The paper developed a block-wise approach for ICA algorithms which can improve the computational efficiency of ICA without the degradation of performance for the separation of biomedical signals. Source signals includ...
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The paper developed a block-wise approach for ICA algorithms which can improve the computational efficiency of ICA without the degradation of performance for the separation of biomedical signals. Source signals including electrocardiogram (ECG), electromyogram (EMG) and 60-Hz sinusoid are linearly mixed for experimental tests. The mean-square errors (MSE) between the original sources and the separated signals are calculated for the evaluation of separation performance. These results demonstrated that the proposed block-wise approach can achieve the desired separation performance of signals in a more efficient way.
For high range resolution acoustic vascular imaging we apply frequency domain interferometry and Capon method to a few frames of in-phase and quadrature (IQ) data acquired by a commercial ultrasonographic device. To s...
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For high range resolution acoustic vascular imaging we apply frequency domain interferometry and Capon method to a few frames of in-phase and quadrature (IQ) data acquired by a commercial ultrasonographic device. To suit the adaptive beamforming algorithm to medical acoustic imaging we employ three techniques; frequency averaging, whitening, and pseudo-double RF data conversion. The proposed method detected two couples of boundaries 0.26 and 0.19 mm apart using a single frame and two frames of IQ data, respectively, where each couple of boundaries is indistinguishable from a single boundary utilizing B-mode images. Further this algorithm could depict a swine femoral artery with higher range resolution than conventional B-mode imaging. These results indicate the potential of the proposed method for the range resolution improvement in ultrasonography, originating the progress in detection of vessel stenosis.
The clinical data stored in the health information system can be categorized as two types including structuralized data and non-structuralized ones. In the paper, a data extraction system is developed to assist data r...
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The clinical data stored in the health information system can be categorized as two types including structuralized data and non-structuralized ones. In the paper, a data extraction system is developed to assist data retrieval from the non-structuralized textual clinical documents such as radiology reports, pathology reports, etc. The system provides keyword-based and semantic-driven data matching methodology to extract the specific information from the textual clinical documents. The matching methodology provides the capabilities to recognize the selected keywords and the related semantics in the documents. Through the extraction verification interface, clinicians can extract and verify the matched information semi-automatically. The extracted data can be filled into predefined case-oriented templates. The structuralized data can be stored back into the clinical data warehouse for further analyzing. Moreover, the case-oriented templates can support collecting corresponding extracted data for various researches.
An important problem in Wireless Sensor Networks (WSN) is the occurrence of failures that lead to the disconnection of parts of the network, compromising the final results achieved by the WSN operation. A way to overc...
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An important problem in Wireless Sensor Networks (WSN) is the occurrence of failures that lead to the disconnection of parts of the network, compromising the final results achieved by the WSN operation. A way to overcome such problem is to provide a reliable connection to support the connectivity via other types of nodes that communicate with the sensor nodes. This paper proposes the usage of a network composed by Unmanned Aerial Vehicles (UAVs) as a relay network to guarantee the delivery of data produced by WSN nodes on the ground to the users. Results from simulations of the proposed technique are provided and discussed.
Sensor networks are being used in several emerging applications not even imagined some years ago due to advances in sensing, computing, and communication techniques. However, these advances also pose various challenge...
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A data mining framework has been proposed to estimate intracranial pressure (ICP) non-invasively in our previous work. In the corresponding approach, the feature vector extracted from arterial blood pressure (ABP) and...
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Sensor networks are being used in several emerging applications not even imagined some years ago due to advances in sensing, computing, and communication techniques. However, these advances also pose various challenge...
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Sensor networks are being used in several emerging applications not even imagined some years ago due to advances in sensing, computing, and communication techniques. However, these advances also pose various challenges that must be faced. One important challenge is related to the autonomous capability needed to setup and adapt the networks, which decentralizes the control of the network, saving communication and energy resources. Middleware technology helps in addressing this kind of problem, but there is still a need for additional solutions, particularly considering dynamic changes in users' requirements and operation conditions. This paper presents an agent-based framework acting as an integral part of a middleware to support autonomous setup and adaptation of sensor networks. It adds interoperability among heterogeneous nodes in the network, by means of autonomous behavior and reasoning. These features also address the needs for system setup and adaptations in the network, reducing the communication overhead and decentralizing the decision making mechanism. Additionally, preliminary results are also presented.
A data mining framework has been proposed to estimate intracranial pressure (ICP) non-invasively in our previous work. In the corresponding approach, the feature vector extracted from arterial blood pressure (ABP) and...
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A data mining framework has been proposed to estimate intracranial pressure (ICP) non-invasively in our previous work. In the corresponding approach, the feature vector extracted from arterial blood pressure (ABP) and flow velocity (FV) is translated to the estimated errors by the mapping function for each entry in the database. In this paper, three different mapping function solutions, linear least squares (LLS), truncated singular value decomposition (TSVD) and standard Tikhonov regularization (STR) are systemically tested to compare the possible effects of different solutions on the non-invasive ICP estimation. The conducted comparison demonstrated that the selection of mapping function solution actually influences the estimation. Among the tested three solutions for mapping function, TSVD and STR show better ICP estimation performance with smaller ICP errors than LLS.
Changes of ICP waveform morphology are characterized with different patients' states like hypertension, hydrocephalus and traumatic brain injury etc. Morphological clustering and analysis of ICP pulse (MOCAIP) app...
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Changes of ICP waveform morphology are characterized with different patients' states like hypertension, hydrocephalus and traumatic brain injury etc. Morphological clustering and analysis of ICP pulse (MOCAIP) approach is recently developed to extract ICP morphology feature, in which hierarchical clustering is used to extract the dominated pulse. In this paper, we evaluate the feasibility of using principle component analysis (PCA) and independent component analysis (ICA) to extract dominated pulse. The comparative study among clustering, PCA and ICP based approaches shows that PCA approach may be an alternative of clustering approach to extract dominated pulse in a faster fashion when dataset is of large size.
This report summarizes the proceedings of the second workshop of the 'Minimum Information for Biological and biomedical Investigations' (MIBBI) consortium held on Dec 1-2, 2010 in Rüdesheim, Germany throu...
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This report summarizes the proceedings of the second workshop of the 'Minimum Information for Biological and biomedical Investigations' (MIBBI) consortium held on Dec 1-2, 2010 in Rüdesheim, Germany through the sponsorship of the Beilstein-Institute. MIBBI is an umbrella organization uniting communities developing Minimum Information (MI) checklists to standardize the description of data sets, the workflows by which they were generated and the scientific context for the work. This workshop brought together representatives of more than twenty communities to present the status of their MI checklists and plans for future development. Shared challenges and solutions were identified and the role of MIBBI in MI checklist development was discussed. The meeting featured some thirty presentations, wide-ranging discussions and breakout groups. The top outcomes of the two-day workshop as defined by the participants were: 1) the chance to share best practices and to identify areas of synergy; 2) defining a series of tasks for updating the MIBBI Portal; 3) reemphasizing the need to maintain independent MI checklists for various communities while leveraging common terms and workflow elements contained in multiple checklists; and 4) revision of the concept of the MIBBI Foundry to focus on the creation of a core set of MIBBI modules intended for reuse by individual MI checklist projects while maintaining the integrity of each MI project. Further information about MIBBI and its range of activities can be found at http://***/.
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