Physiological Measurement (PHYSIOL MEAS)
Subject coverage. A journal for sensors, instrumentation and systems in physiology and medicine. It covers the quantitative assessment and visualization of physiological function in clinical research and practice, with an emphasis on the development of new methods of measurement and their validation. Practical and theoretical papers are published in topics including: measurement in applied physiology; human biology and clinical medicine; instrumentation and methods of data analysis; clinical engineering; patient monitoring; life support systems and prosthetic devices; measurement of flow and pressure.
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Other titlesPhysiological measurement (Online), Physiol. meas
Material typeDocument, Periodical, Internet resource
Document typeInternet Resource, Computer File, Journal / Magazine / Newspaper
Publications in this journal
- SourceAvailable from: iopscience.iop.org[show abstract] [hide abstract]
ABSTRACT: This focus section of Physiological Measurement follows the successful 13th International Conference on Biomedical Applications of Electrical Impedance Tomography (EIT 2012). The conference was held in China and hosted at Tianjin University. It was co-organized by Professor Xuemin Wang from Tianjin University, Professor Jie Zhang from the University of Kentucky, and Professor Eung Je Woo of Kyung Hee University.Physiological Measurement 06/2013; 34(6).
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ABSTRACT: Normalized pulse volume (NPV) derived from the ear has the potential to be a practical index for monitoring daily life stress. However, ear NPV has not yet been validated. Therefore, we compared NPV derived from an index finger using transmission photoplethysmography as a reference, with NPV derived from a middle finger and four sites of the ear using reflection photoplethysmography during baseline and while performing cold and warm water immersion in ten young and six middle-aged subjects. The results showed that logarithmically-transformed NPV (lnNPV) during cold water immersion as compared with baseline values was significantly lower, only at the index finger, the middle finger and the bottom of the ear-canal. Furthermore, lnNPV reactivities (ΔlnNPV; the difference between baseline and test values) from an index finger were significantly related to ΔlnNPV from the middle finger and the bottom of the ear-canal (young: r = 0.90 and 0.62, middle-aged: r = 0.80 and 0.58, respectively). In conclusion, these findings show that reflection and transmission photoplethysmography are comparable methods to derive NPV in accordance with our theoretical prediction. NPV derived from the bottom of the ear-canal is a valid approach, which could be useful for evaluating daily life stress.Physiological Measurement 02/2013; 34(3):359-75.
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ABSTRACT: Recent evidence suggests that the mechano-sensitivity of the vascular network may underlie rapid dilatory events in skeletal muscles. Previous investigations have been mostly based either on in vitro or on whole-limb studies, neither preparation allowing one to assess the musculo-vascular specificity under physiological conditions. The aim of this work is to characterize the mechano-sensitivity of an exclusively-muscular vascular bed in vivo. In five anesthetized rabbits, muscle blood flow was continuously monitored in the masseteric artery, bilaterally (n = 10). Hyperaemic responses were evoked by compressive stimuli of different extent (50, 100 and 200 mm Hg) and duration (0.5, 1, 2 and 5 s) exerted by a servo-controlled motor on the masseter muscle. Peak amplitude of the hyperaemic response ranged from 340 ± 30% of baseline (at 50 mm Hg) to 459 ± 57% (at 200 mm Hg) (P < 0.05), did not depend on stimulus duration and exhibited very good reliability (ICC = 0.98) when reassessed at 30 min intervals. The time course of the response depended neither on applied pressure nor on the duration of the stimulus. In conclusion, for its high sensitivity and reliability this technique is adequate to characterize mechano-vascular reactivity and may prove useful in the investigation of the underlying mechanisms, with implications in the control of vascular tone and blood pressure in health and disease.Physiological Measurement 02/2013; 34(3).
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ABSTRACT: This focus issue of Physiological Measurement follows the 38th Annual International Computing in Cardiology (CinC) Conference, hosted in Hangzhou, China in September 2011 by Zhejiang University. Each year, the NIH-sponsored PhysioNet resource (http://physionet.org/) runs an open competition lasting several months, aimed at encouraging the development of solutions to an unsolved or poorly solved problem in biomedicine, in most cases making use of relevant clinical and experimental data provided freely by PhysioNet. Participants in these annual challenges discuss their diverse approaches to the Challenge problems during dedicated scientific sessions at CinC. The topics of these PhysioNet/CinC Challenges range from physiologic signal processing and analysis to forecasting and modelling clinically important events and processes.In 2011, the PhysioNet/CinC Challenge was to develop an efficient algorithm able to run within a mobile phone, that can provide useful feedback in the process of acquiring a diagnostically usable 12-lead electrocardiogram (ECG). At a minimum, such an algorithm should indicate if an ECG is of adequate quality for interpretation, completing its analysis within a few seconds, while the patient is still present, so that another recording can be made immediately if needed.The ECG is among the most useful tools for diagnosing cardiovascular diseases (CVD), the most frequent cause of death worldwide. Although both CVD and mobile phones are ubiquitous, adequate primary health care is not. Many rural populations around the world rely on clinics staffed by lay volunteers to identify those in need of secondary care by health care professionals in distant city hospitals. It is increasingly feasible to provide rural clinics with inexpensive medical instruments such as electrocardiographs that transmit digital ECGs to smart phones for storage and display. These devices extend the reach of diagnosticians to remote areas, but without some means of quality control, technology alone cannot deliver consistently usable information to those able to interpret it. Methods that improve the quality of data collected result in better usage of the scarcest resource, clinical expertise. The growing interest in mHealth to provide point-of-care diagnostics to underserved populations is also driving the desire to leverage the power of smart phones to insert intelligence into medical data acquisition.PhysioNet provided a Challenge data set of 2000 12-lead ECG records, together with an open-source sample application able to run on an Android phone. (These remain freely available to interested readers at http://physionet.org/challenge/2011/.) The application was provided as a working example of a Challenge entry that can read an ECG and classify it as acceptable or unacceptable. Gold standard annotations (grades) for the ECGs were crowd-sourced from the public and invited experts. The annotators were also asked to rate their own expertise or experience level. In all, 8,327 grades were obtained; 1,733 ECGs were classified as acceptable or unacceptable, and 267 as indeterminate. In nearly all of the latter group, only a single grade was available; divided opinions were very rare. There was a high degree of self-consistency, consistency with other observers at the same and at different experience levels, and consistency with the reference classifications regardless of experience level. A random selection of half of the Challenge data set was designated as Set A, a training subset, and participants were provided with the grades for these 1000 ECGs. The remaining records were divided at random into Set B, a public test subset (500 ECGs available for study, with grades withheld) and Set C, a hidden test subset (500 ECGs not available for study, used only by PhysioNet for testing submitted algorithms).Each participant entered one or more of three Challenge events. In event 1, participants developed algorithms for classifying ECGs with respect to quality, and submitted their algorithms' classifications of Set B. Each entry was scored for accuracy (defined as the fraction of Acceptable and Unacceptable ECGs that were correctly classified, with Indeterminate ECGs excluded). In the other two events, participants submitted Java implementations of their algorithms to be used in the sample mobile application; these were tested in two reference mobile phones (in event 2, using Set B, with scoring as in event 1; and in Event 3 using Set C, with scoring based on a function of both accuracy and mobile phone processing speed). A total of 49 teams and individuals participated in the Challenge. Accuracies generally varied between 80% and 93% with average execution times of less than 2 seconds on the reference phones. [Many participants reported the accuracy of their methods as measured using training Set A; if these results are significantly better than those obtained using test Sets B and (especially) C, this is indicative of overfitting the training data.] A full description of the Challenge can be found in Silva et al (2011).Problems of noise and transient drops in data quality are not just confined to diagnostic ECGs however, and issues of noise plague physiologic measurements of many types in hospitals, causing high levels of false alarms, (Aboukhalil et al 2008). Although many of the papers in this focus issue (presented in alphabetical order by the surname of the first author) relate specifically to the PhysioNet/CinC Challenge 2011, several address the broader question of signal quality metrics in cardiorespiratory monitoring.Chen and Yang (2012) approached the Challenge problem of screening the quality of 12-lead ECGs by applying the inverse Dower transform to obtain 3-lead VCGs that they classified using multiscale recurrence analysis with self-organising maps to identify time-frequency features associated with poor and good quality ECG. They report 95.25% accuracy on the training data (Set A) and 90.0% on the independent test data (Set B).Clifford et al (2012) extended their original work in the Challenge, which used a series of signal quality metrics (based on morphological, statistical and spectral characteristics) and a support vector machine or multilayer perceptron neural network. The modifications included 1) labelling of and training on single leads, 2) upsampling the noisy data using the noise stress test database, 3) varying the window size and 4) testing their system on arrhythmic data. A classification accuracy of 98% on the training data (Set A) and 97% on the test data (Set B) was achieved. Reducing the window size led to a moderate drop in accuracy by < 1% per second removed, although this may be partially attributed to the transience of the noise. Tests on arrhythmic data led to a drop in accuracy to 93% indicating that algorithms may need retraining for some arrhythmias.Di Marco et al (2012) addressed ECG quality assessment by identifying baseline drift, flat line, QRS-artifact, spurious spikes, amplitude step changes, and other noise, using a time-frequency approach. Classification was based on cascaded single-condition decision rules tested levels of contaminants against classification thresholds. A supervised learning approach was also taken to combining the thresholds. The authors found that their cascaded heuristic threshold algorithm performed best with an accuracy of 91.40% on the Challenge test data (Set B).Hayn et al (2012) explored the use of four criteria; a no signal detector, a spike detector, a lead crossing point analysis and a measure of the robustness of QRS detection. An accuracy of 93.3% was achieved on the training data (Set A) and 91.6% on the test data (Set B). A simplified version of their algorithm (omitting the robustness measure) was the winning entry in event 3 of the Challenge. Scores for this algorithm for events 2 and 3 (both run on a smart phone) were 0.834 (Set B) and 0.873 (Set C) respectively.Jekova et al (2012) aimed to identify four major sources of ECG quality disruption: missing signal or reduced energy of the QRS complex above 4Hz; presence of high amplitude and steep artifacts above 1Hz; baseline drift at frequencies below 1Hz; power-line interference in a band ±2Hz around its central frequency; and high-frequency and electromyographic noises above 20Hz. The authors introduced 13 adjustable thresholds for amplitude and slope criteria, and reported the sensitivity (Se) and specificity (Sp) of their methods for detecting unacceptable ECGs; by adjusting thresholds, they obtained results ranging from Se = 98.7%, Sp = 80.9% to Se = 81.8%, Sp = 97.8% on the Challenge training data (Set A).Johannesen and Galeotti (2012) described a two stage approach to screening ECGs, first identifying missing signals, large voltage shifts, and saturation, then quantifying baseline wave, mains frequency, and muscle noise using average template matching. The authors achieved a classification accuracy of 92.3% on the Challenge training data (Set A) and 90.0% on the test data (Set B).Li and Clifford (2012) extended the method of Clifford et al described above to obtain a signal quality metric for the photoplethysmogram (PPG). The features were based on cross correlation with a local beat template, stretched in both a linear and nonlinear manner then combined using a multi-layer perceptron neural network. An expert-labelled database of 1055 segments of PPG, each 6 seconds long, recorded from 104 separate critical care admissions during both normal and verified arrhythmic events, was used to train and test their algorithms. An accuracy of 97.5% on the training set and 95.2% on test set was reported.Monasterio et al (2012) took a novel approach to identifying the quality of a data segment by combining both physiological and signal quality features in a machine learning framework, using multiple cardiovascular signals (ECG, PPG and respiratory waveforms). The aim of the research was to classify desaturations in neonates as true or false. A total of 1616 desaturation events from 27 neonatal admissions were annotated by two independent reviewers as true (physiologically relevant) or false (noise-related). The patients were divided into two independent groups for training and validation, and a series of signal quality and physiological metrics (such as gradient of heart rate and respiration rate) were estimated. A support vector machine was trained to use 13 of these features to classify the events as true or false. An accuracy of 100% was achieved during training, and a sensitivity of 86%, a specificity of 91%, and an accuracy of 90%was achieved in the test set.Redmond et al (2012) used three annotators to manually annotate 300 short single-lead ECG recordings to identify movement artifact, QRS locations and signal quality, with overreading to reconcile differences in order to obtain a gold standard three-level quality index (good, average, or bad). Template-based and signal morphology-based features were then presented to a Parzen-window supervised statistical classifier model, which achieved a three-level classification accuracy of 78.7% when using fully automated preprocessing algorithms to remove gross motion artifact and detect QRS locations. The authors note that this accuracy is similar to the human inter-scorer agreement.Xia et al (2012a) report on their approach to the Challenge problem, which won events 1 and 2. Twelve signal quality heuristics were developed and calculated for each of the 12 ECG leads, yielding a 12 by 12 matrix. The elements were then summed and thresholded to provide a classification for a given 12 lead ECG. After optimisation of the threshold, the authors achieved an accuracy of 95%, with a sensitivity of 88% and specificity of 97%.In the final paper of this focus issue, three of the authors of the previous paper (Xia, Garcia, and Zhao) Xia et al (2012b) focused on detection of electrode misplacement using a series of ECG features and a multilayer perceptron neural network. In the best case, with clean ECGs from training data, they were able to detect LA/LL misplacement with 87.4% accuracy, and all other misplacements with 98.4% accuracy. Noisy ECGs, and those containing arrhythmias, presented a more difficult challenge, and the authors note that accuracy may be poor on test data, suggesting the need for a more extensive data set for development and testing of electrode misplacement detection methods.It is noteworthy that the use of shared data sets in many of the papers in this focus issue permits the reader to make objective comparisons of the performance of the methods described in them. These comparisons can point the way to further advances toward assessment of quality in ECGs and other physiologic signals. Seven Challenge participants, including the authors of several of the papers included here, have also contributed their algorithms as open-source software for further study; these can be found at http://physionet.org/challenge/2011/sources/.As noted on PhysioNet, 'The annual PhysioNet/CinC Challenges seek to provide stimulating yet friendly competitions, while at the same time offering both specialists and non-specialists alike opportunities to make progress on significant open problems whose solutions may be of profound clinical value. The use of shared data provided via PhysioNet makes it possible for participants to work independently toward a common objective. At CinC, participants can make meaningful results-based comparisons of their methods; lively and well-informed discussions are the norm at scientific sessions dedicated to these challenges. Discovery of the complementary strengths of diverse approaches to a problem when coupled with deep understanding of that problem frequently sparks new collaborations and opportunities for further study.... It is especially significant that many of those who have participated in these challenges would not otherwise have had access to the data needed to study these topics.'PhysioNet invites readers of Physiological Measurement to participate in its Challenge series, and to identify and develop topics for future Challenges.ReferencesAboukhalil A, Nielsen L, Saeed M, Mark R G and Clifford G 2008 Reducing false alarm rates for critical arrhythmias using the arterial blood pressure waveform J. Biomed. Inform.41 442–51Chen Y and Yang H 2012 Self-organized neural network for the quality control of 12-lead ECG signals Physiol. Meas.33 1399–1418Clifford G D, Behar J, Li Q and Rezek I 2012 Signal quality indices and data fusion for determining acceptability of electrocardiograms collected in noisy ambulatory environments Physiol. Meas.33 1419–33Di Marco L Y, Duan W, Bojarnejad M, Zheng D, King, Murray A and Langley P 2012 Evaluation of an algorithm based on single-condition decision rules for binary classification of 12-lead ambulatory ECG recording quality Physiol. Meas.33 1435–48Hayn D, Jammerbund B and Schreier G 2012 QRS detection based ECG quality assessment Physiol. Meas.33 1449–61Jekova I, Krasteva V, Christov I and Abacherli R 2012 Threshold-based system for noise detection in multilead ECG recordings Physiol. Meas.33 1463–77Johannesen J and Galeotti L 2012 Automatic ECG quality scoring methodology: mimicking human annotators Physiol. Meas.33 1479–89Li Q and Clifford G D 2012 Dynamic time warping and machine learning for signal quality assessment of pulsatile signals Physiol. Meas.33 1491–1501Monasterio V, Burgess F and Clifford G D 2012 Robust classification of neonatal apnoea-related desaturations Physiol. Meas.33 1503–16Redmond S J, Xie Y, Chang D, Basilakis J and Lovell N H 2012 Electrocardiogram signal quality measures for unsupervised telehealth environments Physiol. Meas.331517–33Silva I, Moody G B and Celi L 2011 Improving the quality of ECGs collected using mobile phones: the PhysioNet/Computing in Cardiology Challenge 2011 Comput. Cardiol.38 1273–76Xia H, Garcia G, Bains J, Wortham D and Zhao X 2012a Matrix of regularity for improving the quality of ECGs Physiol. Meas.33 1535–48Xia H, Garcia G and Zhao X 2012b Automatic detection of ECG electrode misplacement: a tale of two algorithms Physiol. Meas.33 1549–61Physiological Measurement 01/2012; 33(9).
Article: Fast Track Communications[show abstract] [hide abstract]
ABSTRACT: Physiological Measurement (PMEA) is introducing a new article type—Fast Track Communications (FTCs)—with immediate effect. FTCs are outstanding short papers reporting important, timely new developments in physiological measurement and biomedical engineering. Fast Track Communications are intended for very high quality research, reporting highly significant new results, should not normally exceed eight journal pages (or 5000 words) in length so must be written in a clear and concise style, are sent to the Editor/Board Member for review in the first instance, benefit from accelerated publication (the average time from submission to online publication is expected to be under 3 months), and are placed at the front of the journal. When submitting FTCs for publication authors should provide a written justification to the Editor, explaining why the article meets our stringent quality and novelty criteria and justifies faster publication. The initial assessment of FTCs will be made by the Editor/Board Member. If they fail to reach the required standard this may slow down publication. In such cases FTCs may be recommended for consideration as a regular paper or possibly a note. (Notes will continue as a submission category for shorter, more technical communications than papers, both otherwise having the same review criteria.) Previously we have occasionally published short papers as 'Letters to the Editor'. These are now discontinued. However, authors may still submit letter-type comments about previously published PMEA papers, which will be published (alongside a reply from the original authors) only if they are considered by the Board to be of high interest to the general PMEA readership. We look forward to publishing our first Fast Track Communications in 2011. Richard Bayford Editor-in-Chief Jon Ruffle PublisherPhysiological Measurement 03/2011; 32(4).
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ABSTRACT: The publishers of Physiological Measurement (PMEA), IOP Publishing, in association with the journal owners, the Institute of Physics and Engineering in Medicine (IPEM), jointly award an annual prize for the best paper published in PMEA during the previous year.The responsibility for deciding the ultimate winner falls to the Editorial and International Advisory boards of the journal. A shortlist is constructed using the comments and ratings of our expert referees. For the 2010 award, 6 papers were considered. After the construction of the shortlist, it was then down to our board members to personally assess and rank the papers.This year, we had a close contest with a worthy winner. We have much pleasure in advising readers that the 2010 Martin Black award goes to Martin Mendez et al for their paper investigating the automatic screening of obstructive sleep apnea.Automatic screening of obstructive sleep apnea from the ECG based on empirical mode decomposition and wavelet analysis Martin Mendez, Jeroen Corthout, Sabine Van Huffel, Matteo Matteucci, Thomas Penzel, Sergio Cerutti and Anna Maria Bianchi 2010 Physiol. Meas.31 273–89All of the shortlisted papers were of great merit, and the full top-6 is listed below (in alphabetical order). In 2011, we have already published many high quality papers that should make next year's award very competitive. We look forward to seeing the outstanding work that will be published by our authors during the rest of the year.Richard Bayford Editor-in-ChiefJon RufflePublisherReferencesEdlow B L, Meeri N K , Durduran T, Zhou C, Putt M E, Yodh A G, Greenberg J H and Detre J A 2010 The effects of healthy aging on cerebral hemodynamic responses to posture change Physiol. Meas.31 477–95Haar P J, Broaddus W C, Chen Z, Fatouros P P, Gillies G T and Corwin F D 2010 Quantification of convection-enhanced delivery to the ischemic brain Physiol. Meas.31 1075–89Mendez M O, Corthout J, Van Huffel S, Matteucci M, Penzel T, Cerutti S and Bianchi A M 2010 Automatic screening of obstructive sleep apnea from the ECG based on empirical mode decomposition and wavelet analysis Physiol. Meas.31 273–89Mintchev M P, Deneva M G, Aminkov B I, Fattouche M, Yadid-Pecht O and Bray R C 2010 Pilot study of temporary controllable gastric pseudobezoars for dynamic non-invasive gastric volume reduction Physiol. Meas.31 131–44Stjerna S, Alatalo P, Mäki J and Vanhatalo S 2010 Evaluation of an easy, standardized and clinically practical method (SurePrep) for the preparation of electrode–skin contact in neurophysiological recordings Physiol. Meas.31 889–901Zolgharni M, Griffiths H and Ledger P D 2010 Frequency-difference MIT imaging of cerebral haemorrhage with a hemispherical coil array: numerical modelling Physiol. Meas.31 S111–25Physiological Measurement 01/2011; 32(9).
Article: EDITORIAL: 14th International Conference on Electrical Bioimpedance and the 11th International Conference on Biomedical Applications of Electrical Impedance Tomography (University of Florida, Gainesville, USA, 4-8 April 2010) 14th International Conference on Electrical Bioimpedance and the 11th International Conference on Biomedical Applications of Electrical Impedance Tomography (University of Florida, Gainesville, USA, 4-8 April 2010)[show abstract] [hide abstract]
ABSTRACT: This issue of Physiological Measurement follows the successful 14th International Conference on Electrical Bioimpedance and the 11th International Conference on Biomedical Applications of Electrical Impedance Tomography. The conference was hosted at the University of Florida, Gainesville, USA. It was organized by Rosalind Sadleir from the University of Florida, with Eung Je Woo of Kyung Hee University. The conference provided a platform for investigators in all aspects of bioimpedance and electrical impedance tomography (EIT) to converse on common areas of interest, whilst also being an opportunity for the community to broaden its outlook in the areas of clinical applications and new technologies and providing a link to researchers working on the measurement of bio-impedance, key to the development of impedance tomography and its clinical applications. A highlight of the meeting was the presentation of the Herman P Schwan award to bioimpedance leader Professor Sverre Grimnes (University of Oslo). The student paper competition was won by Christian Tronstad, also of the University of Oslo. The conference was privileged to host four eminent keynote speakers, headed by Professor Jakko Malmivuo (Tampere University of Technology, Tampere, Finland) who presented an address entitled 'Principle of reciprocity solves the most important problems in bioimpedance and in general in bioelectromagnetism', and Professor Bin He (University of Minnesota at Twin Cities, Minnesota, USA) who examined 'Electrical source and impedance imaging of biological tissues: opportunities and challenges'. Important clinical perspectives on applications of bioimpedance and EIT were provided by Dr Nathan W Levin (Albert Einstein College of Medicine and Renal Research Institute, New York, USA) who spoke on 'Bioimpedance applications: a nephrologist's point of view' and Dr Gerhard K Wolf (Children's Hospital Boston, Massachusetts, USA) whose presentation was 'Lung imaging with electrical impedance tomography: will it change management?' Two events particularly appreciated by younger members of the bioimpedance and EIT communities were a pre-conference workshop on 'Bioelectricity basics' organized by Sverre Grimmes and Ørjan Martinsen, with contributions from Richard Bayford and Uwe Pliquett, and a two-day intensive course on bioelectromagnetism by Professor Malmivuo, based on the text Bioelectromagnetism by Malmivuo and Plonsey. This issue contains papers stemming from discussions and feedback in these research areas during the conference. It was also an opportunity for new researchers to join the community and propose innovations. A total of 131 oral papers were presented at the conference, and all authors were invited to prepare new peer-reviewed papers for submission to this issue of Physiological Measurement. The manuscripts were put through a careful review process before selection. A total of 18 were accepted, covering an important range of topics. The papers included in this issue clearly reflect the continuing interest in both bioimpedance and EIT, producing a wide range of clinical applications that were strongly represented at the conference. These include brain function, breast and thorax imaging. It is important that researchers do not neglect the challenges that clinical applications of bioimpedance and EIT present, as there are still many technical difficulties the technology needs to overcome in order to provide valuable clinical tools. However, there are promising signs that these tools are moving closer to realization, particularly for thorax imaging. Both bioimpedance and EIT continue to provide researchers with new challenges and attract more researchers into these research areas, as evident by the number of attendees at this conference (176). The high quality of the research papers in this issue is clear evidence of the significant advances in the field. At the end of the meeting it was announced that the next joint conference will be held in Heilbad Heiligenstadt, Germany in 2013. We look forward to another successful meeting at that time.Physiological Measurement 01/2011; 32(7).
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ABSTRACT: The publishers of Physiological Measurement (PMEA), IOP Publishing, in association with the journal owners, the Institute of Physics and Engineering in Medicine (IPEM), jointly award an annual prize for the best paper published in PMEA during the previous year. The responsibility for deciding the ultimate winner falls to the Editorial and International Advisory boards of the journal. However, a shortlist of papers has to first be put together. Since a paper published early in the volume will have more exposure than one published later, it would be unfair to use download or citation statistics to judge papers. For this reason, the shortlist is constructed using the comments and ratings of our expert referees. For the 2009 award, 6 papers were considered. After the construction of the shortlist, it was then down to our board members to personally assess and rank the papers. This year, we have a clear and worthy winner. We have much pleasure in advising readers that the 2009 Martin Black award goes to Andy Adler et al for their paper on electrical impedance tomography. GREIT: a unified approach to 2D linear EIT reconstruction of lung images Andy Adler, John Arnold, Richard Bayford, Andrea Borsic, Brian Brown, Paul Dixon, Theo Faes, Inez Frerichs, Herve Gagnon, Yvo Garber, Bartlomiej Grychtol, Gunter Hahn, William Lionheart, Anjum Malik, Robert P Patterson, Janet Stocks, Andrew Tizzard, Norbert Weiler and Gerhard K Wolf 2009 Physiol. Meas. 30 S35-55 All of the shortlisted papers were of great merit, and the full top-6 is listed below (in alphabetical order). Richard Bayford Editor-in-Chief Jon Ruffle Publisher References Adler A, Arnold J H, Bayford R, Borsic A, Brown B, Dixon P, Faes T J C, Frerichs I, Gagnon H, Gärber Y, Grychtol B, Hahn G, Lionheart W R B, Malik A, Patterson R P, Stocks J, Tizzard A, Weiler N and Wolf G K 2009 GREIT: a unified approach to 2D linear EIT reconstruction of lung images Physiol. Meas. 30 S35-55 Aelen P, Jurkov A, Aulanier A and Mintchev M P 2009 Pilot acute study of feedback-controlled retrograde peristalsis invoked by neural gastric electrical stimulation Physiol. Meas. 30 309-22 Borsic A, Halter R, Wan Y, Hartov A and Paulsen K D 2009 Sensitivity study and optimization of a 3D electric impedance tomography prostate probe Physiol. Meas. 30 S1-18 Kim K K, Kim J S, Lim Y G and Park K S 2009 The effect of missing RR-interval data on heart rate variability analysis in the frequency domain Physiol. Meas. 30 1039-50 Remme E W, Hoff L, Halvorsen P S, Nærum E, Skulstad H, Fleischer L A, Elle O J and Fosse E 2009 Validation of cardiac accelerometer sensor measurements Physiol. Meas. 30 1429-44 Wang L, Su S W, Celler B G, Chan G S H, Cheng T M and Savkin A V 2009 Assessing the human cardiovascular response to moderate exercise: feature extraction by support vector regression Physiol. Meas. 30 227-44Physiological Measurement 01/2010; 31(9).
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ABSTRACT: This issue of Physiological Measurement follows the successful 10th International Conference on Electrical Impedance Tomography. The conference was hosted by the University of Manchester's School of Mathematics and organized by Bill Lionheart, Richard Bayford from Middlesex University and Eung Je Woo of Kyung Hee University. A combined workshop on electromagnetic inverse problems was also held at the same time, organized by Olive Dorn and Bill Lionheart. This workshop shared plenary lectures and some sessions. One of the plenaries was by Mark Nelson on electrosensory data acquisition and signal processing strategies in electric fish; perhaps the first time the medical EIT community had shared a conference with experts on the variety of EIT that occurs naturally in some fish. Indeed some electrosensing fish were present at the meeting and an aquarium especially set up in the School of Mathematics.The conference provided a platform for investigators in all aspects of EIT to engage in common areas of interest whilst also giving an opportunity for the community to broaden its outlook in the areas of clinical applications and new technologies associated with EIT. It also provided a link to researchers working on the mathematical aspect of inverse problems that limit the development of EIT for clinical applications. This upholds the tradition of successful conferences on biomedical applications of EIT, as with the previous jointly organized conference on electrical impedance tomography in 2008, co-hosted by the Thayer School of Engineering at Dartmouth College and organized by Alex Hartov of Dartmouth and Eung Je Woo.This special issue of Physiological Measurement contains articles stemming from discussion and feedback during the conference on EIT research areas. The conference was also an opportunity for new researchers to join the community and propose recent innovations. A total of 84 oral papers were presented and all authors were invited to prepare new peer-reviewed papers for inclusion in this issue. The manuscripts were put through a careful review process before a total of 12 were accepted covering an important range of topics.The articles included in this year's special issue clearly reflect the continuing interest in EIT covering a wide range of clinical applications that were strongly represented at the conference. These included brain function, breast imaging, the thorax, and a new target: the prostate. It is important that researchers do not neglect the challenges that clinical applications of bio-impedance and EIT present as there are still many technical difficulties that the technology needs to overcome in order to provide valuable clinical tools. However, there are promising signs that these tools are close to realization, particularly for thorax imaging. This was clear as the most popular clinical application at the conference was a special session organized by Inez Frerichs, which provided an invaluable forum for clinical practitioners and EIT researchers to exchange ideas.As EIT continues to provide researchers with new challenges, the high quality of the research articles in this special issue is clear evidence of the significant advances in the field.Physiological Measurement 01/2010; 31(8).
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ABSTRACT: Grubb's exponent is a useful parameter quantifying cerebral hemodynamics. This letter is a reply to the Comment by Boas and Payne (2009 Physiol. Meas. 30 L9-11). We reiterated our view that Grubb's exponent estimated by linear regression is theoretically inappropriate to be used to predict cerebral blood flow (CBF). In their Comment, Boas and Payne proposed the novel use of total least squares (TLS) to estimate Grubb's exponent which we also agreed is a better technique than linear regression, and Grubb's exponent estimated by TLS will allow the prediction of CBF from cerebral blood volume (CBV).Physiological Measurement 10/2009; 30(10):L13-L14.
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ABSTRACT: We propose a new algorithm to detect and classify transient cardiac ischemia episodes, designed with the goal of providing a real-time execution without penalizing the classifier accuracy much. The algorithm is based on a novel mixture of time-domain analysis and machine learning techniques, specifically bagging of decision trees, and it has been developed using a well-recognized and freely distributed database, namely the long-term ST database. The ST episode detection sensitivity/positive predictivity using the annotation protocol A for this database is 68.26%/74.91%. The sensitivity result increases until 93.97% for the most dangerous episodes in terms of duration and magnitude (annotated according to protocol C). The test of the algorithm over the freely distributed part of the European Society of Cardiology database has shown results of sensitivity and positive predictivity of 83.33% and 77.31%, respectively. Those results are close to the results obtained by related works that present approaches to detect ischemia episodes off-line, which is remarkable if we take into account that in our real-time approach, less information is available during the classification process.Physiological Measurement 10/2009; 30(9):983-98.
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ABSTRACT: Energy scavenging has increasingly become an interesting option for powering electronic devices because of the almost infinite lifetime and the non-dependence on fuels for energy generation. Moreover, the rise of wireless technologies promises new applications in medical monitoring systems, but these still face limitations due to battery lifetime and size. A trade-off of these two factors has typically governed the size, useful life and capabilities of an autonomous system. Energy generation from sources such as motion, light and temperature gradients has been established as commercially viable alternatives to batteries for human-powered flashlights, solar calculators, radio receivers and thermal-powered wristwatches, among others. Research on energy harvesting from human activities has also addressed the feasibility of powering wearable or implantable systems. Biomedical sensors can take advantage of human-based activities as the energy source for energy scavengers. This review describes the state of the art of energy scavenging technologies for powering sensors and instrumentation of physiological variables. After a short description of the human power and the energy generation limits, the different transduction mechanisms, recent developments and challenges faced are reviewed and discussed.Physiological Measurement 10/2009; 30(9):R35-62.
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ABSTRACT: Magnetic resonance electrical impedance tomography (MREIT) is a new bio-imaging modality providing cross-sectional conductivity images from measurements of internal magnetic flux densities produced by externally injected currents. Recent experimental results of postmortem and in vivo imaging of the canine brain demonstrated its feasibility by showing conductivity images with meaningful contrast among different brain tissues. MREIT image reconstructions involve a series of data processing steps such as k-space data handling, phase unwrapping, image segmentation, meshing, modelling, finite element computation, denoising and so on. To facilitate experimental studies, we need a software tool that automates these data processing steps. In this paper, we summarize such an MREIT software package called CoReHA (conductivity reconstructor using harmonic algorithms). Performing imaging experiments of the postmortem canine abdomen, we demonstrate how CoReHA can be utilized in MREIT. The abdomen with a relatively large field of view and various organs imposes new technical challenges when it is chosen as an imaging domain. Summarizing a few improvements in the experimental MREIT technique, we report our first conductivity images of the postmortem canine abdomen. Illustrating reconstructed conductivity images, we discuss how they discern different organs including the kidney, spleen, stomach and small intestine. We elaborate, as an example, that conductivity images of the kidney show clear contrast among cortex, internal medulla, renal pelvis and urethra. We end this paper with a brief discussion on future work using different animal models.Physiological Measurement 09/2009; 30(9):957-66.
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ABSTRACT: Changes in patients' autonomic tone and specific pharmacologic interventions may modify the ventricular response (actual heart rate) during atrial fibrillation (AF). Hypnotic agents such as propofol may modify autonomic balance as they promote a sedative state. It has been shown that propofol slightly slows atrial fibrillatory activity, but the net global effect on the ventricular response remains unknown. We aimed to evaluate in patients in AF the effect of a propofol bolus on the ventricular rate and regularity at ECG. We analysed the possible relation with local atrial fibrillatory activities, as ratios between atrial and ventricular rates (AVRs), analysing atrial activity from intracardiac electrograms at the free wall of the right and left atria and at the interatrial septum. We compared data at the baseline and after complete hypnosis. Propofol was associated with a more homogeneous ventricular response and lower AVR values at the interatrial septum.Physiological Measurement 09/2009; 30(8):833-45.
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ABSTRACT: In humans, the muscle sympathetic nerve activity (MSNA) signal is challenging to detect, record and analyze. Several methods exist that attempt to capture the latent construct of MSNA. We directly compared the performance of five MSNA parameters: burst frequency, burst incidence, median burst amplitude, arbitrary units (AU) and fractal dimension (FD). The MSNA signal was recorded in 33 subjects for approximately 30 min before, during and after the application of a graded cold pressor test stimulus at 18 degrees C, 10 degrees C and 2 degrees C in random order with an adequate wash-out period. Using coefficient of variation, Shannon's entropy and principal component analysis, we observed that these five parameters defined two physical and conceptual domains of MSNA-frequency and amplitude. Since AU combines information from both these domains, we observed that it explained maximum inter-subject and inter-experimental segment variation. FD did not explain the inter-subject variability and was identified as a unique parameter in the factor analysis. Epidemiological studies that attempt to quantify MSNA may consistently use AU as the parameter for quantification of MSNA.Physiological Measurement 09/2009; 30(8):861-8.
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