| 1. |
- Brown, Shannon, et al.
(författare)
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Intarsia-sensorized band and textrodes for real-time myoelectric pattern recognition
- 2016
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Ingår i: Engineering in Medicine and Biology Society (EMBC), 2016 IEEE 38th Annual International Conference of the. - : Institute of Electrical and Electronics Engineers (IEEE). - 1557-170X. - 9781457702204 ; , s. 6074-6077
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Konferensbidrag (refereegranskat)abstract
- Surface Electromyography (sEMG) has applications in prosthetics, diagnostics and neuromuscular rehabilitation. Self-adhesive Ag/AgCl are the electrodes preferentially used to capture sEMG in short-term studies, however their long-term application is limited. In this study we designed and evaluated a fully integrated smart textile band with electrical connecting tracks knitted with intarsia techniques and knitted textile electrodes. Real-time myoelectric pattern recognition for motor volition and signal-to-noise ratio (SNR) were used to compare its sensing performance versus the conventional Ag-AgCl electrodes. After a comprehending measurement and performance comparison of the sEMG recordings, no significant differences were found between the textile and the Ag-AgCl electrodes in SNR and prediction accuracy obtained from pattern recognition classifiers.
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| 2. |
- Gu, Irene Yu-Hua, 1953, et al.
(författare)
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3D Limb Movement Tracking and Analysis for Neurological Dysfunctions of Neonates Using Multi-Camera Videos
- 2016
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Ingår i: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS. - 1557-170X. - 9781457702204 ; 2016-October, s. 2395-2398
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Konferensbidrag (refereegranskat)abstract
- Central nervous system dysfunction in infants may be manifested through inconsistent, rigid and abnormal limb movements. Detection of limb movement anomalies associated with such neurological dysfunctions in infants is the first step towards early treatment for improving infant development. This paper addresses the issue of detecting and quantifying limb movement anomalies in infants through non-invasive 3D image analysis methods using videos from multiple camera views. We propose a novel scheme for tracking 3D time trajectories of markers on infant’s limbs by video analysis techniques. The proposed scheme employ videos captured from three camera views. This enables us to detect a set of enhanced 3D markers through cross-view matching and to effectively handle marker self-occlusions by other body parts. We track a set of 3D trajectories of limb movements by a set of particle filters in parallel, enabling more robust 3D tracking of markers, and use the 3D model errors for quantifying abrupt limb movements. The proposed work makes a significant advancement to the previous work in [1] through employing tracking in 3D space, and hence overcome several main barriers that hinder real applications by using single camera-based techniques. To the best of our knowledge, applying such a multi-view video analysis approach for assessing neurological dysfunctions of infants through 3D time trajectories of markers on limbs is novel, and could lead to computer-aided tools for diagnosis of dysfunctions where early treatment may improve infant development. Experiments were conducted on multi-view neonate videos recorded in a clinical setting and results have provided further support to the proposed method.
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| 3. |
- Medved, Dennis, et al.
(författare)
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Selection of an optimal feature set to predict heart transplantation outcomes
- 2016
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Ingår i: 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016. - 1557-170X. - 9781457702204 ; 2016-October, s. 3290-3293
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Konferensbidrag (refereegranskat)abstract
- Heart transplantation (HT) is a life saving procedure, but a limited donor supply forces the surgeons to prioritize the recipients. The understanding of factors that predict mortality could help the doctors with this task. The objective of this study is to find locally optimal feature sets to predict survival of HT patients for different time periods. To this end, we applied logistic regression together with a greedy forward and backward search. As data source, we used the United Network for Organ Sharing (UNOS) registry, where we extracted adult patients (>17 years) from January 1997 to December 2008. As methods to predict survival, we used the Index for Mortality Prediction After Cardiac Transplantation (IMPACT) and the International Heart Transplant Survival Algorithm (IHTSA). We used the LIBLINEAR library together with the Apache Spark cluster computing framework to carry out the computation and we found feature sets for 1, 5, and 10 year survival for which we obtained area under the ROC curves (AUROC) of 68%, 68%, and 76%, respectively.
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| 4. |
- Anderson, Rachele, et al.
(författare)
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Insights on Spectral Measures for HRV Based on a Novel Approach for Data Acquisition
- 2018
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Ingår i: Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings. - 1557-170X. ; 2018, s. 510-513
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Tidskriftsartikel (refereegranskat)abstract
- In this paper, we present new insights on classical spectral measures for heart rate variability (HRV), based on a novel method for HRV acquisition. A dynamic breathing task, where the test participants are asked to breathe following a metronome with slowly increasing frequency, allows for the acquisition of respiratory-related HRV-data covering the frequency range in which adults breathe in different everyday situations. We discuss how the use of a time-frequency representation, e.g. the spectrogram or the Wigner-Ville distribution, should be preferred to the traditional use of the periodogram, due to the non-stationarity of the data. We argue that this approach can highlight the correlation of spectral measures such as low-frequency and high-frequency HRV with relevant factors as age, gender and Body-Mass-Index, thanks to the improved quality of the spectral measures.
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| 5. |
- Du, Jiaying, et al.
(författare)
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The effects of perceived USB-delay for sensor and embedded system development
- 2016
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Ingår i: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBSVolume 2016. - 9781457702204 ; , s. 2492-2495
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Konferensbidrag (refereegranskat)abstract
- Perceiving delay in computer input devices is a problem which gets even more eminent when being used in healthcare applications and/or in small, embedded systems. Therefore, the amount of delay found as acceptable when using computer input devices was investigated in this paper. A device was developed to perform a benchmark test for the perception of delay. The delay can be set from 0 to 999 milliseconds (ms) between a receiving computer and an available USB-device. The USB-device can be a mouse, a keyboard or some other type of USB-connected input device. Feedback from performed user tests with 36 people form the basis for the determination of time limitations for the USB data processing in microprocessors and embedded systems without users' noticing the delay. For this paper, tests were performed with a personal computer and a common computer mouse, testing the perception of delays between 0 and 500 ms. The results of our user tests show that perceived delays up to 150 ms were acceptable and delays larger than 300 ms were not acceptable at all.
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| 6. |
- Gao, Shi Chao, et al.
(författare)
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Data-driven estimation of blood pressure using photoplethysmographic signals
- 2016
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Ingår i: Proceedings of EMBC-16, 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. - : IEEE. - 9781457702204
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Konferensbidrag (refereegranskat)abstract
- Noninvasive measurement of blood pressure by optical methods receives considerable interest, but the complexity of the measurement and the difficulty of adjusting parameters restrict applications. We develop a method for estimating the systolic and diastolic blood pressure using a single-point optical recording of a photoplethysmographic (PPG) signal. The estimation is data-driven, we use automated machine learning algorithms instead of mathematical models. Combining supervised learning with a discrete wavelet transform, the method is insensitive to minor irregularities in the PPG waveform, hence both pulse oximeters and smartphone cameras can record the signal. We evaluate the accuracy of the estimation on 78 samples from 65 subjects (40 male, 25 female, age 29±7) with no history of cardiovascular disease. The estimate for systolic blood pressure has a mean error 4.9±4.9 mm Hg, and 4.3±3.7 mm Hg for diastolic blood pressure when using the oximeter-obtained PPG. The same values are 5.1±4.3 mm Hg and 4.6±4.3 mm Hg when using the phone-obtained PPG, comparing with A&D UA-767PBT result as gold standard. The simplicity of the method encourages ambulatory measurement, and given the ease of sharing the measured data, we expect a shift to data-oriented approaches deriving insight from ubiquitous mobile devices that will yield more accurate machine learning models in monitoring blood pressure.
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| 7. |
- Gavrilis, Dimitris, et al.
(författare)
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An Inelligent Assistant for Physicians
- 2016
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Ingår i: 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Orlando, FL, 17-20 August 2016. - Piscataway, NJ : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 2586-2589
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Konferensbidrag (refereegranskat)abstract
- This paper presents a software tool developed for assisting physicians during an examination process. The tool consists of a number of modules with the aim to make the examination process not only quicker but also fault proof moving from a simple electronic medical records management system towards an intelligent assistant for the physician. The intelligent component exploits users inputs as well as well established standards to line up possible suggestions for filling in the examination report. As the physician continues using it, the tool keeps extracting new knowledge. The architecture of the tool is presented in brief while the intelligent component which builds upon the notion of multilabel learning is presented in more detail. Our preliminary results from a real test case indicate that the performance of the intelligent module can reach quite high performance without a large amount of data.
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| 8. |
- Ge, Chenjie, 1991, et al.
(författare)
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Co-Saliency-Enhanced Deep Recurrent Convolutional Networks for Human Fall Detection in E-Healthcare
- 2018
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Ingår i: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS. - 1557-170X. ; , s. 1572-1575
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Konferensbidrag (refereegranskat)abstract
- This paper addresses the issue of fall detection from videos for e-healthcare and assisted-living. Instead of using conventional hand-crafted features from videos, we propose a fall detection scheme based on co-saliency-enhanced recurrent convolutional network (RCN) architecture for fall detection from videos. In the proposed scheme, a deep learning method RCN is realized by a set of Convolutional Neural Networks (CNNs) in segment-levels followed by a Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), to handle the time-dependent video frames. The co-saliency-based method enhances salient human activity regions hence further improves the deep learning performance. The main contributions of the paper include: (a) propose a recurrent convolutional network (RCN) architecture that is dedicated to the tasks of human fall detection in videos; (b) integrate a co-saliency enhancement to the deep learning scheme for further improving the deep learning performance; (c) extensive empirical tests for performance analysis and evaluation under different network settings and data partitioning. Experiments using the proposed scheme were conducted on an open dataset containing multicamera videos from different view angles, results have shown very good performance (test accuracy 98.96%). Comparisons with two existing methods have provided further support to the proposed scheme.
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| 9. |
- Ge, Chenjie, 1991, et al.
(författare)
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Deep Learning and Multi-Sensor Fusion for Glioma Classification Using Multistream 2D Convolutional Networks
- 2018
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Ingår i: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS. - 1557-170X. ; , s. 5894-5897
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Konferensbidrag (refereegranskat)abstract
- This paper addresses issues of brain tumor, glioma, grading from multi-sensor images. Different types of scanners (or sensors) like enhanced T1-MRI, T2-MRI and FLAIR, show different contrast and are sensitive to different brain tissues and fluid regions. Most existing works use 3D brain images from single sensor. In this paper, we propose a novel multistream deep Convolutional Neural Network (CNN) architecture that extracts and fuses the features from multiple sensors for glioma tumor grading/subcategory grading. The main contributions of the paper are: (a) propose a novel multistream deep CNN architecture for glioma grading; (b) apply sensor fusion from T1-MRI, T2-MRI and/or FLAIR for enhancing performance through feature aggregation; (c) mitigate overfitting by using 2D brain image slices in combination with 2D image augmentation. Two datasets were used for our experiments, one for classifying low/high grade gliomas, another for classifying glioma with/without 1p19q codeletion. Experiments using the proposed scheme have shown good results (with test accuracy of 90.87% for former case, and 89.39 % for the latter case). Comparisons with several existing methods have provided further support to the proposed scheme.
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| 10. |
- Grigorios-Aris, Cheimariotis, et al.
(författare)
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Automatic segmentation of lungs in SPECT images using active shape model trained by meshes delineated in CT images
- 2016
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Ingår i: 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016. - 9781457702204 ; 2016-October, s. 1280-1283
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Konferensbidrag (refereegranskat)abstract
- This paper presents a fully automated method for segmentation of 3D SPECT ventilation and perfusion images. It relies on statistical information on lung shape derived by CT manual segmentation and its main processing steps are: shape model extraction, binary segmentation, positioning of mean shape in SPECT images and iterative shape adaptation based on intensity profiles and on what is considered 'plausible' lung shape. The Active Shape Model is used to generate accurate anatomic results in SPECT images with functional information and thus unclear borders, especially in the case of pathologies. The method was compared against ground truth manual segmentation on CT images, using volumetric, difference dice coefficient, sensitivity and precision.
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