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BACKGROUND: Delayed medical attendance is a leading cause of death in patients with ST elevation myocardial infarction (STEMI). METHODS: We aimed to introduce, develop, and validate a novel method (RELF method) for detection of transmural ischemia based on a new and easy-to-use 3-lead configuration and orthonormalization of ST reference vectors (STDVN). The study included 60 patients undergoing coronary artery occlusion (CAO) during balloon inflation and 30 healthy subjects. RESULTS: STDVN was significantly different and an optimal discriminator between CAO patients and healthy subjects (respectively 8.00±4.50 vs. 1.90±0.86 normalized units, p<0.001). Compared to the 12-lead ECG, the RELF method was sensitive (90 vs. 73%, p=0.13) and more specific (91 vs. 75%, p<0.001). CONCLUSIONS: The RELF method is highly accurate for early detection of acute occlusion related ischemia and it outperforms the conventional 12-lead ECG criteria for STEMI. This method provides a platform for self-detection of CAO with handheld devices or smart phones.

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Linear methods of reconstruction play an important role in medical electrical impedance tomography (EIT) and there is a wide variety of algorithms based on several assumptions. With the Graz consensus reconstruction algorithm for EIT (GREIT), a novel linear reconstruction algorithm as well as a standardized framework for evaluating and comparing methods of reconstruction were introduced that found widespread acceptance in the community. In this paper, we propose a two-sided extension of this concept by first introducing a novel method of evaluation. Instead of being based on point-shaped resistivity distributions, we use 2759 pairs of real lung shapes for evaluation that were automatically segmented from human CT data. Necessarily, the figures of merit defined in GREIT were adjusted. Second, a linear method of reconstruction that uses orthonormal eigenimages as training data and a tunable desired point spread function are proposed. Using our novel method of evaluation, this approach is compared to the classical point-shaped approach. Results show that most figures of merit improve with the use of eigenimages as training data. Moreover, the possibility of tuning the reconstruction by modifying the desired point spread function is shown. Finally, the reconstruction of real EIT data shows that higher contrasts and fewer artifacts can be achieved in ventilation- and perfusion-related images.

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The theoretical solution for the independence of bioelectric and biomagnetic signals rising from volume sources was published by Jaakko Malmivuo in 1995 [1]. In 2000 his research group published a clinical study on electro- and magnetocardiography which confirmed this result [2, 3]. In 2005 Iwasaki and co-workers published a clinical study on the detection of epileptic foci with electro- and magnetoencephalo-graphy [4]. They came to similar result as Malmivuo et al. in their study on ECG and MCG. Because the theoretical solution is now confirmed independently by two research groups with two different clinical studies and different volume sources, there is no doubt that the problem of the independence of bioelectric and biomagnetic signals from volume sources is now solved.

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Since the detection of the first biomagnetic signals in 1963 there has been continuous discussion on the properties and relative merits of bioelectric and biomagnetic measurements. In this review article it is briefly discussed the early history of this controversy. Then the theory of the independence and interdependence of bioelectric and biomagnetic signals is explained, and a clinical study on ECG and MCG that strongly supports this theory is presented. The spatial resolutions of EEG and MEG are compared in detail, and the issue of the maximum number of electrodes in EEG is also discussed. Finally, some special properties of EEG and MEG methods are described. In brief, the conclusion is that EEG and MEG are only partially independent and their spatial resolutions are about the same. Recording both of them brings some additional information on the bioelectric activity of the brain. These two methods have certain unique properties that make either of them more beneficial in certain applications.

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BACKGROUND: The electroencephalography (EEG) is an attractive and a simple technique to measure the brain activity. It is attractive due its excellent temporal resolution and simple due to its non-invasiveness and sensor design. However, the spatial resolution of EEG is reduced due to the low conducting skull. In this paper, we compute the potential distribution over the closed surface covering the brain (cortex) from the EEG scalp potential. We compare two methods - L-curve and generalised cross validation (GCV) used to obtain the regularisation parameter and also investigate the feasibility in applying such techniques to N170 component of the visually evoked potential (VEP) data. METHODS: Using the image data set of the visible human man (VHM), a finite difference method (FDM) model of the head was constructed. The EEG dataset (256-channel) used was the N170 component of the VEP. A forward transfer matrix relating the cortical potential to the scalp potential was obtained. Using Tikhonov regularisation, the potential distribution over the cortex was obtained. RESULTS: The cortical potential distribution for three subjects was solved using both L-curve and GCV method. A total of 18 cortical potential distributions were obtained (3 subjects with three stimuli each - fearful face, neutral face, control objects). CONCLUSIONS: The GCV method is a more robust method compared to L-curve to find the optimal regularisation parameter. Cortical potential imaging is a reliable method to obtain the potential distribution over cortex for VEP data.

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Volume conductor head models contain thin tissue layers, some of which have highly contrasting conductivity values relative to neighboring tissues. We expound the cerebrospinal fluid (CSF) and the six cortical layers of the gray matter. The dual nature of the CSF competes with the well-known shunting behavior of the skull. The incorporation of the six ultra thin cortical layers demonstrate the significance of the electrical attraction and shunting of lead field currents in multilayered tissues owing to the inherent conductive properties of each tissue. We relate the similar effects of the CSF to the diploë, i.e., the soft bone between the two hard bone layers of the skull. A natural subsequence of this article will allow researchers and clinicians to conceptually understand the measurement sensitivity distribution of a bipolar electroencephalography (EEG) lead. We recommend including the highly conductive thin layers such as the diploë of the skull and the CSF into head models as well as further investigation into the cortical layers I-VI of the gray matter. Comprehensively, when a thin tissue layer differs in relative conductivity from its neighboring layers, it should be included in the model owing to its influence upon the EEG lead fields, i.e., the measurement sensitivity distributions.

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Bioelectric source measurements are influenced by the measurement location as well as the conductive properties of the tissues. Volume conductor effects such as the poorly conducting bones or the moderately conducting skin are known to affect the measurement precision and accuracy of the surface electroencephalography (EEG) measurements. This paper investigates the influence of age via skull conductivity upon surface and subdermal bipolar EEG measurement sensitivity conducted on two realistic head models from the Visible Human Project. Subdermal electrodes (a.k.a. subcutaneous electrodes) are implanted on the skull beneath the skin, fat, and muscles. We studied the effect of age upon these two electrode types according to the scalp-to-skull conductivity ratios of 5, 8, 15, and 30 : 1. The effects on the measurement sensitivity were studied by means of the half-sensitivity volume (HSV) and the region of interest sensitivity ratio (ROISR). The results indicate that the subdermal implantation notably enhances the precision and accuracy of EEG measurements by a factor of eight compared to the scalp surface measurements. In summary, the evidence indicates that both surface and subdermal EEG measurements benefit better recordings in terms of precision and accuracy on younger patients.

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We present the four key areas of research-preprocessing, the volume conductor, the forward problem, and the inverse problem-that affect the performance of EEG and MEG source imaging. In each key area we identify prominent approaches and methodologies that have open issues warranting further investigation within the community, challenges associated with certain techniques, and algorithms necessitating clarification of their implications. More than providing definitive answers we aim to identify important open issues in the quest of source localization.

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We have developed a multielectrode lead technique to improve the signal-to-noise ratio (SNR) of scalp-recorded electroencephalography (EEG) signals generated deep in the brain. The basis of the method lies in optimization of the measurement sensitivity distribution of the multielectrode lead. We claim that two factors improve the SNR in a multielectrode lead: (1) the sensitivity distribution of a multielectrode lead is more specific in measuring signals generated deep in the brain and (2) spatial averaging of noise occurs when several electrodes are applied in the synthesis of a multielectrode lead. We showed theoretically that within a three-layer spherical head model the sensitivity distributions of multielectrode leads are more specific for deep sources than those of traditional bipolar leads. We also estimated with simulations and with preliminary measurements the total improvement in SNR achieved by both the more specific lead field and spatial averaging. Results obtained with simulations and with experimental measurements show an apparent improvement in SNR obtained with multielectrode leads. This encourages for future development of the method.

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New portable electrocardiogram (ECG) measurement systems are emerging into market. Some use nonstandard bipolar electrode montage and sometimes very small interelectrode distances to improve the usability of the system. Modeling could provide a straightforward method to test new electrode systems. The aim of this study was to assess whether modeling the electrodes' measuring sensitivity with lead field method can provide a simple tool for testing a number of new electrode locations. We evaluated whether the actual ECG signal strength can be estimated by lead fields with two realistic 3D finite difference method (FDM) thorax models. We compared the modeling results to clinical body surface potential map (BSPM) data from 236 normal patients and studied 117 unipolar and 42 bipolar leads. In the case of unipolar electrodes the modeled measuring sensitivities correlated well with the clinical data (r=0.86, N=117, p<0.05). In the case of bipolar electrodes the correlation was moderate (r=0.62 between Model 1 and clinical data, r=0.71 between Model 2 and clinical data, N=42 and p<0.05 for both). Based on this we can conclude that lead field analysis based on realistic thorax models provides a good initial prediction for designing new electrode montages and measurement systems.

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We have developed a parameter, which describes how well the measurement is concentrated on the region of interest source area compared to other source areas in the volume conductor. The parameter concept is called the region of interest sensitivity ratio (ROISR). We assume that ROISR is also connected to the SNR of the measurement. The objective of the present study was to investigate the assumed correlation between the ROISR and SNR of the measurement with three-layer spherical head model. We studied how the source distribution and orientation affect the correlation and thus how applicable the ROISR is in analysing the sensitivity distributions of measurements. We simulated bipolar EEG-evoked potential measurements with 16 combinations of four-source distribution and four-source orientation models. The results indicate that the ROISR correlates with the SNR of the measurement with all tested source distributions and orientations. Thus the ROISRs concept can be applied to analyse measurement setups by modelling and analysing the sensitivity distributions.

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In this paper, we introduce a new modelling related parameter called region of interest sensitivity ratio (ROISR), which describes how well the sensitivity of an electroencephalography (EEG) measurement is concentrated within the region of interest (ROI), i.e. how specific the measurement is to the sources in ROI. We demonstrate the use of the concept by analysing the sensitivity distributions of bipolar EEG measurement. We studied the effects of interelectrode distance of a bipolar EEG lead on the ROISR with cortical and non-cortical ROIs. The sensitivity distributions of EEG leads were calculated analytically by applying a three-layer spherical head model. We suggest that the developed parameter has correlation to the signal-to-noise ratio (SNR) of a measurement, and thus we studied the correlation between ROISR and SNR with 254-channel visual evoked potential (VEP) measurements of two testees. Theoretical simulations indicate that source orientation and location have major impact on the specificity and therefore they should be taken into account when the optimal bipolar electrode configuration is selected. The results also imply that the new ROISR method bears a strong correlation to the SNR of measurement and can thus be applied in the future studies to efficiently evaluate and optimize EEG measurement setups.

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The present paper describes a study where effects of anterior myocardium on body surface potentials were investigated. The study combines numerical lead field analysis combined with cardiac automata model. Electric fields are calculated with finite difference method in a 3-D model of male thorax. The cardiac activation applied in the study is an ectopic beat originating in the apex. The correlations and mean differences between signal generated by anterior segments of left ventricle and signal generated by both ventricles were analysed for 117 leads. The results show that there are leads which have high correlation (>0.9) with low the relative mean difference (<0.2) between the signals generated by anterior segments and signals generated by whole ventricles. These electrode locations would be optimal to monitor the activation of anterior segments when ectopic beats originate in apex.

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European Commission has funded building a curriculum on Biomedical Engineering to the Internet for European universities under the project EVICAB. EVICAB forms a curriculum which will be free access and available free of charge. Therefore, in addition to the European universities, it will be available worldwide. EVICAB will make high quality education available for everyone, not only for the university students, and facilitate the development of the discipline of Biomedical Engineering.

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The purpose of the present study is to conduct preliminary experimental measurements to validate the improvement in the detection of deep EEG sources achieved with new multielectrode EEG leads. As a measurement we had brainstem auditory evoked potentials (BAEPs), which include deep generators in the brainstem and midbrain. The BAEPs were measured with a 124-channel EEG cap. We have previously developed a multielectrode lead technique, which has its basis in optimization of the sensitivity distribution of a multielectrode lead for detecting signals generated by deep sources. The purpose of the present study is to validate with experimental measurements the results previously obtained with theoretical approach and simulations. The results show that the amplitude SNR of BAEPs obtained with multielectrode lead is on average 1.6 times that of traditional bipolar BAEP lead. Though improvement obtained in experimental measurements is smaller than was theoretically approximated it encourages for further development of the multielectrode leads.

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The diagnostic performance of heart rate variability (HRV) analysis from exercise ECG in the detection of coronary artery disease (CAD) is unknown. Bicycle exercise ECG recordings from The Finnish Cardiovascular Study (FINCAVAS) of angiography-proofed CAD patients (n = 112) and a patient group with a low likelihood of CAD (n = 114) were analyzed. HRV parameters (SDNN, RMSSD, Poincaré SD1 and SD2) were calculated from 1 min segments before exercise, during exercise and after exercise. All the parameters were in addition calculated from heart rate (HR)-corrected RR-interval segments. The ST-segment depressions in each stage were also determined. The diagnostic performance of the parameters was evaluated with the area under the receiver operating characteristic (ROC) curve method. The uncorrected HRV parameters showed the best diagnostic performance in the recovery segments but the correlation with HR was also high (SDNN: 0.758/-0.64, RMSSD: 0.747/-0.60; area under the ROC/correlation coefficient). The HR correction decreased the correlation and the diagnostic performance in recovery segments (SDNN: 0.515/-0.12, RMSSD: 0.609/0.20). The diagnostic performance of ST-level at its best was higher than any of HRV parameters (ST-level: 0.795/0.36). According to the results, the HR correction decreased the diagnostic performance of the recovery phase. The HRV parameters calculated from 1 min segments of exercise test ECG were not as capable as traditional ST-segment analysis. In conclusion, the HRV analysis from exercise or recovery phase seems to be inadequate in the detection of CAD.

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To minimize time-consuming and expensive in vitro and in vivo testing, information regarding the effects of implantation and the implants on measurements should be available during the designing of active implantable devices measuring bioelectric signals such as electrocardiograms (ECG). Modeling offers a fairly inexpensive and effective means of studying and demonstrating the effects of implantation on ECG measurements prior to any in vivo tests, and can thus provide the designer with valuable information. Finite difference model (FDM) and lead field approaches offer straightforward and effective modeling methods supporting the designing of active implantable ECG devices. The present study demonstrates such methods in developing and studying ECG implants. They were applied in demonstrating the effects of implant dimensions and of electrode implantation on the measurement sensitivity of the ECG device. The results of the simulations indicated that the interelectrode distance is the factor of the implant design determining the lead sensitivity. Other parameters related implant dimensions and shape have minor effect on the morphology of the ECG or on the average sensitivity of the measurement. This is shown for example when the interelectrode distance was reduced to 1/3 of original the average lead sensitivity decreased by 69.1% while larger relative changes in other dimensions produced clearly smaller changes. It was also observed here that implanting the electrodes deeper under the skin has major effects on the local sensitivities in heart muscle and thus affect to the morphology of the ECG. The study indicated also that non-conducting medium (i.e. implant insulated body) between the electrodes increases the sensitivity on heart muscle compared to cases where only electrodes are implanted.

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The purpose of the present theoretical study was to examine the spatial resolution of electroencephalography (EEG) by means of the accuracy of the inverse cortical EEG solution. The study focused on effect of the amount of measurement noise and the number of electrodes on the spatial resolution with different resistivity ratios for the scalp, skull and brain. The results show that if the relative skull resistivity is lower than earlier believed, the spatial resolution of different electrode systems is less sensitive to the measurement noise. Furthermore, there is then also greater advantage to be obtained with high-resolution EEG at realistic noise levels.

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The objective in bioelectric measurements such as ECG and EEG is to register the signal arising from sources in the region of interest. It is also desired that signal-to-noise ratio (SNR) of a measurement is high. The sensitivity of an ideal measurement should focus on and be greater on the target areas in comparison to other areas of the volume conductor. Previously the half-sensitivity volume (HSV) has been applied to describe how focused the measurement is. In this paper we introduce a concept of the half-sensitivity ratio (HSR) which describes how well the sensitivity is concentrated in HSV compared to other source regions i.e. how specific the measurement is to the sources in HSV. Further we may have different region of interests (ROI) to which the measurements are wanted to be specific. Then the concept is called region of interest sensitivity ratio (ROISR). We present here an application of the HSR in analysing sensitivity distributions of bioelectric measurements. We studied the effects of interelectrode distance and the scalp/skull/brain resistivity ratio on the HSR of a bipolar EEG measurement with a three-layer spherical head model. The results indicate that when the focus of interest is on cortical activity more specified and concentrated sensitivity distributions are achieved with smaller interelectrode distances. Further a preliminary measurement with visual evoked potentials provides evidence of the relationship between HSR and SNR of a measurement.

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The purpose of the present study was to evaluate how the brain sources located at different depths can be most effectively measured with bipolar EEG leads. The specificity of an EEG lead to detect sources was studied with a new parameter called region of interest sensitivity ratio (ROISR) by employing a spherical head model. We studied the specificity as a function of electrode distance and further as a function of scalp:skull:brain resistivity ratio. The simulations indicate that the closer to the surface of the brain the source is located, the shorter is the interelectrode distance in the optimal lead. Also in the case of superficial sources, the small misplacement of the electrodes results in a substantial decrease in specificity. The resistivity ratio has the largest effect on the specificity, when the source is located close to the surface of the brain. However in the case of deep sources, the resistivity ratio has only minimal effect on the specificity.

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