Showing papers in "IEEE Transactions on Biomedical Engineering in 2009"
TL;DR: A new measure of dysphonia, pitch period entropy (PPE), is introduced, which is robust to many uncontrollable confounding effects including noisy acoustic environments and normal, healthy variations in voice frequency, and is well suited to telemonitoring applications.
Abstract: In this paper, we present an assessment of the practical value of existing traditional and nonstandard measures for discriminating healthy people from people with Parkinson's disease (PD) by detecting dysphonia. We introduce a new measure of dysphonia, pitch period entropy (PPE), which is robust to many uncontrollable confounding effects including noisy acoustic environments and normal, healthy variations in voice frequency. We collected sustained phonations from 31 people, 23 with PD. We then selected ten highly uncorrelated measures, and an exhaustive search of all possible combinations of these measures finds four that in combination lead to overall correct classification performance of 91.4%, using a kernel support vector machine. In conclusion, we find that nonstandard methods in combination with traditional harmonics-to-noise ratios are best able to separate healthy from PD subjects. The selected nonstandard methods are robust to many uncontrollable variations in acoustic environment and individual subjects, and are thus well suited to telemonitoring applications.
816 citations
TL;DR: The findings show that, although the wavelet transform approach can be used to characterize nonstationary signals, it does not perform as accurately as frequency-based features when classifying dynamic activities performed by healthy subjects.
Abstract: Driven by the demands on healthcare resulting from the shift toward more sedentary lifestyles, considerable effort has been devoted to the monitoring and classification of human activity. In previous studies, various classification schemes and feature extraction methods have been used to identify different activities from a range of different datasets. In this paper, we present a comparison of 14 methods to extract classification features from accelerometer signals. These are based on the wavelet transform and other well-known time- and frequency-domain signal characteristics. To allow an objective comparison between the different features, we used two datasets of activities collected from 20 subjects. The first set comprised three commonly used activities, namely, level walking, stair ascent, and stair descent, and the second a total of eight activities. Furthermore, we compared the classification accuracy for each feature set across different combinations of three different accelerometer placements. The classification analysis has been performed with robust subject-based cross-validation methods using a nearest-neighbor classifier. The findings show that, although the wavelet transform approach can be used to characterize nonstationary signals, it does not perform as accurately as frequency-based features when classifying dynamic activities performed by healthy subjects. Overall, the best feature sets achieved over 95% intersubject classification accuracy.
528 citations
TL;DR: This study investigated the use of surface electromyography (EMG) combined with pattern recognition (PR) to identify user locomotion modes and showed reliable classification for the seven tested modes.
Abstract: This study investigated the use of surface electromyography (EMG) combined with pattern recognition (PR) to identify user locomotion modes. Due to the nonstationary characteristics of leg EMG signals during locomotion, a new phase-dependent EMG PR strategy was proposed for classifying the user's locomotion modes. The variables of the system were studied for accurate classification and timely system response. The developed PR system was tested on EMG data collected from eight able-bodied subjects and two subjects with long transfemoral (TF) amputations while they were walking on different terrains or paths. The results showed reliable classification for the seven tested modes. For eight able-bodied subjects, the average classification errors in the four defined phases using ten electrodes located over the muscles above the knee (simulating EMG from the residual limb of a TF amputee) were 12.4% plusmn 5.0%, 6.0% plusmn 4.7%, 7.5% plusmn 5.1%, and 5.2% plusmn 3.7%, respectively. Comparable results were also observed in our pilot study on the subjects with TF amputations. The outcome of this investigation could promote the future design of neural-controlled artificial legs.
464 citations
TL;DR: An unsupervised algorithm is proposed to enhance P300 evoked potentials by estimating spatial filters; the raw EEG signals are projected into the estimated signal subspace, and the results show that the proposed method is efficient and accurate.
Abstract: A brain-computer interface (BCI) is a communication system that allows to control a computer or any other device thanks to the brain activity. The BCI described in this paper is based on the P300 speller BCI paradigm introduced by Farwell and Donchin. An unsupervised algorithm is proposed to enhance P300 evoked potentials by estimating spatial filters; the raw EEG signals are then projected into the estimated signal subspace. Data recorded on three subjects were used to evaluate the proposed method. The results, which are presented using a Bayesian linear discriminant analysis classifier, show that the proposed method is efficient and accurate.
451 citations
TL;DR: This paper presents a generic and patient-specific classification system designed for robust and accurate detection of ECG heartbeat patterns that can adapt to significant interpatient variations in ECG patterns by training the optimal network structure, and achieves higher accuracy over larger datasets.
Abstract: This paper presents a generic and patient-specific classification system designed for robust and accurate detection of ECG heartbeat patterns. The proposed feature extraction process utilizes morphological wavelet transform features, which are projected onto a lower dimensional feature space using principal component analysis, and temporal features from the ECG data. For the pattern recognition unit, feedforward and fully connected artificial neural networks, which are optimally designed for each patient by the proposed multidimensional particle swarm optimization technique, are employed. By using relatively small common and patient-specific training data, the proposed classification system can adapt to significant interpatient variations in ECG patterns by training the optimal network structure, and thus, achieves higher accuracy over larger datasets. The classification experiments over a benchmark database demonstrate that the proposed system achieves such average accuracies and sensitivities better than most of the current state-of-the-art algorithms for detection of ventricular ectopic beats (VEBs) and supra-VEBs (SVEBs). Over the entire database, the average accuracy-sensitivity performances of the proposed system for VEB and SVEB detections are 98.3%-84.6% and 97.4%-63.5%, respectively. Finally, due to its parameter-invariant nature, the proposed system is highly generic, and thus, applicable to any ECG dataset.
423 citations
TL;DR: A novel signal processing algorithm for the surface electromyogram (EMG) is proposed to extract simultaneous and proportional control information for multiple DOFs and a DOF-wise nonnegative matrix factorization is developed to estimate neural control information from the multichannel surface EMG.
Abstract: A novel signal processing algorithm for the surface electromyogram (EMG) is proposed to extract simultaneous and proportional control information for multiple DOFs. The algorithm is based on a generative model for the surface EMG. The model assumes that synergistic muscles share spinal neural drives, which correspond to the intended activations of different DOFs of natural movements and are embedded within the surface EMG. A DOF-wise nonnegative matrix factorization (NMF) is developed to estimate neural control information from the multichannel surface EMG. It is shown, both by simulation and experimental studies, that the proposed algorithm is able to extract the multidimensional control information simultaneously. A direct application of the proposed method would be providing simultaneous and proportional control of multifunction myoelectric prostheses.
415 citations
TL;DR: A proof of concept to an automatic fall detection system for elderly people based on floor vibration and sound sensing, and uses signal processing and pattern recognition algorithm to discriminate between fall events and other events.
Abstract: Falls are a major risk for the elderly people living independently. Rapid detection of fall events can reduce the rate of mortality and raise the chances to survive the event and return to independent living. In the last two decades, several technological solutions for detection of falls were published, but most of them suffer from critical limitations. In this paper, we present a proof of concept to an automatic fall detection system for elderly people. The system is based on floor vibration and sound sensing, and uses signal processing and pattern recognition algorithm to discriminate between fall events and other events. The classification is based on special features like shock response spectrum and mel frequency ceptral coefficients. For the simulation of human falls, we have used a human mimicking doll: ldquoRescue Randy.rdquo The proposed solution is unique, reliable, and does not require the person to wear anything. It is designed to detect fall events in critical cases in which the person is unconscious or in a stress condition. From the preliminary research, the proposed system can detect human mimicking dolls falls with a sensitivity of 97.5% and specificity of 98.6%.
331 citations
TL;DR: It is shown that it is possible to decode individual flexion and extension movements of each finger with greater than 90% accuracy in a transradial amputee using only noninvasive surface myoelectric signals.
Abstract: Upper limb prostheses are increasingly resembling the limbs they seek to replace in both form and functionality, including the design and development of multifingered hands and wrists. Hence, it becomes necessary to control large numbers of degrees of freedom (DOFs), required for individuated finger movements, preferably using noninvasive signals. While existing control paradigms are typically used to drive a single-DOF hook-based configurations, dexterous tasks such as individual finger movements would require more elaborate control schemes. We show that it is possible to decode individual flexion and extension movements of each finger (ten movements) with greater than 90% accuracy in a transradial amputee using only noninvasive surface myoelectric signals. Further, comparison of decoding accuracy from a transradial amputee and able-bodied subjects shows no statistically significant difference ( p < 0.05) between these subjects. These results are encouraging for the development of real-time control strategies based on the surface myoelectric signal to control dexterous prosthetic hands.
326 citations
TL;DR: In this article, a watershed-like algorithm was proposed to separate clustered nuclei from fluorescence microscopy cellular images, using shape markers and marking function in an adaptive H-minima transform.
Abstract: We present a method to separate clustered nuclei from fluorescence microscopy cellular images, using shape markers and marking function in a watershed-like algorithm. Shape markers are extracted using an adaptive H-minima transform. A marking function based on the outer distance transform is introduced to accurately separate clustered nuclei. With synthetic images, we quantitatively demonstrate the performance of our method and provide comparisons with existing approaches. On mouse neuronal and Drosophila cellular images, we achieved 6%-7% improvement of segmentation accuracies over earlier methods.
283 citations
TL;DR: The proposed computer tomography lung nodule computer-aided detection method has been trained and validated on a clinical dataset of 108 thoracic CT scans using a wide range of tube dose levels and shows much promise for clinical applications.
Abstract: In this paper, a new computer tomography (CT) lung nodule computer-aided detection (CAD) method is proposed for detecting both solid nodules and ground-glass opacity (GGO) nodules (part solid and nonsolid). This method consists of several steps. First, the lung region is segmented from the CT data using a fuzzy thresholding method. Then, the volumetric shape index map, which is based on local Gaussian and mean curvatures, and the ldquodotrdquo map, which is based on the eigenvalues of a Hessian matrix, are calculated for each voxel within the lungs to enhance objects of a specific shape with high spherical elements (such as nodule objects). The combination of the shape index (local shape information) and ldquodotrdquo features (local intensity dispersion information) provides a good structure descriptor for the initial nodule candidates generation. Antigeometric diffusion, which diffuses across the image edges, is used as a preprocessing step. The smoothness of image edges enables the accurate calculation of voxel-based geometric features. Adaptive thresholding and modified expectation-maximization methods are employed to segment potential nodule objects. Rule-based filtering is first used to remove easily dismissible nonnodule objects. This is followed by a weighted support vector machine (SVM) classification to further reduce the number of false positive (FP) objects. The proposed method has been trained and validated on a clinical dataset of 108 thoracic CT scans using a wide range of tube dose levels that contain 220 nodules (185 solid nodules and 35 GGO nodules) determined by a ground truth reading process. The data were randomly split into training and testing datasets. The experimental results using the independent dataset indicate an average detection rate of 90.2%, with approximately 8.2 FP/scan. Some challenging nodules such as nonspherical nodules and low-contrast part-solid and nonsolid nodules were identified, while most tissues such as blood vessels were excluded. The method's high detection rate, fast computation, and applicability to different imaging conditions and nodule types shows much promise for clinical applications.
282 citations
TL;DR: A new discriminative filter bank (FB) common spatial pattern algorithm to extract subject-specific FB for MI classification enhances the classification accuracy in BCI competition III dataset IVa and competition IV dataset IIb.
Abstract: Event-related desynchronization/synchronization patterns during right/left motor imagery (MI) are effective features for an electroencephalogram-based brain-computer interface (BCI). As MI tasks are subject-specific, selection of subject-specific discriminative frequency components play a vital role in distinguishing these patterns. This paper proposes a new discriminative filter bank (FB) common spatial pattern algorithm to extract subject-specific FB for MI classification. The proposed method enhances the classification accuracy in BCI competition III dataset IVa and competition IV dataset IIb. Compared to the performance offered by the existing FB-based method, the proposed algorithm offers error rate reductions of 17.42% and 8.9% for BCI competition datasets III and IV, respectively.
TL;DR: A multichannel electrogmyography sensor system capable of receiving and processing signals from up to 32 implanted myoelectric sensors (IMES) designed for permanent long-term implantation with no servicing requirements and tested in animals.
Abstract: We have developed a multichannel electrogmyography sensor system capable of receiving and processing signals from up to 32 implanted myoelectric sensors (IMES). The appeal of implanted sensors for myoelectric control is that electromyography (EMG) signals can be measured at their source providing relatively cross-talk-free signals that can be treated as independent control sites. An external telemetry controller receives telemetry sent over a transcutaneous magnetic link by the implanted electrodes. The same link provides power and commands to the implanted electrodes. Wireless telemetry of EMG signals from sensors implanted in the residual musculature eliminates the problems associated with percutaneous wires, such as infection, breakage, and marsupialization. Each implantable sensor consists of a custom-designed application-specified integrated circuit that is packaged into a biocompatible RF BION capsule from the Alfred E. Mann Foundation. Implants are designed for permanent long-term implantation with no servicing requirements. We have a fully operational system. The system has been tested in animals. Implants have been chronically implanted in the legs of three cats and are still completely operational four months after implantation.
TL;DR: A new computer-aided system aimed for bleeding region detection in CE images is proposed, which exploits color texture feature, an important clue used by physicians, to analyze status of gastrointestinal tract.
Abstract: Capsule endoscopy (CE) has been widely used to diagnose diseases in human digestive tract. However, a tough problem of this new technology is that too many images to be inspected by eyes cause a huge burden to physicians, so it is significant to investigate computerized diagnosis methods. In this paper, a new computer-aided system aimed for bleeding region detection in CE images is proposed. This new system exploits color texture feature, an important clue used by physicians, to analyze status of gastrointestinal tract. We put forward a new idea of chrominance moment as the color part of color texture feature, which makes full use of Tchebichef polynomials and illumination invariant of hue/saturation/intensity color space. Combined with uniform local binary pattern, a current texture representation model, it can be applied to discriminate normal regions and bleeding regions in CE images. Classification of bleeding regions using multilayer perceptron neural network is then deployed to verify performance of the proposed color texture features. Experimental results on our bleeding image data show that the proposed scheme is promising in detecting bleeding regions.
TL;DR: Parylene C was found to provide encapsulation and electrical insulation required for such neural interface devices for more than one year and oxygen plasma etching was finding to be an effective method to etch and pattern Parylene-C films.
Abstract: Electronic neural interfaces have been developed to restore function to the nervous system for patients with neural disorders. A conformal and chronically stable dielectric encapsulation is required to protect the neural interface device from the harsh physiological environment and localize the active electrode tips. Chemical vapor deposited Parylene-C films were studied as a potential implantable dielectric encapsulation material using impedance spectroscopy and leakage current measurements. Both tests were performed in 37degC saline solution, and showed that the films provided an electrically insulating encapsulation for more than one year. Isotropic and anisotropic oxygen plasma etching processes were compared for removing the Parylene-C insulation to expose the active electrode tips. Also, the relationship between tip exposure and electrode impedance was determined. The conformity and the uniformity of the Parylene-C coating were assessed using optical microscopy, and small thickness variations on the complex 3-D electrode arrays were observed. Parylene C was found to provide encapsulation and electrical insulation required for such neural interface devices for more than one year. Also, oxygen plasma etching was found to be an effective method to etch and pattern Parylene-C films.
TL;DR: The measured raw MES signals are rotated by class-specific principal component matrices to spatially decorrelate the measured data prior to feature extraction to allow a pattern recognition classifier to better discriminate the test motions.
Abstract: Information extracted from multiple channels of the surface myoelectric signal (MES) recording sites can be used as inputs to control systems for powered upper limb prostheses. For small, closely spaced muscles, such as the muscles in the forearm, the detected MES often contains contributions from more than one muscle, the contribution from each specific muscle being modified by the dispersive propagation through the volume conductor between the muscle and the detection points. In this paper, the measured raw MES signals are rotated by class-specific principal component matrices to spatially decorrelate the measured data prior to feature extraction. This ldquotunesrdquo the data to allow a pattern recognition classifier to better discriminate the test motions. This processing technique was used to significantly (p<0.01) reduce pattern recognition classification error for both intact limbed and transradial amputee subjects.
TL;DR: A new method that uses the pulse oximeter signal to estimate the respiratory rate using a recently developed time-frequency spectral estimation method, variable-frequency complex demodulation (VFCDM), to identify frequency modulation (FM) of the photoplethysmogram waveform.
Abstract: We present a new method that uses the pulse oximeter signal to estimate the respiratory rate. The method uses a recently developed time-frequency spectral estimation method, variable-frequency complex demodulation (VFCDM), to identify frequency modulation (FM) of the photoplethysmogram waveform. This FM has a measurable periodicity, which provides an estimate of the respiration period. We compared the performance of VFCDM to the continuous wavelet transform (CWT) and autoregressive (AR) model approaches. The CWT method also utilizes the respiratory sinus arrhythmia effect as represented by either FM or AM to estimate respiratory rates. Both CWT and AR model methods have been previously shown to provide reasonably good estimates of breathing rates that are in the normal range (12-26 breaths/min). However, to our knowledge, breathing rates higher than 26 breaths/min and the real-time performance of these algorithms are yet to be tested. Our analysis based on 15 healthy subjects reveals that the VFCDM method provides the best results in terms of accuracy (smaller median error), consistency (smaller interquartile range of the median value), and computational efficiency (less than 0.3 s on 1 min of data using a MATLAB implementation) to extract breathing rates that varied from 12-36 breaths/min.
TL;DR: Experimental results show that moderate OA and minimal OA can be differentiated from normal cases with accuracy of 91.5% and 80.4%, respectively.
Abstract: We describe a method for automated detection of radiographic osteoarthritis (OA) in knee X-ray images. The detection is based on the Kellgren-Lawrence (KL) classification grades, which correspond to the different stages of OA severity. The classifier was built using manually classified X-rays, representing the first four KL grades ( normal, doubtful, minimal, and moderate). Image analysis is performed by first identifying a set of image content descriptors and image transforms that are informative for the detection of OA in the X-rays and assigning weights to these image features using Fisher scores. Then, a simple weighted nearest neighbor rule is used in order to predict the KL grade to which a given test X-ray sample belongs. The dataset used in the experiment contained 350 X-ray images classified manually by their KL grades. Experimental results show that moderate OA (KL grade 3) and minimal OA (KL grade 2) can be differentiated from normal cases with accuracy of 91.5% and 80.4%, respectively. Doubtful OA (KL grade 1) was detected automatically with a much lower accuracy of 57%. The source code developed and used in this study is available for free download at www.openmicroscopy.org.
TL;DR: A refined MSE is proposed (RMSE), which overcomes two limitations of MSE and confirms the complementary information that can be derived by observing complexity as a function of the temporal scale.
Abstract: Multiscale entropy (MSE) was proposed to characterize complexity as a function of the time-scale factor tau. Despite its broad use, this technique suffers from two limitations: (1) the artificial MSE reduction due to the coarse graining procedure and (2) the introduction of spurious MSE oscillations due to the suboptimal procedure for the elimination of the fast temporal scales. We propose a refined MSE (RMSE), and we apply it to simulations and to 24-h Holter recordings of heart rate variability (HRV) obtained from healthy and aortic stenosis (AS) groups. The study showed that the refinement relevant to the elimination of the fast temporal scales was more helpful at short scales (spanning the range of short-term HRV oscillations), while that relevant to the procedure of coarse graining was more useful at large scales. In healthy subjects, during daytime, RMSE was smaller at short scales (i.e., tau =1-2) and larger at longer scales (i.e., tau =4-20) than during nighttime. In AS population, RMSE was smaller during daytime both at short and long time scales (i.e., tau = 1 -11) than during nighttime. RMSE was larger in healthy group than in AS population during both daytime (i.e., tau = 2 -9) and nighttime (i.e., tau = 2). RMSE overcomes two limitations of MSE and confirms the complementary information that can be derived by observing complexity as a function of the temporal scale.
TL;DR: It is concluded that beamforming constitutes an alternative method for SF that might be particularly useful for BCIs used in clinical settings, i.e., in an environment where artifact-free datasets are difficult to obtain.
Abstract: Spatial filtering (SF) constitutes an integral part of building EEG-based brain-computer interfaces (BCIs). Algorithms frequently used for SF, such as common spatial patterns (CSPs) and independent component analysis, require labeled training data for identifying filters that provide information on a subject's intention, which renders these algorithms susceptible to overfitting on artifactual EEG components. In this study, beamforming is employed to construct spatial filters that extract EEG sources originating within predefined regions of interest within the brain. In this way, neurophysiological knowledge on which brain regions are relevant for a certain experimental paradigm can be utilized to construct unsupervised spatial filters that are robust against artifactual EEG components. Beamforming is experimentally compared with CSP and Laplacian spatial filtering (LP) in a two-class motor-imagery paradigm. It is demonstrated that beamforming outperforms CSP and LP on noisy datasets, while CSP and beamforming perform almost equally well on datasets with few artifactual trials. It is concluded that beamforming constitutes an alternative method for SF that might be particularly useful for BCIs used in clinical settings, i.e., in an environment where artifact-free datasets are difficult to obtain.
TL;DR: A wirelessly operated, minimally invasive retinal prosthesis was developed for preclinical chronic implantation studies in Yucatan minipig models and verified both in vitro and in vivo in three pigs for more than seven months.
Abstract: A wirelessly operated, minimally invasive retinal prosthesis was developed for preclinical chronic implantation studies in Yucatan minipig models. The implant conforms to the outer wall of the eye and drives a microfabricated polyimide stimulating electrode array with sputtered iridium oxide electrodes. This array is implanted in the subretinal space using a specially designed ab externo surgical technique that fixes the bulk of the prosthesis to the outer surface of the sclera. The implanted device is fabricated on a host polyimide flexible circuit. It consists of a 15-channel stimulator chip, secondary power and data receiving coils, and discrete power supply components. The completed device is encapsulated in poly(dimethylsiloxane) except for the reference/counter electrode and the thin electrode array. In vitro testing was performed to verify the performance of the system in biological saline using a custom RF transmitter circuit and primary coils. Stimulation patterns as well as pulse strength, duration, and frequency were programmed wirelessly using custom software and a graphical user interface. Wireless operation of the retinal implant has been verified both in vitro and in vivo in three pigs for more than seven months, the latter by measuring stimulus artifacts on the eye surface using contact lens electrodes.
TL;DR: This paper introduces and demonstrates a novel brain-machine interface (BMI) architecture based on the concepts of reinforcement learning (RL), coadaptation, and shaping, and quantifies BMI performance in closed-loop brain control over six to ten days for three rats as a function of increasing task difficulty.
Abstract: This paper introduces and demonstrates a novel brain-machine interface (BMI) architecture based on the concepts of reinforcement learning (RL), coadaptation, and shaping. RL allows the BMI control algorithm to learn to complete tasks from interactions with the environment, rather than an explicit training signal. Coadaption enables continuous, synergistic adaptation between the BMI control algorithm and BMI user working in changing environments. Shaping is designed to reduce the learning curve for BMI users attempting to control a prosthetic. Here, we present the theory and in vivo experimental paradigm to illustrate how this BMI learns to complete a reaching task using a prosthetic arm in a 3-D workspace based on the user's neuronal activity. This semisupervised learning framework does not require user movements. We quantify BMI performance in closed-loop brain control over six to ten days for three rats as a function of increasing task difficulty. All three subjects coadapted with their BMI control algorithms to control the prosthetic significantly above chance at each level of difficulty.
TL;DR: An integrated framework for detecting peripheral sympathetic responses through purely imaging means is introduced and the first time that the maxillary area is analyzed, which opens a new research area that leads to unobtrusive screening technologies in neurophysiology and psychophysiology.
Abstract: In the present paper, we introduce an integrated framework for detecting peripheral sympathetic responses through purely imaging means The measurements are performed on three facial areas of sympathetic importance, that is, periorbital, supraorbital, and maxillary To the best of our knowledge, this is the first time that the sympathetic importance of the maxillary area is analyzed Because the imaging measurements are thermal in nature and are composed of multiple components of variable frequency (ie, blood flow, sweat gland activation, and breathing), we chose wavelets as the image analysis framework The measurements also carry substantial noise due to imperfections in tissue tracking and segmentation The image analysis is grounded on galvanic skin response (GSR) signals, which are still considered the golden standard in peripheral neurophysiological and psychophysiological studies The experimental results show that monitoring of the facial channels yields similar detecting power to GSR's However, detailed quantification of the responses, although feasible in GSR through appropriate modeling, is quite difficult in the facial channels for the moment Further improvements in facial tissue tracking and segmentation are bound to overcome this limitation This paper opens a new research area that leads to unobtrusive screening technologies in neurophysiology and psychophysiology
TL;DR: A generalizable description of the micrometer-scale penetration mechanics and material properties of mouse brain tissue in vivo is presented and can be applied to the quantitative design of most future implantable devices.
Abstract: Substantial advancement in the understanding of the neuronal basis of behavior and the treatment of neurological disorders has been achieved via the implantation of various devices into the brain. To design and optimize the next generation of neuronal implants while striving to minimize tissue damage, it is necessary to understand the mechanics of probe insertion at relevant length scales. Unfortunately, a broad-based understanding of brain-implant interactions at the necessary micrometer scales is largely missing. This paper presents a generalizable description of the micrometer-scale penetration mechanics and material properties of mouse brain tissue in vivo. Cylindrical stainless steel probes were inserted into the cerebral cortex and olfactory bulb of mice. The effects of probe size, probe geometry, insertion rate, insertion location, animal age, and the presence of the dura and pia on the resulting forces were measured continuously throughout probe insertion and removal. Material properties (modulus, cutting force, and frictional force) were extracted using mechanical analysis. The use of rigid, incompressible, cylindrical probes allows for a general understanding of how probe design and insertion methods influence the penetration mechanics of brain tissue in vivo that can be applied to the quantitative design of most future implantable devices.
TL;DR: This paper presents a method for obstructive sleep apnea screening based on the electrocardiogram (ECG) recording during sleep, and shows that the two classifiers are able to separate entirely (100%) the normal recordings from the apneic recordings.
Abstract: This paper presents a method for obstructive sleep apnea (OSA) screening based on the electrocardiogram (ECG) recording during sleep. OSA is a common sleep disorder produced by repetitive occlusions in the upper airways and this phenomenon can usually be observed also in other peripheral systems such as the cardiovascular system. Then the extraction of ECG characteristics, such as the RR intervals and the area of the QRS complex, is useful to evaluate the sleep apnea in noninvasive way. In the presented analysis, 50 recordings coming from the apnea Physionet database were used; data were split into two sets, the training and the testing set, each of which was composed of 25 recordings. A bivariate time-varying autoregressive model (TVAM) was used to evaluate beat-by-beat power spectral densities for both the RR intervals and the QRS complex areas. Temporal and spectral features were changed on a minute-by-minute basis since apnea annotations where given with this resolution. The training set consisted of 4950 apneic and 7127 nonapneic minutes while the testing set had 4428 apneic and 7927 nonapneic minutes. The K-nearest neighbor (KNN) and neural networks (NN) supervised learning classifiers were employed to classify apnea and non apnea minutes. A sequential forward selection was used to select the best feature subset in a wrapper setting. With ten features the KNN algorithm reached an accuracy of 88%, sensitivity equal to 85%, and specificity up to 90%, while NN reached accuracy equal to 88%, sensitivity equal to 89% and specificity equal to 86%. In addition to the minute-by-minute classification, the results showed that the two classifiers are able to separate entirely (100%) the normal recordings from the apneic recordings. Finally, an additional database with eight recordings annotated as normal or apneic was used to test again the classifiers. Also in this new dataset, the results showed a complete separation between apneic and normal recordings.
TL;DR: Results show that the proposed method is preferable to the other methods in the context of artifact removal by achieving a better tradeoff between removing artifacts and preserving inherent brain activities.
Abstract: An automatic electroencephalogram (EEG) artifact removal method is presented in this paper. Compared to past methods, it has two unique features: 1) a weighted version of support vector machine formulation that handles the inherent unbalanced nature of component classification and 2) the ability to accommodate structural information typically found in component classification. The advantages of the proposed method are demonstrated on real-life EEG recordings with comparisons made to several benchmark methods. Results show that the proposed method is preferable to the other methods in the context of artifact removal by achieving a better tradeoff between removing artifacts and preserving inherent brain activities. Qualitative evaluation of the reconstructed EEG epochs also demonstrates that after artifact removal inherent brain activities are largely preserved.
TL;DR: This study worked toward 2-D semiautomatic segmentation of both normal and degenerated lumbar intervertebral discs from T2-weighted midsagittal MR images of the spine by developing three variations of atlas-based segmentation using a probabilistic disc atlas.
Abstract: Intervertebral disc degeneration is an age-associated condition related to chronic back pain, while its consequences are responsible for over 90% of spine surgical procedures. In clinical practice, MRI is the modality of reference for diagnosing disc degeneration. In this study, we worked toward 2-D semiautomatic segmentation of both normal and degenerated lumbar intervertebral discs from T2-weighted midsagittal MR images of the spine. This task is challenged by partial volume effects and overlapping gray-level values between neighboring tissue classes. To overcome these problems three variations of atlas-based segmentation using a probabilistic atlas of the intervertebral disc were developed and their accuracies were quantitatively evaluated against manually segmented data. The best overall performance, when considering the tradeoff between segmentation accuracy and time efficiency, was accomplished by the atlas-robust-fuzzy c-means approach, which combines prior anatomical knowledge by means of a rigidly registered probabilistic disc atlas with fuzzy clustering techniques incorporating smoothness constraints. The dice similarity indexes of this method were 91.6% for normal and 87.2% for degenerated discs. Research in progress utilizes the proposed approach as part of a computer-aided diagnosis system for quantification and characterization of disc degeneration severity. Moreover, this approach could be exploited in computer-assisted spine surgery.
TL;DR: It is concluded that the instrumented shoe concept allows accurate and continuous estimation of CoM displacement under ambulatory conditions.
Abstract: The center of mass (CoM) and the center of pressure (CoP) are two variables that are crucial in assessing energy expenditure and stability of human walking. The purpose of this study is to estimate the CoM displacement continuously using an ambulatory measurement system. The measurement system consists of instrumented shoes with 6 DOF force/moment sensors beneath the heels and the fore-feet. Moreover, two inertial sensors are rigidly attached to the force/moment sensors for the estimation of position and orientation. The estimation of CoM displacement is achieved by fusing low-pass filtered CoP data with high-pass filtered double integrated CoM acceleration, both estimated using the instrumented shoes. Optimal cutoff frequencies for the low-pass and high-pass filters appeared to be 0.2 Hz for the horizontal direction and 0.5 Hz for the vertical direction. The CoM estimation using this ambulatory measurement system was compared to CoM estimation using an optical reference system based on the segmental kinematics method. The rms difference of each component of the CoM displacement averaged over a hundred trials obtained from seven stroke patients was (0.020 plusmn 0.007) m (mean plusmn standard deviation) for the forward x-direction, (0.013 plusmn 0.005) m for the lateral y-direction, and (0.007 plusmn 0.001) m for the upward z-direction. Based on the results presented in this study, it is concluded that the instrumented shoe concept allows accurate and continuous estimation of CoM displacement under ambulatory conditions.
TL;DR: Results show the system has the ability to sense and replicate motion to within 1 mm and 1deg in the axial and radial directions, respectively.
Abstract: A novel remote catheter navigation system has been developed to reduce physical stress and irradiation to the interventionalist during fluoroscopic X-ray guided catheter intervention. The unique teleoperated design of this system allows the interventionalist to apply conventional axial and radial motion, as used in current practice, to an input catheter placed in a radiation-safe location to control a second catheter placed inside the procedure room. A catheter sensor (used to measure motion of the input catheter) and a catheter manipulator (used to manipulate the second catheter) are both presented. Performance evaluation of the system was assessed by first conducting bench-top experiments to quantify accuracy and precision of both sensed and replicated motion, and then conducting two experiments to evaluate the latency from sensed to replicated motion. The first study consisted of replicating motions of prescribed motion trajectories, while the second study utilized eight operators to remotely navigate a catheter through a normal carotid model. The results show the system has the ability to sense and replicate motion to within 1 mm and 1deg in the axial and radial directions, respectively. Remote catheter manipulation was found to be operator dependent and occurred under 300 ms. Future applications of this technology are then presented.
TL;DR: With the ability to capture a large range and extract concave shapes, FVF demonstrates improvements over techniques like gradient vector flow, boundary vectors flow, and magnetostatic active contour on three sets of experiments.
Abstract: In this paper, we propose a new approach that we call the ldquofluid vector flowrdquo (FVF) active contour model to address problems of insufficient capture range and poor convergence for concavities. With the ability to capture a large range and extract concave shapes, FVF demonstrates improvements over techniques like gradient vector flow, boundary vector flow, and magnetostatic active contour on three sets of experiments: synthetic images, pediatric head MRI images, and brain tumor MRI images from the Internet brain segmentation repository.
TL;DR: A finite-element model of a realistic irregularly shaped biological cell in an external electric field that allows the calculation of time-dependent changes of the induced transmembrane voltage ( DeltaPsi) and simulation of cell membrane electroporation is described and it is shown that steady-state models are insufficient for accurate description and determination of electroporated regions of the membrane.
Abstract: We describe a finite-element model of a realistic irregularly shaped biological cell in an external electric field that allows the calculation of time-dependent changes of the induced transmembrane voltage ( DeltaPsi) and simulation of cell membrane electroporation. The model was first tested by comparing its results to the time-dependent analytical solution for DeltaPsi on a nonporated spherical cell, and a good agreement was obtained. To simulate electroporation, the model was extended by introducing a variable membrane conductivity. In the regions exposed to a sufficiently high DeltaPsi, the membrane conductivity rapidly increased with time, leading to a modified spatial distribution of DeltaPsi. We show that steady-state models are insufficient for accurate description of DeltaPsi, as well as determination of electroporated regions of the membrane, and time-dependent models should be used instead. Our modeling approach also allows direct comparison of calculations and experiments. As an example, we show that calculated regions of electroporation correspond to the regions of molecular transport observed experimentally on the same cell from which the model was constructed. Both the time-dependent model of DeltaPsi and the model of electroporation can be exploited further to study the behavior of more complicated cell systems, including those with cell-to-cell interactions.