2013 IEEE Signal Processing in Medicine and Biology Symposium (SPMB)最新文献

{"title":"Improved clustering Of spike patterns through video segmentation and motion analysis of micro electrocorticographic data","authors":"Bugra Akyildiz, Yilin Song, J. Viventi, Yao Wang","doi":"10.1109/SPMB.2013.6736774","DOIUrl":"https://doi.org/10.1109/SPMB.2013.6736774","url":null,"abstract":"We have developed flexible, active, multiplexed recording devices for high resolution recording over large, clinically relevant areas in the brain. While this technology has enabled a much higher-resolution view of the electrical activity of the brain, the analytical methods to process, categorize and respond to the huge volumes of seizure data produced by these devices have not yet been developed. This paper examines a series of segmentation, feature extraction, and unsupervised clustering methods for interictal and itcal spike segmentation and spike pattern clustering. We first applied advanced video analysis techniques (particularly region growing and motion analysis) for spike segmentation and feature extraction. Then we examined the effectiveness of several different clustering methods for identifying natural clusters of the spike patterns using different features. These methdos have been applied to in-vivo feline seizure recordings. Based on both the similarity with a human clustering result and on the ratio of the intra-cluster vs. inter-cluster correlations, we found the best results by clustering using a Dirichlet Process Mixture Model on the correlation matrix of the spikes extracted using video segmentation. Effective clustering of spike patterns and subsequent analysis of the temporal variation of the spike pattern is an important step towards understanding how seizures initiate, progress and terminate.","PeriodicalId":182231,"journal":{"name":"2013 IEEE Signal Processing in Medicine and Biology Symposium (SPMB)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117013503","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Seizure detection using empirical mode decomposition and time-frequency energy concentration","authors":"Amal Feltane, G. F. Boudreaux Bartels, Yacine Boudria, W. Besio","doi":"10.1109/SPMB.2013.6736765","DOIUrl":"https://doi.org/10.1109/SPMB.2013.6736765","url":null,"abstract":"The aim of this study is to evaluate a new method for seizure detection using the tripolar Laplacian electroence-phalography signal (tEEG) recorded using a tripolar concentric ring electrode (TCRE) on the scalp surface of rats based on empirical mode decomposition (EMD) and time-frequency energy concentration. Data from 10 rats were examined with the proposed algorithm. After EMD decomposition, three oscillation components named intrinsic mode functions (IMFs) were selected. An energy estimate of the TFR for the selected IMFs was calculated and used as a feature for automatic seizure detection of the tEEG signals. After classification the obtained results using the proposed method produced an accuracy of 98.61%. This study developed the proposed algorithm to work with TCREs, and shows it to be effective to detect seizures from rat's tEEG signals.","PeriodicalId":182231,"journal":{"name":"2013 IEEE Signal Processing in Medicine and Biology Symposium (SPMB)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126036320","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Functional connectivity network based on graph analysis of scalp EEG for epileptic classification","authors":"S. Sargolzaei, M. Cabrerizo, M. Goryawala, A. S. Eddin, M. Adjouadi","doi":"10.1109/SPMB.2013.6736779","DOIUrl":"https://doi.org/10.1109/SPMB.2013.6736779","url":null,"abstract":"The proposed study presents a novel fully automated data-driven approach for differentiating epileptic subjects from normal controls using graph-based functional connectivity networks calculated using scalp EEG. A set of fourteen density-related, graph distance-based and spectral topological features extracted from the network graph is employed for the classification process. The proposed algorithm demonstrated an accuracy of 87.5% with a sensitivity of 75% and specificity of 100% when tested on 8 subjects. The study showed that graph-based functional connectivity networks in epileptic subjects were significantly different from those of controls (p<;0.05). The study has the potential for aiding neurologists in decision making for diagnostic purposes solely based on scalp EEG.","PeriodicalId":182231,"journal":{"name":"2013 IEEE Signal Processing in Medicine and Biology Symposium (SPMB)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127736020","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Preprocessing for improved computer aided detection in medical ultrasound","authors":"R. Mammone, S. Love, L. Barinov, W. Hulbert, A. Jairaj, C. Podilchuk","doi":"10.1109/SPMB.2013.6736776","DOIUrl":"https://doi.org/10.1109/SPMB.2013.6736776","url":null,"abstract":"Recently, a new speckle noise reduction and contrast enhancement technique has been introduced that is motivated by the research in compressive sampling or sensing. Compressive sampling is based on the principle that a sparse signal such as ultrasound can be fully recovered when sampled below the Nyquist rate. This allows for a new noise reduction technique that preserves the high frequency and fine details while reducing the effects of speckle noise. This method improves the overall perceptual quality of the image for visualization and diagnosis by the radiologist. This paper examines how the improvement in SNR makes the method suitable as a preprocessor to improve a computer aided detection (CAD) system for breast cancer detection. Classical performance metrics such as false positive rates, false negative rates and receiver operator curves will be used to show the benefits of this approach. Initial experiments look promising for microcalcification detection, where the new method yields a false negative rate of 20 percent at a false positive rate of 0.5 percent while the traditional speckle reduction techniques yield a false negative rate of 60 percent at a false positive rate of 0.5 percent.","PeriodicalId":182231,"journal":{"name":"2013 IEEE Signal Processing in Medicine and Biology Symposium (SPMB)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115846302","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Speckle reduction of medical ultrasound using Compressive Re-Sampling and instantaneous SNR","authors":"R. Mammone, L. Barinov, A. Jairaj, W. Hulbert, C. Podilchuk","doi":"10.1109/SPMB.2013.6736769","DOIUrl":"https://doi.org/10.1109/SPMB.2013.6736769","url":null,"abstract":"Medical Ultrasonography is a valuable imaging technology for medical diagnostics and to guide interventional procedures. However, ultrasound imaging suffers from speckle noise, an inherent characteristic of all coherent imaging techniques due to the presence of sub-resolution scatterers. Speckle noise produces a reduction in contrast resolution which is responsible for the overall lower effective resolution of ultrasound compared to x-ray or MRI imaging. In the case of breast imaging, ultrasound speckle can mask small details such as low contrast tumors or microcalcifications, which may be an early indication of breast cancer. This limitation prevents ultrasound from displacing mammography as the gold standard for breast cancer screening. Traditional speckle reduction techniques attempt to remove speckle noise while preserving edges and other important features but there is always a tradeoff between removing the speckle noise and blurring tissue structure and details. We introduce a novel speckle reduction and contrast enhancement method for ultrasound imaging that is motivated by the fundamental ideas behind compressive sampling. We also introduce a way to estimate instantaneous SNR in order to identify the areas that are mostly signal from the areas that are mostly noise in order to preserve the signal while suppressing the noise. We have shown improvements in SNR on the order of 12dB in the lab and improved visualization of clinical data.","PeriodicalId":182231,"journal":{"name":"2013 IEEE Signal Processing in Medicine and Biology Symposium (SPMB)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125445407","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimal chaotic synchronization of stochastic delayed recurrent neural networks","authors":"Ziqian Liu","doi":"10.1109/SPMB.2013.6736775","DOIUrl":"https://doi.org/10.1109/SPMB.2013.6736775","url":null,"abstract":"This paper presents a theoretical design of how an optimal synchronization is achieved for stochastic delayed recurrent neural networks. According to the concept of drive-response, a control method is developed to guarantee that the chaotic drive network synchronizes with the chaotic response network influenced by uncertain noise signals. The formulation of a nonlinear optimal control law is rigorously derived by using Lyapunov technique and solving a Hamilton-Jacobi-Bellman (HJB) equation. To verify the analytical results, a numerical example is given to demonstrate the effectiveness of the proposed approach, which is simple and easy to implement in reality.","PeriodicalId":182231,"journal":{"name":"2013 IEEE Signal Processing in Medicine and Biology Symposium (SPMB)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132065313","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Resting state fMRI data analysis using support vector machines","authors":"Xiaomu Song, N. Chen","doi":"10.1109/SPMB.2013.6736773","DOIUrl":"https://doi.org/10.1109/SPMB.2013.6736773","url":null,"abstract":"Resting state functional magnetic resonance imaging (fMRI) aims to measure baseline neuronal connectivity independent of functional tasks. Most existing network detection methods rely on a fixed threshold to identify functionally connected voxels under the resting state. Due to fMRI non-stationarity, a fixed threshold cannot adapt to inter-session and inter-subject variation. In this work, a new method is proposed for resting state fMRI data analysis. Specifically, the resting state network mapping is formulated as an outlier detection process that is implemented using one-class support vector machine (SVM). The results are refined by using a spatial-feature domain prototype selection method and two-class SVM reclassification. The final decision on each voxel is made by comparing its probabilities of functionally connected and unconnected. The proposed method was evaluated using synthetic and experimental fMRI data. A comparison study was also performed with independent component analysis (ICA) and correlation analysis. The experimental results show that the proposed method can provide comparable or better network detection performance than ICA and correlation analysis. The method is potentially applicable to various resting state quantitative fMRI studies.","PeriodicalId":182231,"journal":{"name":"2013 IEEE Signal Processing in Medicine and Biology Symposium (SPMB)","volume":"217 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122184438","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Initial assessment of artifact filtering for RSVP Keyboard™","authors":"M. Haghighi, M. Akçakaya, U. Orhan, Deniz Erdoğmuş, B. Oken, M. Fried-Oken","doi":"10.1109/SPMB.2013.6736777","DOIUrl":"https://doi.org/10.1109/SPMB.2013.6736777","url":null,"abstract":"RSVP Keyboard™ is an electroencephalography (EEG)-based spelling interface that uses evoked response potential classification with the help of language models. As in any brain computer interface, severe physiological and environmental signal artifacts that affect signal quality in EEG are a detriment to performance. To alleviate the negative effects of such artifacts on RSVP Keyboard™, we implemented a filter that is based on an existing methodology from the literature. Using statistical modeling of pre-recorded EEG that includes three types of artifacts intentionally generated by operators, we perform Monte Carlo simulations of copy-phrase tasks and analyze the effect of artifact filtering on estimated typing performance. The presented results demonstrate an evidence against the usability of the tested method for online artifact reduction applications.","PeriodicalId":182231,"journal":{"name":"2013 IEEE Signal Processing in Medicine and Biology Symposium (SPMB)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128535994","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Virtual musculoskeletal arm and robotic arm driven by a biomimetic model of sensorimotor cortex with reinforcement learning","authors":"S. Dura-Bernal, G. Chadderdon, S. Neymotin, Xianlian Zhou, A. Przekwas, J. Francis, W. Lytton","doi":"10.1109/SPMB.2013.6736768","DOIUrl":"https://doi.org/10.1109/SPMB.2013.6736768","url":null,"abstract":"Neocortical mechanisms of learning sensorimotor control involve a complex series of interactions at multiple levels, from synaptic mechanisms to network connectomics. We developed a model of sensory and motor cortex consisting of several hundred spiking model-neurons. A biomimetic model (BMM) was trained using spike-timing dependent reinforcement learning to drive a simple kinematic two-joint virtual arm in a motor task requiring convergence on a single target. After learning, networks demonstrated retention of behaviorally-relevant memories by utilizing proprioceptive information to perform reach-to-target from multiple starting positions. We utilized the output of this model to drive mirroring motion of a robotic arm. In order to improve the biological realism of the motor control system, we replaced the simple virtual arm model with a realistic virtual musculoskeletal arm which was interposed between the BMM and the robot arm. The virtual musculoskeletal arm received input from the BMM signaling neural excitation for each muscle. It then fed back realistic proprioceptive information, including muscle fiber length and joint angles, which were employed in the reinforcement learning process. The limb position information was also used to control the robotic arm, leading to more realistic movements. This work explores the use of reinforcement learning in a spiking model of sensorimotor cortex and how this is affected by the bidirectional interaction with the kinematics and dynamic constraints of a realistic musculoskeletal arm model. It also paves the way towards a full closed-loop biomimetic brain-effector system that can be incorporated in a neural decoder for prosthetic control, and used for developing biomimetic learning algorithms for controlling real-time devices. Additionally, utilizing biomimetic neuronal modeling in brain-machine interfaces offers the possibility for finer control of prosthetics, and the ability to better understand the brain.","PeriodicalId":182231,"journal":{"name":"2013 IEEE Signal Processing in Medicine and Biology Symposium (SPMB)","volume":"19 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116340639","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Emulating conventional disc electrode with the outer ring of the tripolar concentric ring electrode in phantom and human electroencephalogram data","authors":"O. Makeyev, Yacine Boudria, Zhenghan Zhu, Thomas Lennon, W. Besio","doi":"10.1109/SPMB.2013.6736778","DOIUrl":"https://doi.org/10.1109/SPMB.2013.6736778","url":null,"abstract":"Conventional electroencephalography (EEG) with disc electrodes has major drawbacks including poor spatial resolution, selectivity and low signal-to-noise ratio that critically limit its use. Concentric ring electrodes are a promising alternative with potential to improve all of the aforementioned aspects significantly. In our previous work, the tripolar concentric ring electrode (TCRE) was successfully used in a wide range of applications demonstrating its superiority to conventional disc electrodes, in particular, in accuracy of Laplacian estimation (tEEG). For applications that may benefit from simultaneous recording of EEG and tEEG in this paper we propose to use the signal from the outer ring of the TCRE as an emulation (eEEG) of EEG recorded using conventional disc electrodes. This will allow us to record EEG emulation from the exact same locations at the exact same time as the tEEG using a single recording system. Time domain neuronal signal synchrony was measured using cross-correlation in phantom and human experiments suggesting the potential of eEEG as an emulation of EEG.","PeriodicalId":182231,"journal":{"name":"2013 IEEE Signal Processing in Medicine and Biology Symposium (SPMB)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128050418","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}