Showing papers in "Medical & Biological Engineering & Computing in 2012"
TL;DR: Recent technological advances in electrochemotherapy are described, which allow treatment of liver and bone metastases, soft tissue sarcomas, brain tumors, and colorectal and esophageal tumors.
Abstract: Electrochemotherapy, a combination of high voltage electric pulses and of an anticancer drug, has been demonstrated to be highly effective in treatment of cutaneous and subcutaneous tumors. Unique properties of electrochemotherapy (e.g., high specificity for targeting cancer cells, high degree of localization of treatment effect, capacity for preserving the innate immune response and the structure of the extracellular matrix) are facilitating its wide spread in the clinics. Due to high effectiveness of electrochemotherapy in treatment of cutaneous and subcutaneous tumors regardless of histological origin, there are now attempts to extend its use to treatment of internal tumors. To advance the applicability of electrochemotherapy to treatment of internal solid tumors, new technological developments are needed that will enable treatment of these tumors in daily clinical practice. New electrodes through which electric pulses are delivered to target tissue need to be designed with the aim to access target tissue anywhere in the body. To increase the probability of complete tumor eradication, the electrodes have to be accurately positioned, first to provide an adequate extent of electroporation of all tumor cells and second not to damage critical healthy tissue or organs in its vicinity. This can be achieved by image guided insertion of electrodes that will enable accurate positioning of the electrodes in combination with patient-specific numerical treatment planning or using a predefined geometry of electrodes. In order to be able to use electrochemotherapy safely for treatment of internal tumors located in relative proximity of the heart (e.g., in case of liver metastases), the treatment must be performed without interfering with the heart’s electrical activity. We describe recent technological advances, which allow treatment of liver and bone metastases, soft tissue sarcomas, brain tumors, and colorectal and esophageal tumors. The first clinical experiences in these novel application areas of electrochemotherapy are also described.
192 citations
TL;DR: A novel multimodal medical image fusion (MIF) method based on non-subsampled contourlet transform (NSCT) and pulse-coupled neural network (PCNN) is presented, which exploits the advantages of both the NSCT and the PCNN to obtain better fusion results.
Abstract: In this article, a novel multimodal medical image fusion (MIF) method based on non-subsampled contourlet transform (NSCT) and pulse-coupled neural network (PCNN) is presented. The proposed MIF scheme exploits the advantages of both the NSCT and the PCNN to obtain better fusion results. The source medical images are first decomposed by NSCT. The low-frequency subbands (LFSs) are fused using the ‘max selection’ rule. For fusing the high-frequency subbands (HFSs), a PCNN model is utilized. Modified spatial frequency in NSCT domain is input to motivate the PCNN, and coefficients in NSCT domain with large firing times are selected as coefficients of the fused image. Finally, inverse NSCT (INSCT) is applied to get the fused image. Subjective as well as objective analysis of the results and comparisons with state-of-the-art MIF techniques show the effectiveness of the proposed scheme in fusing multimodal medical images.
134 citations
TL;DR: A comprehensive survey of the progress made in computational modeling of the human atria during the last 10 years, from simple “peanut”-like structures to very detailed models including atrial wall and fiber direction.
Abstract: This review article gives a comprehensive survey of the progress made in computational modeling of the human atria during the last 10 years. Modeling the anatomy has emerged from simple “peanut”-like structures to very detailed models including atrial wall and fiber direction. Electrophysiological models started with just two cellular models in 1998. Today, five models exist considering e.g. details of intracellular compartments and atrial heterogeneity. On the pathological side, modeling atrial remodeling and fibrotic tissue are the other important aspects. The bridge to data that are measured in the catheter laboratory and on the body surface (ECG) is under construction. Every measurement can be used either for model personalization or for validation. Potential clinical applications are briefly outlined and future research perspectives are suggested.
126 citations
TL;DR: This study uses the International 10-20 EEG electrode placement system to position a standard figure-of-eight TMS coil over 13 commonly adopted targets and provides the first pictorial and numerical atlas of TMS-induced electric fields for a range of coil positions.
Abstract: Computational models have been be used to estimate the electric and magnetic fields induced by transcranial magnetic stimulation (TMS) and can provide valuable insights into the location and spatial distribution of TMS stimulation. However, there has been little translation of these findings into practical TMS research. This study uses the International 10-20 EEG electrode placement system to position a standard figure-of-eight TMS coil over 13 commonly adopted targets. Using a finite element method and an anatomically detailed and realistic head model, this study provides the first pictorial and numerical atlas of TMS-induced electric fields for a range of coil positions. The results highlight the importance of subject-specific gyral folding patterns and of local thickness of subarachnoid cerebrospinal fluid (CSF). Our modelling shows that high electric fields occur primarily on the peaks of those gyri which have only a thin layer of CSF above them. These findings have important implications for inter-individual generalizability of the TMS-induced electric field. We propose that, in order to determine with accuracy the site of stimulation for an individual subject, it is necessary to solve the electric field distribution using subject-specific anatomy obtained from a high-resolution imaging modality such as MRI.
108 citations
TL;DR: The accuracy and safety of thoracic transpedicular screw placement with patient-specific drill template technique in scoliosis significantly reduces the operation time and radiation exposure for the members of the surgical team, making it a practical, simple and safe method.
Abstract: With the rapid increase in the use of thoracic pedicle screws in scoliosis, accurate and safe placement of screw within the pedicle is a crucial step during the scoliosis surgery. To make thoracic pedicle screw placement safer various techniques are used, Patient-specific drill template with pre-planned trajectory has been thought as a promising solution, it is critical to assess the efficacy, safety profile with this technique. In this paper, we develop and validate the accuracy and safety of thoracic transpedicular screw placement with patient-specific drill template technique in scoliosis. Patients with scoliosis requiring instrumentation were recruited. Volumetric CT scan was performed on each desired thoracic vertebra and a 3-D reconstruction model was generated from the CT scan data. The optimal screw size and orientation were determined and a drill template was designed with a surface that is the inverse of the posterior vertebral surface. The drill template and its corresponding vertebra were manufactured using rapid prototyping technique and tested for violations. The navigational template was sterilized and used intraoperatively to assist with the placement of thoracic screws. After surgery, the positions of the pedicle screws were evaluated using CT scan and graded for validation. This method showed its ability to customize the placement and the size of each pedicle screw based on the unique morphology of the thoracic vertebra. In all the cases, it was relatively very easy to manually place the drill template on the lamina of the vertebral body during the surgery. This method significantly reduces the operation time and radiation exposure for the members of the surgical team, making it a practical, simple and safe method. The potential use of such a navigational template to insert thoracic pedicle screws in scoliosis is promising. The use of surgical navigation system successfully reduced the perforation rate and insertion angle errors, demonstrating the clear advantage in safe and accurate pedicle screw placement of scoliosis surgery.
106 citations
TL;DR: In this article, the feasibility of percutaneous transcatheter aortic valve implantation in morphologies was explored using finite element (FE) modeling. But the authors did not consider the impact of morphological parameters on the performance of the TAVI.
Abstract: Transcatheter aortic valve implantation (TAVI) enables treatment of aortic stenosis with no need for open heart surgery. According to current guidelines, only patients considered at high surgical risk can be treated with TAVI. In this study, patient-specific analyses were performed to explore the feasibility of TAVI in morphologies, which are currently borderline cases for a percutaneous approach. Five patients were recruited: four patients with failed bioprosthetic aortic valves (stenosis) and one patient with an incompetent, native aortic valve. Three-dimensional models of the implantation sites were reconstructed from computed tomography images. Within these realistic geometries, TAVI with an Edwards Sapien stent was simulated using finite element (FE) modelling. Engineering and clinical outcomes were assessed. In all patients, FE analysis proved that TAVI was morphologically feasible. After the implantation, stress distribution showed no risks of immediate device failure and geometric orifice areas increased with low risk of obstruction of the coronary arteries. Maximum principal stresses in the arterial walls were higher in the model with native outflow tract. FE analyses can both refine patient selection and characterise device mechanical performance in TAVI, overall impacting on procedural safety in the early introduction of percutaneous heart valve devices in new patient populations.
104 citations
TL;DR: This review illustrates several modifications over classical technique done by many researchers and brings out the scopes of laser speckle-based analysis in various medical applications.
Abstract: Speckle pattern forms when a rough object is illuminated with coherent light (laser) and the backscattered radiation is imaged on a screen. The pattern changes over time due to movement in the object. Such time-integrate speckle pattern can be statistically analyzed to reveal the flow profile. For higher velocity the speckle contrast gets reduced. This theory can be utilized for tissue perfusion in capillaries of human skin tissue and cerebral blood flow mapping in rodents. Early, the technique was suffered from low resolution and computational intricacies for real-time monitoring purpose. However, modern engineering has made it feasible for real-time monitoring in microcirculation imaging with improved resolution. This review illustrates several modifications over classical technique done by many researchers. Recent advances in speckle contrast methods gain major interest, leading towards practical implementation of this technique. The review also brings out the scopes of laser speckle-based analysis in various medical applications.
100 citations
TL;DR: In this paper, the authors identify characteristic features in scalp EEG that simultaneously give the best discrimination between epileptic seizures and background EEG in minimally pre-processed scalp data; and have minimal computational complexity to be suitable for online, real-time analysis.
Abstract: This study identifies characteristic features in scalp EEG that simultaneously give the best discrimination between epileptic seizures and background EEG in minimally pre-processed scalp data; and have minimal computational complexity to be suitable for online, real-time analysis. The discriminative performance of 65 previously reported features has been evaluated in terms of sensitivity, specificity, area under the sensitivity–specificity curve (AUC), and relative computational complexity, on 47 seizures (split in 2,698 2 s sections) in over 172 h of scalp EEG from 24 adults. The best performing features are line length and relative power in the 12.5–25 Hz band. Relative power has a better seizure detection performance (AUC = 0.83; line length AUC = 0.77), but is calculated after the discrete wavelet transform and is thus more computationally complex. Hence, relative power achieves the best performance for offline detection, whilst line length would be preferable for online low complexity detection. These results, from the largest systematic study of seizure detection features, aid future researchers in selecting an optimal set of features when designing algorithms for both standard offline detection and new online low computational complexity detectors.
98 citations
TL;DR: The obtained results showed that age causes a major reduction in the mechanical parameters of healthy ascending aorta and that no significant differences are found between the mechanical strength of aneurysmal or BAV aortic specimens and the corresponding age-matched control group.
Abstract: The mechanical properties of aortic wall, both healthy and pathological, are needed in order to develop and improve diagnostic and interventional criteria, and for the development of mechanical models to assess arterial integrity. This study focuses on the mechanical behaviour and rupture conditions of the human ascending aorta and its relationship with age and pathologies. Fresh ascending aortic specimens harvested from 23 healthy donors, 12 patients with bicuspid aortic valve (BAV) and 14 with aneurysm were tensile-tested in vitro under physiological conditions. Tensile strength, stretch at failure and elbow stress were measured. The obtained results showed that age causes a major reduction in the mechanical parameters of healthy ascending aortic tissue, and that no significant differences are found between the mechanical strength of aneurysmal or BAV aortic specimens and the corresponding age-matched control group. The physiological level of the stress in the circumferential direction was also computed to assess the physiological operation range of healthy and diseased ascending aortas. The mean physiological wall stress acting on pathologic aortas was found to be far from rupture, with factors of safety (defined as the ratio of tensile strength to the mean wall stress) larger than six. In contrast, the physiological operation of pathologic vessels lays in the stiff part of the response curve, losing part of its function of damping the pressure waves from the heart.
97 citations
TL;DR: This article investigates in detail the practical performance of different implementations of the compressive sensing theory when applied to scalp EEG signals.
Abstract: Highly miniaturised, wearable computing and communication systems allow unobtrusive, convenient and long term monitoring of a range of physiological parameters. For long term operation from the physically smallest batteries, the average power consumption of a wearable device must be very low. It is well known that the overall power consumption of these devices can be reduced by the inclusion of low power consumption, real-time compression of the raw physiological data in the wearable device itself. Compressive sensing is a new paradigm for providing data compression: it has shown significant promise in fields such as MRI; and is potentially suitable for use in wearable computing systems as the compression process required in the wearable device has a low computational complexity. However, the practical performance very much depends on the characteristics of the signal being sensed. As such the utility of the technique cannot be extrapolated from one application to another. Long term electroencephalography (EEG) is a fundamental tool for the investigation of neurological disorders and is increasingly used in many non-medical applications, such as brain–computer interfaces. This article investigates in detail the practical performance of different implementations of the compressive sensing theory when applied to scalp EEG signals.
93 citations
TL;DR: A new protocol for mandibular reconstruction using CAD/CAM to construct custom-made guides and plates may represent a viable way to reproduce the patient’s anatomical contour, give the surgeon better procedural control, and reduce operation time.
Abstract: This paper describes a new protocol for mandibular reconstruction. Computer-aided design/computer-aided manufacturing (CAD/CAM) technology was used to manufacture custom-made cutting guides for tumor ablation and reconstructive plates to support fibula free flaps. CT scan data from a patient with an odontogenic keratocyst on the left mandibular ramus were elaborated to produce a virtual surgical plan of mandibular osteotomy in safe tissue for complete ramus resection. The CAD/CAM procedure was used to construct a customized surgical device composed of a cutting guide and a titanium reconstructive bone plate. The cutting guide allowed the surgeon to precisely transfer the virtual planned osteotomy into the surgical environment. The bone plate, including a custom-made anatomical condylar prosthesis, was designed using the outer surface of the healthy side of the mandible to obtain an ideal contour and avoid the bone deformities present on the side affected by the tumor. Operation time was reduced in the demolition and reconstruction phases. Functional and aesthetic outcomes allowed patients to immediately recover their usual appearance and functionality. This new protocol for mandibular reconstruction using CAD/CAM to construct custom-made guides and plates may represent a viable way to reproduce the patient's anatomical contour, give the surgeon better procedural control, and reduce operation time.
TL;DR: The iterative integral method (IIM) is a stable and relatively quick parameter identification method that can be applied in a broad variety of model configurations and is not susceptible to local minima and is thus, starting point and operator independent.
Abstract: Parameter identification methods are used to find optimal parameter values to fit models to measured data. The single integral method was defined as a simple and robust parameter identification method. However, the method did not necessarily converge to optimum parameter values. Thus, the iterative integral method (IIM) was developed. IIM will be compared to a proprietary nonlinear-least-squares-based Levenberg–Marquardt parameter identification algorithm using a range of reasonable starting values. Performance is assessed by the rate and accuracy of convergence for an exemplar two parameters insulin pharmacokinetic model, where true values are known a priori. IIM successfully converged to within 1% of the true values in all cases with a median time of 1.23 s (IQR 0.82–1.55 s; range 0.61–3.91 s). The nonlinear-least-squares method failed to converge in 22% of the cases and had a median (successful) convergence time of 3.29 s (IQR 2.04–4.89 s; range 0.42–44.9 s). IIM is a stable and relatively quick parameter identification method that can be applied in a broad variety of model configurations. In contrast to most established methods, IIM is not susceptible to local minima and is thus, starting point and operator independent.
TL;DR: It is considered that the cartilage-to-cartilage contact area and the peak contact pressure in the meniscus may be significant parameters in evaluating degenerative osteoarthritis.
Abstract: To investigate the effects of meniscectomy on degenerative osteoarthritis, a three-dimensional (3D) finite element (FE) model of the human lower limb is constructed from a combination of magnetic resonance (MR) images and computed tomographic (CT) images that can provide anatomically suitable boundary conditions for a knee joint. Four cases, i.e., the intact meniscus, and the partial, sub-total, and total meniscectomy of the medial meniscus are modeled and simulated. We consider that the cartilage-to-cartilage contact area and the peak contact pressure in the meniscus may be significant parameters in evaluating degenerative osteoarthritis. Partial meniscectomy can be regarded as a better treatment than sub-total/total meniscectomy, and a high possibility of degenerative osteoarthritis is anticipated after total meniscectomy. Moreover, medial meniscectomy has the potential to bring about degenerative osteoarthritis in both the medial compartment and the lateral compartment of a knee joint.
TL;DR: A full fluid–structure interaction (FSI) model is developed that is able to cope with arbitrary coaptation between the leaflets of the aortic valve during the closing phase and should be used to accurately resolve the kinematic details during the end-closing stage.
Abstract: While aortic valve root compliance and leaflet coaptation have significant influence on valve closure, their implications have not yet been fully evaluated. The present study developed a full fluid–structure interaction (FSI) model that is able to cope with arbitrary coaptation between the leaflets of the aortic valve during the closing phase. Two simplifications were also evaluated for the simulation of the closing phase only. One employs an FSI model with a rigid root and the other uses a “dry” (without flow) model. Numerical tests were performed to verify the model. New metrics were defined to process the results in terms of leaflet coaptation area and contact pressure. The axial displacement of the leaflets, closure time and coaptation parameters were similar in the two FSI models, whereas the dry model, with imposed uniform load on the leaflets, produced larger coaptation area and contact pressure, larger axial displacement and faster closure time compared with the FSI model. The differences were up to 30% in the coaptation area, 55% in the contact pressure and 170% in the closure time. Consequently, an FSI model should be used to accurately resolve the kinematics of the aortic valve and leaflet coaptation details during the end-closing stage.
TL;DR: A bio-realistic finite element model of the lumbar spine is developed using a comprehensive geometrical representation of spinal components and material laws that include strain rate dependency, bone fracture, and ligament failure to suggest that the strain rate dependent behavior of spinal component plays a significant role in load-sharing and failure mechanisms of the spine under different loading conditions.
Abstract: Despite an increase in the number of experimental and numerical studies dedicated to spinal trauma, the influence of the rate of loading or displacement on lumbar spine injuries remains unclear. In the present work, we developed a bio-realistic finite element model (FEM) of the lumbar spine using a comprehensive geometrical representation of spinal components and material laws that include strain rate dependency, bone fracture, and ligament failure. The FEM was validated against published experimental data and used to compare the initiation sites of spinal injuries under low (LD) and high (HD) dynamic compression, flexion, extension, anterior shear, and posterior shear. Simulations resulted in force-displacement and moment-angular rotation curves well within experimental corridors, with the exception of LD flexion where angular stiffness was higher than experimental values. Such a discrepancy is attributed to the initial toe-region of the ligaments not being included in the material law used in the study. Spinal injuries were observed at different initiation sites under LD and HD loading conditions, except under shear loads. These findings suggest that the strain rate dependent behavior of spinal components plays a significant role in load-sharing and failure mechanisms of the spine under different loading conditions. Bone fracture - Dynamic load - Finite element model - Ligament tear - Lumbar spine - Experimental validation
TL;DR: It is concluded that the excess pressure determines the excess work done by the ventricle, which may have clinically important implications.
Abstract: This study is based on the hypothesis that the pressure within the arterial network can be usefully decomposed as the sum of a reservoir pressure and an excess pressure. The reservoir pressure waveform is defined to be the same in each vessel but delayed by the wave travel time from the root of the aorta. Using calculus of variations and mass conservation, which relates the flow and rates of change of pressure in the vessels, we show that the reservoir pressure waveform minimises the ventricular hydraulic work for any physiologically or clinically reasonable ejection waveform and arterial properties, i.e. vessel compliances and terminal resistances. We conclude that the excess pressure determines the excess work done by the ventricle, which may have clinically important implications.
TL;DR: Steady-state somatosensory evoked potentials (SSSEPs) have been elicited applying vibro-tactile stimulation to all fingertips of the right hand using a 200-Hz carrier frequency modulated with a rectangular signal to determine if SSSEPs can be classified with a classifier based on unseen data.
Abstract: Steady-state somatosensory evoked potentials (SSSEPs) have been elicited applying vibro-tactile stimulation to all fingertips of the right hand. Nine healthy subjects participated in two sessions within this study. All fingers were stimulated 40 times with a 200-Hz carrier frequency modulated with a rectangular signal. The frequencies of the rectangular signal ranged between 17 and 35 Hz in 2 Hz steps. Relative band power tuning curves were calculated, introducing two different methods. Person-specific resonance-like frequencies were selected based on the data from the first session. The selected resonance-like frequencies were compared with the second session using an ANOVA for repeated measures to investigate the stability of SSSEPs over time. To determine, if SSSEPs can be classified with a classifier based on unseen data, an LDA classifier was trained with data from the first and applied to data from the second session. Person-specific resonance-like frequencies within a range from 19 to 29 Hz were found. The relative band power of the resonance-like frequencies did not differ significantly between the two sessions. Significant differences were found for the two methods and the used channels. SSSEPs were classified with a hit rate from 51 to 96 %.
TL;DR: The methodology of QRS detection, SDB analysis, calculation of ECG-derived respiration curves, and estimation of a sleep pattern is described in detail and underline the need for further testing in larger patient groups with different underlying diseases.
Abstract: The diagnosis of sleep-disordered breathing (SDB) usually relies on the analysis of complex polysomnographic measurements performed in specialized sleep centers. Automatic signal analysis is a promising approach to reduce the diagnostic effort. This paper addresses SDB and sleep assessment solely based on the analysis of a single-channel ECG recorded overnight by a set of signal analysis modules. The methodology of QRS detection, SDB analysis, calculation of ECG-derived respiration curves, and estimation of a sleep pattern is described in detail. SDB analysis detects specific cyclical variations of the heart rate by correlation analysis of a signal pattern and the heart rate curve. It was tested with 35 SDB-annotated ECGs from the Apnea-ECG Database, and achieved a diagnostic accuracy of 80.5%. To estimate sleep pattern, spectral parameters of the heart rate are used as stage classifiers. The reliability of the algorithm was tested with 18 ECGs extracted from visually scored polysomnographies of the SIESTA database; 57.7% of all 30 s epochs were correctly assigned by the algorithm. Although promising, these results underline the need for further testing in larger patient groups with different underlying diseases.
TL;DR: This study addressed layer-specific differences in the biomechanical response of ascending aortic aneusysms, obtained from patients during graft replacement using the Fung-type model, to shed light into the mechanisms promoting aneurysm dissection and rupture.
Abstract: This study addressed layer-specific differences in the biomechanical response of ascending aortic aneusysms, obtained from patients during graft replacement. Tensile tests were conducted on pairs of (orthogonally directed) intimal, medial, and adventitial strips from the anterior, posterior, and two lateral quadrants. The experimental data were reduced by the Fung-type model, affording appropriate characterization of the material properties. Testing of individual layers beyond rupture disclosed their failure properties, namely their capacity to bear varying deformation and stress levels. Material parameters \( c_{\theta \theta } \) and \( c_{zz} \), specifying circumferential and longitudinal stiffness, received the highest values in the adventitia or intima and the smallest in the media, with \( c_{\theta \theta } \) > \( c_{zz} \) in every layer but the intima. Similar extensibility at failure was found among layers, whereas the adventitia was the strongest of all. Circumferentially and longitudinally directed strips from each layer did not show uniform material parameters and failure properties among regions, but most differences did not reach significance. Medial and adventitial but not intimal layers were stronger circumferentially than longitudinally. This is the first study to place emphasis on the biomechanical properties of the distinct layers of human aneurysmal aorta that may be expected to shed light into the mechanisms promoting aneurysm dissection and rupture.
TL;DR: The influence of heterogeneous and anisotropic tissue on the electric field may be clinically relevant in anatomic regions that are functionally subdivided and surrounded by multiple fibres of passage.
Abstract: The aim was to quantify the influence of heterogeneous isotropic and heterogeneous anisotropic tissue on the spatial distribution of the electric field during deep brain stimulation (DBS). Three finite element tissue models were created of one patient treated with DBS. Tissue conductivity was modelled as (I) homogeneous isotropic, (II) heterogeneous isotropic based on MRI, and (III) heterogeneous anisotropic based on diffusion tensor MRI. Modelled DBS electrodes were positioned in the subthalamic area, the pallidum, and the internal capsule in each tissue model. Electric fields generated during DBS were simulated for each model and target-combination and visualized with isolevels at 0.20 (inner), and 0.05 V mm−1 (outer). Statistical and vector analysis was used for evaluation of the distribution of the electric field. Heterogeneous isotropic tissue altered the spatial distribution of the electric field by up to 4% at inner, and up to 10% at outer isolevel. Heterogeneous anisotropic tissue influenced the distribution of the electric field by up to 18 and 15% at each isolevel, respectively. The influence of heterogeneous and anisotropic tissue on the electric field may be clinically relevant in anatomic regions that are functionally subdivided and surrounded by multiple fibres of passage.
TL;DR: A way to record error potentials (ErrPs) during continuous feedback using time-coded motor imagery with only one pattern and the detection rate was above chance level which is a positive outcome and encourages further investigation.
Abstract: Patients who benefit from Brain-Computer Interfaces (BCIs) may have difficulties to generate more than one distinct brain pattern which can be used to control applications. Other BCI issues are low performance, accu- racy, and, depending on the type of BCI, a long preparation and/or training time. This study aims to show possible solu- tions. First, we used time-coded motor imagery (MI) with only one pattern. Second, we reduced the training time by recording only 20 trials of active MI to set up a BCI classifier. Third, we investigated a way to record error potentials (ErrPs) during continuous feedback. Ten subjects controlled an arti- ficial arm by performing MI over target time periods between 1 and 4 s. The subsequent movement of this arm served as continuous feedback. Discrete events, which are required to elicit ErrPs, were added by mounting blinking LEDs on top of the continuously moving arm to indicate the future move- ments. Time epochs after these events were used to evaluate ErrPs offline. The achieved error rate for the arm movement was on average 26.9%. Obtained ErrPs looked similar to results from the previous studies dealing with error detection and the detection rate was above chance level which is a positive outcome and encourages further investigation.
TL;DR: In this article, a comparison of a set of conventional and directed connectivity measures [cross-correlation, coherence, phase slope index (PSI), directed transfer function (DTF), partial-directed coherence (PDC), and transfer entropy (TE)] with eight-node simulation data was performed.
Abstract: Directional connectivity measures exist with different theoretical backgrounds, i.e., information theoretic, parametric-modeling based or phase related. In this paper, we perform the first comparison in this extend of a set of conventional and directed connectivity measures [cross-correlation, coherence, phase slope index (PSI), directed transfer function (DTF), partial-directed coherence (PDC) and transfer entropy (TE)] with eight-node simulation data based on real resting closed eye electroencephalogram (EEG) source signal. The ability of the measures to differentiate the direct causal connections from the non-causal connections was evaluated with the simulated data. Also, the effects of signal-to-noise ratio (SNR) and decimation were explored. All the measures were able to distinguish the direct causal interactions from the non-causal relations. PDC detected less non-causal connections compared to the other measures. Low SNR was tolerated better with DTF and PDC than with the other measures. Decimation affected most the results of TE, DTF and PDC. In conclusion, parametric-modeling-based measures (DTF, PDC) had the highest sensitivity of connections and tolerance to SNR in simulations based on resting closed eye EEG. However, decimation of data has to be carefully considered with these measures.
TL;DR: New flash patterns were designed to minimize TTI, decrease repetition blindness, and examine the temporal relationship between each flash of a given stimulus by placing a minimum of one, two, or three non-target flashes between each target flashes.
Abstract: Longer target-to-target intervals (TTI) produce greater P300 event-related potential amplitude, which can increase brain–computer interface (BCI) classification accuracy and decrease the number of flashes needed for accurate character classification. However, longer TTIs requires more time for each trial, which will decrease the information transfer rate of BCI. In this paper, a P300 BCI using a 7 × 12 matrix explored new flash patterns (16-, 18- and 21-flash pattern) with different TTIs to assess the effects of TTI on P300 BCI performance. The new flash patterns were designed to minimize TTI, decrease repetition blindness, and examine the temporal relationship between each flash of a given stimulus by placing a minimum of one (16-flash pattern), two (18-flash pattern), or three (21-flash pattern) non-target flashes between each target flashes. Online results showed that the 16-flash pattern yielded the lowest classification accuracy among the three patterns. The results also showed that the 18-flash pattern provides a significantly higher information transfer rate (ITR) than the 21-flash pattern; both patterns provide high ITR and high accuracy for all subjects.
TL;DR: In this work, outlier detection (focusing on bad contacts) is discussed for monopolar HDsEMG signals and a new method is proposed to identify ‘bad’ channels, showing that this method is promising.
Abstract: Recently developed techniques allow the analysis of surface EMG in multiple locations over the skin surface (high-density surface electromyography, HDsEMG). The detected signal includes information from a greater proportion of the muscle of interest than conventional clinical EMG. However, recording with many electrodes simultaneously often implies bad-contacts, which introduce large power-line interference in the corresponding channels, and short-circuits that cause near-zero single differential signals when using gel. Such signals are called 'outliers' in data mining. In this work, outlier detection (focusing on bad contacts) is discussed for monopolar HDsEMG signals and a new method is proposed to identify 'bad' channels. The overall performance of this method was tested using the agreement rate against three experts' opinions. Three other outlier detection methods were used for comparison. The training and test sets for such methods were selected from HDsEMG signals recorded in Triceps and Biceps Brachii in the upper arm and Brachioradialis, Anconeus, and Pronator Teres in the forearm. The sensitivity and specificity of this algorithm were, respectively, 96.9 ± 6.2 and 96.4 ± 2.5 in percent in the test set (signals registered with twenty 2D electrode arrays corresponding to a total of 2322 channels), showing that this method is promising.
TL;DR: The results indicate that the proposed approach can differentiate diseased tissue from normal tissue and can characterize the mechanical information of disease tissue, which means that this method can be applied as a means of abnormality localization to diagnose prostate cancers.
Abstract: Palpation is an intuitive examination procedure in which the kinesthetic and tactile sensations of the physician are used Although it has been widely used to detect and localize diseased tissues in many clinical fields, the procedure is subjective and dependent on the experience of the individual physician Palpation results and biomechanics-based mechanical property characterization are possible solutions that can enable the acquisition of objective and quantitative information on abnormal tissue localization during diagnosis and surgery This paper presents an integrated approach for robotic palpation combined with biomechanical soft tissue characterization In particular, we propose a new palpation method that is inspired by the actual finger motions that occur during palpation procedures To validate the proposed method, robotic palpation experiments on silicone soft tissue phantoms with embedded hard inclusions were performed and the force responses of the phantoms were measured using a robotic palpation system Furthermore, we carried out a numerical analysis, simulating the experiments and estimating the objective and quantitative properties of the tissues The results indicate that the proposed approach can differentiate diseased tissue from normal tissue and can characterize the mechanical information of diseased tissue, which means that this method can be applied as a means of abnormality localization to diagnose prostate cancers
TL;DR: Performance evaluation of a preliminary system prototype based on a fabric glove, with integrated textile electrodes placed at the fingertips, able to acquire and process the electrodermal response (EDR) to discriminate affective states is very satisfactory and promising in the field of affective computing.
Abstract: This paper reports on performance evaluation of a preliminary system prototype based on a fabric glove, with integrated textile electrodes placed at the fingertips, able to acquire and process the electrodermal response (EDR) to discriminate affective states. First, textile electrodes have been characterized in terms of voltage–current characteristics and trans-surface electric impedance. Next, signal quality of EDR acquired simultaneously from textile and standard electrodes was comparatively evaluated. Finally, a dedicated experiment in which 35 subjects were enrolled, aiming at discriminating different affective states using only EDR was designed and realized. A new set of features extracted from non-linear methods were used, improving remarkably successful recognition rates. Results are, indeed, very satisfactory and promising in the field of affective computing.
TL;DR: A new signal classification scheme for various types of RS based on multi-scale principal component analysis as a signal enhancement and feature extraction method to capture major variability of Fourier power spectra of the signal.
Abstract: Respiratory sound (RS) signals carry significant information about the underlying functioning of the pulmonary system by the presence of adventitious sounds. Although many studies have addressed the problem of pathological RS classification, only a limited number of scientific works have focused in multi-scale analysis. This paper proposes a new signal classification scheme for various types of RS based on multi-scale principal component analysis as a signal enhancement and feature extraction method to capture major variability of Fourier power spectra of the signal. Since we classify RS signals in a high dimensional feature subspace, a new classification method, called empirical classification, is developed for further signal dimension reduction in the classification step and has been shown to be more robust and outperform other simple classifiers. An overall accuracy of 98.34% for the classification of 689 real RS recording segments shows the promising performance of the presented method.
TL;DR: This work presents a method using several breath sound parameters to differentiate between the two respiratory phases, which is novel and independent of flow level; it requires only one prior- and one post-breath sound segment to identify the phase.
Abstract: Current breathing flow estimation methods use tracheal breath sounds, but one step of the process, 'breath phase (inspiration/expiration) detection', is done by either assuming alternating breath phases or using a second acoustic channel of lung sounds. The alternating assumption is unreliable for long recordings, non-breathing events, such as apnea, swallow or cough change the alternating nature of the phases. Using lung sounds intensity requires the addition of a secondary channel and the associated labor. Hence, an automatic and accurate method for breath-phase detection using only tracheal sounds would be of great benefit. We present a method using several breath sound parameters to differentiate between the two respiratory phases. The proposed method is novel and independent of flow level; it requires only one prior- and one post-breath sound segment to identify the phase. The proposed method was tested on data from 93 healthy individuals, without any history of pulmonary diseases breathing at 4 different flow levels. The most prominent features were from the duration, volume and shape of the sound envelope. This method has shown an accuracy of 95.6% with 95.5% sensitivity and 95.6% specificity for breath-phase identification without assuming breath-phase-alteration and/or using any other information.
TL;DR: The view that true tomographic imaging of fast neural activity in the brain is possible, at least with epicortical electrodes in the first instance is encouraged, as the predictions correlate well with independent experimental data.
Abstract: Electrical impedance tomography (EIT) is a medical imaging method with the potential to image resistance changes which occur during neuronal depolarisation in the brain with a resolution of milliseconds and millimetres. Most biomedical EIT is conducted with applied current over 10 kHz, as this reduces electrode impedance and so instrumentation artefact. However, impedance changes during neuronal depolarization are negligible at such frequencies. In order to estimate optimal recording frequency and specify instrumentation requirements, we have modelled their amplitude and frequency dependence during evoked activity using cable theory. Published values were used for the electrical properties and geometry of cell processes. The model was adjusted for the filtering effect of membrane capacitance and proportion of active neurons. At DC, resistance decreases by 2.8 % in crab nerve during the compound action potential and 0.6 % (range 0.06-1.7 %) locally in cerebral cortex during evoked physiological activity. Both predictions correlate well with independent experimental data. This encourages the view that true tomographic imaging of fast neural activity in the brain is possible, at least with epicortical electrodes in the first instance. It is essential to undertake this at low frequencies below about 100 Hz as above 1 kHz the signal becomes vanishingly small.
TL;DR: A kinematic approach for describing soft tissue artifacts (STA) in human movement analysis suggests that artifacts can be modeled effectively as a systematic relative rigid movement of the marker cluster with respect to the underlying bone.
Abstract: This paper proposes a kinematic approach for describing soft tissue artifacts (STA) in human movement analysis. Artifacts are represented as the field of relative displacements of markers with respect to the bone. This field has two components: deformation component (symmetric field) and rigid motion (skew-symmetric field). Only the skew-symmetric component propagates as an error to the joint variables, whereas the deformation component is filtered in the kinematic analysis process. Finally, a simple technique is proposed for analyzing the sources of variability to determine which part of the artifact may be modeled as an effect of the motion, and which part is due to other sources. This method has been applied to the analysis of the shank movement induced by vertical vibration in 10 subjects. The results show that the cluster deformation is very small with respect to the rigid component. Moreover, both components show a strong relationship with the movement of the tibia. These results suggest that artifacts can be modeled effectively as a systematic relative rigid movement of the marker cluster with respect to the underlying bone. This may be useful for assessing the potential effectiveness of the usual strategies for compensating for STA.