedical applications. The resulting impedance measurements exhibit a better performance in aspects like load dynamic range and accuracy. These type of minimum-size, AD5933-based bioimpedance measurement systems would lead the researcher to develop and implement light and wearable electrical bioimpedance systems for monitoring applications, a new a huge niche for medical technology development

Journal ArticleDOI
TL;DR: The goal of this development is to provide a new diagnostic tool that offers the user a reproducible, easy access to a fast and spatially resolved diagnostic 'heart view'.
Abstract: Capacitive sensors can be employed for measuring the electrocardiogram of a human heart without electric contact with the skin. This configuration avoids contact problems experienced by conventional electrocardiography. In our studies, we integrated these capacitive electrocardiogram electrodes in a 15-sensor array and combined this array with a tablet personal computer. By placing the system on the patient's body, we can measure a 15-channel electrocardiogram even through clothes and without any preparation. The goal of this development is to provide a new diagnostic tool that offers the user a reproducible, easy access to a fast and spatially resolved diagnostic 'heart view'.

Journal ArticleDOI
TL;DR: This is the first time a high-spatial-resolution image of current density is presented using MAET and a mathematical formula whereby the lead field current density may be utilized to reconstruct the distribution of the electrical impedance in a piecewise smooth object is offered.
Abstract: Primarily this report outlines our investigation on utilizing magneto-acousto-electrical-tomography (MAET) to image the lead field current density in volume conductors. A lead field current density distribution is obtained when a current/voltage source is applied to a sample via a pair of electrodes. This is the first time a high-spatial-resolution image of current density is presented using MAET. We also compare an experimental image of current density in a sample with its corresponding numerical simulation. To image the lead field current density, rather than applying a current/voltage source directly to the sample, we place the sample in a static magnetic field and focus an ultrasonic pulse on the sample to simulate a point-like current dipole source at the focal point. Then by using electrodes we measure the voltage/current signal which, based on the reciprocity theorem, is proportional to a component of the lead field current density. In the theory section, we derive the equation relating the measured voltage to the lead field current density and the displacement velocity caused by ultrasound. The experimental data include the MAET signal and an image of the lead field current density for a thin sample. In addition, we discuss the potential improvements for MAET especially to overcome the limitation created by the observation that no signal was detected from the interior of a region having a uniform conductivity. As an auxiliary we offer a mathematical formula whereby the lead field current density may be utilized to reconstruct the distribution of the electrical impedance in a piecewise smooth object.

Journal ArticleDOI
TL;DR: This paper presents a robust technique for the classification of six types of heartbeats through an electrocardiogram (ECG) using a wavelet transform along with the instantaneous RR-interval, which offers substantial advantages over previous techniques for implementation in a practical ECG analyzer.
Abstract: This paper presents a robust technique for the classification of six types of heartbeats through an electrocardiogram (ECG). Features extracted from the QRS complex of the ECG using a wavelet transform along with the instantaneous RR-interval are used for beat classification. The wavelet transform utilized for feature extraction in this paper can also be employed for QRS delineation, leading to reduction in overall system complexity as no separate feature extraction stage would be required in the practical implementation of the system. Only 11 features are used for beat classification with the classification accuracy of approximately 99.5% through a KNN classifier. Another main advantage of this method is its robustness to noise, which is illustrated in this paper through experimental results. Furthermore, principal component analysis (PCA) has been used for feature reduction, which reduces the number of features from 11 to 6 while retaining the high beat classification accuracy. Due to reduction in computational complexity (using six features, the time required is approximately 4 ms per beat), a simple classifier and noise robustness (at 10 dB signal-to-noise ratio, accuracy is 95%), this method offers substantial advantages over previous techniques for implementation in a practical ECG analyzer.

Journal ArticleDOI
TL;DR: It is suggested that multi-frequency time-difference images must be interpreted in terms of relative contrast changes with respect to frequency, primarily due to the limitation of the difference imaging algorithm.
Abstract: Validation and interpretation of reconstructed images using a multi-frequency electrical impedance tomography (mfEIT) requires a conductivity phantom including imaging objects with known complex conductivity (σ + iωe) spectra. We describe imaging experiments using the recently developed mfEIT system called the KHU Mark1 with the frequency range of 10 Hz to 500 kHz. Using a bio-impedance spectroscopy (BIS) system, we first measured complex conductivity spectra of different imaging objects including saline, agar, polyacrylamide, TX151, animal hide gelatin, banana and cucumber. Based on an analysis of how conductivity and permittivity affect measured complex boundary voltages, we suggested a new complex version of a multi-frequency time-difference image reconstruction algorithm. Imaging experiments were conducted to produce time-difference images of the objects at multiple frequencies using the proposed algorithm. Images of a conductor (stainless steel) and an insulator (acrylic plastic) were used to set a common scale bar to display all images. Comparing reconstructed time-difference images at multiple frequencies with measured complex conductivity spectra, we found that they showed an overall similarity in terms of changes in complex conductivity values with respect to frequency. However, primarily due to the limitation of the difference imaging algorithm, we suggest that multi-frequency time-difference images must be interpreted in terms of relative contrast changes with respect to frequency. We propose further imaging studies using biological tissues of known complex conductivity spectra and using human subjects to find clinical applications of the mfEIT system.

Journal ArticleDOI
TL;DR: Noise reduction together with a proper choice of features could improve the classification accuracy to 96.78%, making the automated analysis a possibility, and investigating the performance of a new segmentation algorithm based on pattern recognition.
Abstract: Obstructive sleep apnea (OSA) is a highly prevalent disease in which upper airways are collapsed during sleep, leading to serious consequences. Snoring is the earliest symptom of OSA, but its potential in clinical diagnosis is not fully recognized yet. The first task in the automatic analysis of snore-related sounds (SRS) is to segment the SRS data as accurately as possible into three main classes: snoring (voiced non-silence), breathing (unvoiced non-silence) and silence. SRS data are generally contaminated with background noise. In this paper, we present classification performance of a new segmentation algorithm based on pattern recognition. We considered four features derived from SRS to classify samples of SRS into three classes. The features—number of zero crossings, energy of the signal, normalized autocorrelation coefficient at 1 ms delay and the first predictor coefficient of linear predictive coding (LPC) analysis—in combination were able to achieve a classification accuracy of 90.74% in classifying a set of test data. We also investigated the performance of the algorithm when three commonly used noise reduction (NR) techniques in speech processing—amplitude spectral subtraction (ASS), power spectral subtraction (PSS) and short time spectral amplitude (STSA) estimation—are used for noise reduction. We found that noise reduction together with a proper choice of features could improve the classification accuracy to 96.78%, making the automated analysis a possibility.

Journal ArticleDOI
TL;DR: Properly calibrated diameter waveforms offer a viable alternative for local pressure estimation at the carotid artery and compared to linear calibration, exponential calibration significantly improves the pressure estimation.
Abstract: Calibrated diameter distension waveforms could provide an alternative for local arterial pressure assessment more widely applicable than applanation tonometry. We compared linearly and exponentially calibrated carotid diameter waveforms to tonometry readings. Local carotid pressures measured by tonometry and diameter waveforms measured by ultrasound were obtained in 2026 subjects participating in the Asklepios study protocol. Diameter waveforms were calibrated using a linear and an exponential calibration scheme and compared to measured tonometry waveforms by examining the mean root-mean-squared error (RMSE), carotid systolic blood pressure (SBPcar) and augmentation index (AIx) of calibrated and measured pressures. Mean RMSE was 5.2(3.3) mmHg (mean(stdev)) for linear and 4.6(3.6) mmHg for exponential calibration. Linear calibration yielded an underestimation of SBPcar by 6.4(4.1) mmHg which was strongly correlated to values of brachial pulse pressure (PPbra) (R = 0.4, P < 0.05). Exponential calibration underestimated true SBPcar by 1.9(3.9) mmHg, independent of PPbra. AIx was overestimated by linear calibration by 1.9(10.1)%, the difference significantly increasing with increasing AIx (R = 0.25, P < 0.001) and by exponential calibration by 5.4(10.6)%, independently of the value of AIx. Properly calibrated diameter waveforms offer a viable alternative for local pressure estimation at the carotid artery. Compared to linear calibration, exponential calibration significantly improves the pressure estimation.

Journal ArticleDOI
TL;DR: The results indicate that cBIS utilizing a dynamic technique continuously during dialysis is an accurate and precise approach to specific end points for the estimation of body hydration status.
Abstract: Although many methods have been utilized to measure degrees of body hydration, and in particular to estimate normal hydration states (dry weight, DW) in hemodialysis (HD) patients, no accurate methods are currently available for clinical use. Biochemcial measurements are not sufficiently precise and vena cava diameter estimation is impractical. Several bioimpedance methods have been suggested to provide information to estimate clinical hydration and nutritional status, such as phase angle measurement and ratio of body fluid compartment volumes to body weight. In this study, we present a calf bioimpedance spectroscopy (cBIS) technique to monitor calf resistance and resistivity continuously during HD. Attainment of DW is defined by two criteria: (1) the primary criterion is flattening of the change in the resistance curve during dialysis so that at DW little further change is observed and (2) normalized resistivity is in the range of observation of healthy subjects. Twenty maintenance HD patients (12 M/8 F) were studied on 220 occasions. After three baseline (BL) measurements, with patients at their DW prescribed on clinical grounds (DWClin), the target post-dialysis weight was gradually decreased in the course of several treatments until the two dry weight criteria outlined above were met (DWcBIS). Post-dialysis weight was reduced from 78.3 ± 28 to 77.1 ± 27 kg (p < 0.01), normalized resistivity increased from 17.9 ± 3 to 19.1 ± 2.3 × 10−2 Ω m3 kg−1 (p < 0.01). The average coefficient of variation (CV) in three repeat measurements of DWcBIS was 0.3 ± 0.2%. The results indicate that cBIS utilizing a dynamic technique continuously during dialysis is an accurate and precise approach to specific end points for the estimation of body hydration status. Since no current techniques have been developed to detect DW as precisely, it is suggested as a standard to be evaluated clinically.

Journal ArticleDOI
TL;DR: A second-order (mass-spring-damper) model was proposed to characterize the behaviour of the soft tissue between the bone and the sensor, and there was a trend to increase the natural frequency of the system with decreasing accelerometer mass.
Abstract: A common problem shared by accelerometers, inertial sensors and any motion measurement method based on skin-mounted sensors is the movement of the soft tissues covering the bones. The aim of this work is to propose a method for the validation of the attachment of skin-mounted sensors. A second-order (mass-spring-damper) model was proposed to characterize the behaviour of the soft tissue between the bone and the sensor. Three sets of experiments were performed. In the first one, different procedures to excite the system were evaluated to select an adequate excitation stimulus. In the second one, the selected stimulus was applied under varying attachment conditions while the third experiment was used to test the model. The heel drop was chosen as the excitation method because it showed lower variability and could discriminate between different attachment conditions. There was, in agreement with the model, a trend to increase the natural frequency of the system with decreasing accelerometer mass. An important result is the development of a standard procedure to test the bandwidth of skin-mounted inertial sensors, such as accelerometers mounted on the skin or markers heavier than a few grams.

Journal ArticleDOI
TL;DR: MSE analysis of HR, SBP and DBP oscillations is able to detect subtle abnormalities in cardiovascular control in young patients with DM and is independent of standard linear measures.
Abstract: Multiscale entropy (MSE) analysis provides information about complexity on various time scales. The aim of this study was to test whether MSE is able to detect autonomic dysregulation in young patients with diabetes mellitus (DM). We analyzed heart rate (HR) oscillations, systolic (SBP) and diastolic blood pressure (DBP) signals in 14 patients with DM type 1 and 14 age- and sex-matched healthy controls. SampEn values (scales 1-10) and linear measures were computed. HR: among the linear measures of heart rate variability significant differences between groups were only found for RMSSD (p = 0.043). MSE was significantly reduced on scales 2 and 3 in DM (p = 0.023 and 0.010, respectively). SBP and DBP: no significant differences were detected with linear measures. In contrast, MSE analysis revealed significantly lower SampEn values in DM on scale 3 (p = 0.039 for SBP; p = 0.015 for DBP). No significant correlations were found between MSE and linear measures. In conclusion, MSE analysis of HR, SBP and DBP oscillations is able to detect subtle abnormalities in cardiovascular control in young patients with DM and is independent of standard linear measures.

Journal ArticleDOI
TL;DR: A characterization of dual-axis swallowing accelerometry signals from healthy adults in the time and time-frequency domains revealed that the two axes contain different information about swallowing and that the superior-inferior axis should be further investigated in future swallowing Accelerometry studies.
Abstract: Single-axis swallowing accelerometry has shown potential as a non-invasive clinical swallowing assessment tool. Previous swallowing accelerometry research has focused exclusively on the anterior–posterior vibration detected on the surface of the neck. However, hyolaryngeal motion during pharyngeal swallowing occurs in both the anterior–posterior and superior–inferior directions, suggesting that dual-axis accelerometry may be worthy of investigation. With this motivation, the present paper provides a characterization of dual-axis swallowing accelerometry signals from healthy adults in the time and time–frequency domains. Time-domain analysis revealed that signals in the two axes exhibited different probability density functions, and minimal cross-correlation and mutual information. Time–frequency analysis highlighted inter-axis dissimilarities in the scalograms, pseudo-spectra and temporal evolution of low- and high-frequency content. Therefore, it was concluded that the two axes contain different information about swallowing and that the superior–inferior axis should be further investigated in future swallowing accelerometry studies.

Journal ArticleDOI
TL;DR: The ability to acquire high-resolution conductivity images will find numerous clinical applications not supported by other medical imaging modalities and the reduction of the imaging current to a level that a human subject can tolerate.
Abstract: Magnetic resonance electrical impedance tomography (MREIT) aims at producing high-resolution cross-sectional conductivity images of an electrically conducting object such as the human body. Following numerous phantom imaging experiments, the most recent study demonstrated successful conductivity image reconstructions of postmortem canine brains using a 3 T MREIT system with 40 mA imaging currents. Here, we report the results of in vivo animal imaging experiments using 5 mA imaging currents. To investigate any change of electrical conductivity due to brain ischemia, canine brains having a regional ischemic model were scanned along with separate scans of canine brains having no disease model. Reconstructed multi-slice conductivity images of in vivo canine brains with a pixel size of 1.4 mm showed a clear contrast between white and gray matter and also between normal and ischemic regions. We found that the conductivity value of an ischemic region decreased by about 10-14%. In a postmortem brain, conductivity values of white and gray matter decreased by about 4-8% compared to those in a live brain. Accumulating more experience of in vivo animal imaging experiments, we plan to move to human experiments. One of the important goals of our future work is the reduction of the imaging current to a level that a human subject can tolerate. The ability to acquire high-resolution conductivity images will find numerous clinical applications not supported by other medical imaging modalities. Potential applications in biology, chemistry and material science are also expected.

Journal ArticleDOI
TL;DR: The novel assistance system allows the identification of malnourished patients, and it can be applied for screening and monitoring of the nutritional status of hospital patients.
Abstract: Protein-energy malnutrition reduces the quality of life, lengthens the time in hospital and dramatically increases mortality. Currently there is no simple and objective method available for assessing nutritional status and identifying malnutrition. The aim of this work is to develop a novel assistance system that supports the physician in the assessment of the nutritional status. Therefore, three subject groups were investigated: the first group consisted of 688 healthy subjects. Two additional groups consisted of 707 patients: 94 patients with primary diseases that are known to cause malnutrition, and 613 patients from a hospital admission screening. In all subjects bioimpedance spectroscopy measurements were performed, and the body composition was calculated. Additionally, in all patients the nutritional status was assessed by the subjective global assessment score. These data are used for the development and validation of the assistance system. The basic idea of the system is that nutritional status is reflected by body composition. Hence, features of the nutritional status, based on the body composition, are determined and compared with reference ranges, derived from healthy subjects' data. The differences are evaluated by a fuzzy logic system or a decision tree in order to identify malnourished patients. The novel assistance system allows the identification of malnourished patients, and it can be applied for screening and monitoring of the nutritional status of hospital patients.

Journal ArticleDOI
TL;DR: An automatic algorithm based on the peripheral arterial tone (PAT) signal to differentiate between light and deep sleep stages and a close to full stage detection method based solely on the PAT and actigraphy signals is proposed.
Abstract: The objective of this study is to develop and assess an automatic algorithm based on the peripheral arterial tone (PAT) signal to differentiate between light and deep sleep stages. The PAT signal is a measure of the pulsatile arterial volume changes at the finger tip reflecting sympathetic tone variations and is recorded by an ambulatory unattended device, the Watch-PAT100, which has been shown to be capable of detecting wake, NREM and REM sleep. An algorithm to differentiate light from deep sleep was developed using a training set of 49 patients and was validated using a separate set of 44 patients. In both patient sets, Watch-PAT100 data were recorded simultaneously with polysomnography during a full night sleep study. The algorithm is based on 14 features extracted from two time series of PAT amplitudes and inter-pulse periods (IPP). Those features were then further processed to yield a prediction function that determines the likelihood of detecting a deep sleep stage epoch during NREM sleep periods. Overall sensitivity, specificity and agreement of the automatic algorithm to identify standard 30 s epochs of light and deep sleep stages were 66%, 89%, 82% and 65%, 87%, 80% for the training and validation sets, respectively. Together with the already existing algorithms for REM and wake detection we propose a close to full stage detection method based solely on the PAT and actigraphy signals. The automatic sleep stages detection algorithm could be very useful for unattended ambulatory sleep monitoring assessing sleep stages when EEG recordings are not available.

Journal ArticleDOI
TL;DR: A 16-channel MIT measurement system that is capable of parallel readout of 16 receiver channels and of low conductivity applications, conductivity less than 5 S m(-1), and a frequency of 10 MHz is introduced.
Abstract: Magnetic induction tomography (MIT) is a technique for imaging the internal conductivity distribution of an object. In MIT current-carrying coils are used to induce eddy currents in the object and the induced voltages are sensed with other coils. From these measurements, the internal conductivity distribution of the object can be reconstructed. In this paper, we introduce a 16-channel MIT measurement system that is capable of parallel readout of 16 receiver channels. The parallel measurements are carried out using high-quality audio sampling devices. Furthermore, approaches for reconstructing MIT images developed for the 16-channel MIT system are introduced. We consider low conductivity applications, conductivity less than 5 S m−1, and we use a frequency of 10 MHz. In the image reconstruction, we use time-harmonic Maxwell's equation for the electric field. This equation is solved with the finite element method using edge elements and the images are reconstructed using a generalized Tikhonov regularization approach. Both difference and static image reconstruction approaches are considered. Results from simulations and real measurements collected with the Philips 16-channel MIT system are shown.

Journal ArticleDOI
TL;DR: It is concluded that uncontrolled single on-line exhalations are not suitable for the reliable measurement of isoprene in the breath and that rebreathing can be the basis of an easily tolerated protocol for the reliability collection of breath samples.
Abstract: Analysis of volatile organic compounds (VOCs) on human breath has great potential as a non-invasive diagnostic technique. It is, therefore, surprising that no single, standard procedure has evolved for breath sampling. Here we present a novel repeated-cycle isothermal rebreathing method, where one cycle comprises five rebreaths, which could be adopted for breath analysis of VOCs. For demonstration purposes, we present measurements of three common breath VOCs: isoprene, acetone and methanol. Their concentrations measured in breath are shown to increase with number of rebreaths until a plateau value is reached by at least 20 rebreaths. The average ratio of plateau concentration to single mixed expired breath concentration was found to be 1.92 +/- 0.57 for isoprene, 1.25 +/- 0.13 for acetone and 1.12 +/- 0.12 for methanol (mean +/- standard deviation). Measurements from on-line single exhalations are presented which demonstrate a positive slope in the time-dependent expirograms of isoprene and acetone. The slope of the isoprene expirogram is persistently linear and the end-expired concentration of isoprene is highly variable in the same subject depending on the duration of exhalation. End-expired values of acetone are not as sensitive to the length of exhalation, and are the same to within measurement uncertainty for any duration of exhalation for any subject. It is concluded that uncontrolled single on-line exhalations are not suitable for the reliable measurement of isoprene in the breath and that rebreathing can be the basis of an easily tolerated protocol for the reliable collection of breath samples.

Journal ArticleDOI
TL;DR: The development of a single-sector thermophysiological human simulator, which consists of a sweating heated cylinder 'Torso' coupled with the iesd-Fiala multi-node model of human physiology and thermal comfort, enables overall physiological and comfort responses, health risk and survival conditions to be predicted for adult humans for various scenarios.
Abstract: Thermal sweating manikins are used to analyse the heat and mass transfer phenomena in the skin-clothing-environment system. However, the limiting factor of present thermal manikins is their inability to simulate adequately the human thermal behaviour, which has a significant effect on the clothing microenvironment. A mathematical model of the human physiology was, therefore, incorporated into the system control to simulate human thermoregulatory responses and the perception of thermal comfort over a wide range of environmental and personal conditions. Thereby, the computer model provides the physiological intelligence, while the hardware is used to measure the required calorimetric states relevant to the human heat exchange with the environment. This paper describes the development of a single-sector thermophysiological human simulator, which consists of a sweating heated cylinder 'Torso' coupled with the iesd-Fiala multi-node model of human physiology and thermal comfort. Validation tests conducted for steady-state and, to some extent, transient conditions ranging from cold to hot revealed good agreement with the corresponding experimental results obtained for semi-nude subjects. The new coupled system enables overall physiological and comfort responses, health risk and survival conditions to be predicted for adult humans for various scenarios.

Journal ArticleDOI
TL;DR: The proposed interpretations of the gamma variate model describe physics aspects of the dilution process and lead to a better understanding of the observed parameters, increasing the value and credibility of the model, and possibly expanding its diagnostic applications.
Abstract: The analysis of intravascular indicator dynamics is important for cardiovascular diagnostics as well as for the assessment of tissue perfusion, aimed at the detection of ischemic regions or cancer hypervascularization. To this end, indicator dilution curves are measured after the intravenous injection of an indicator bolus and fitted by parametric models for the estimation of the hemodynamic parameters of interest. Based on heuristic reasoning, the dilution process is often modeled by a gamma variate. In this paper, we provide both a physical and stochastic interpretation of the gamma variate model. The accuracy of the model is compared with the local density random walk model, a known model based on physics principles. Dilution curves were measured by contrast ultrasonography both in vitro and in vivo (20 patients). Blood volume measurements were used to test the accuracy and clinical relevance of the estimated parameters. Both models provided accurate curve fits and volume estimates. In conclusion, the proposed interpretations of the gamma variate model describe physics aspects of the dilution process and lead to a better understanding of the observed parameters, increasing the value and credibility of the model, and possibly expanding its diagnostic applications.

Journal ArticleDOI
TL;DR: It is shown that this Bayesian filtering framework may be effectively used for removing cardiac contaminants such as the ECG, MCG and ballistocardiographic artifacts from different biomedical recordings such asThe electroencephalogram, electromyogram and also for canceling maternal cardiac signals from fetal ECG/MCG.
Abstract: Electrocardiogram (ECG) and magnetocardiogram (MCG) signals are among the most considerable sources of noise for other biomedical signals. In some recent works, a Bayesian filtering framework has been proposed for denoising the ECG signals. In this paper, it is shown that this framework may be effectively used for removing cardiac contaminants such as the ECG, MCG and ballistocardiographic artifacts from different biomedical recordings such as the electroencephalogram, electromyogram and also for canceling maternal cardiac signals from fetal ECG/MCG. The proposed method is evaluated on simulated and real signals.