| 1. |
- Annala, Leevi, et al.
(författare)
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Generating Hyperspectral Skin Cancer Imagery using Generative Adversarial Neural Network.
- 2020
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Ingår i: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference. - 2694-0604. ; 2020, s. 1600-1603
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Tidskriftsartikel (refereegranskat)abstract
- In this study we develop a proof of concept of using generative adversarial neural networks in hyperspectral skin cancer imagery production. Generative adversarial neural network is a neural network, where two neural networks compete. The generator tries to produce data that is similar to the measured data, and the discriminator tries to correctly classify the data as fake or real. This is a reinforcement learning model, where both models get reinforcement based on their performance. In the training of the discriminator we use data measured from skin cancer patients. The aim for the study is to develop a generator for augmenting hyperspectral skin cancer imagery.
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| 2. |
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| 3. |
- Euler, Luisa, et al.
(författare)
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Textile Electrodes : Influence of Electrode Construction and Pressure on Stimulation Performance in Neuromuscular Electrical Stimulation (NMES)
- 2021
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Ingår i: Engineering in Medicine & Biology Society (EMBC), 2021 43rd Annual International Conference of the IEEE. - : IEEE. - 9781728111797 ; 2021, s. 1305-1308
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Konferensbidrag (refereegranskat)abstract
- The major reason for preventable hospital death isvenous thromboembolism (VTE). Non-pharmacologicaltreatment options include electrical stimulation or compressiontherapy to improve blood flow in the extremities. Textileelectrodes offer potential to replace bulky devices commonlyused in this field, thereby improving the user compliance. In thiswork, the performance of dry and wet knitted electrodes incombination with pressure application to the electrode wasevaluated in neuromuscular electrical stimulation (NMES). Amotor point stimulation on the calf was performed on ninehealthy subjects to induce a plantarflexion and the requiredstimulation intensity as well as the perceived pain were assessed.The performance of the different electrode constructions wascompared and the influence of the pressure application wasanalysed. The results show that wet textile electrodes (0.9 %saline solution) perform significantly better than dry electrodes.However, opportunities were found for improving theperformance of dry textile electrodes by using an uneven surfacetopography in combination with an intermediate to highpressure application to the electrode (> 20 mmHg), e.g. by usinga compression stocking. Moreover, the smaller of the two testedelectrode areas (16 cm2; 32 cm2) appears to be favourable interms of stimulation comfort and efficiency.
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| 4. |
- Johansson, Johannes D, et al.
(författare)
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DBSim and ELMA - Freeware for Simulations of Deep Brain Stimulation.
- 2022
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Ingår i: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference. - 2694-0604. ; 2022, s. 1719-1724
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Tidskriftsartikel (refereegranskat)abstract
- Finite Element Method (FEM) simulations of the electric field is a useful tool to estimate the activated tissue around Deep Brain Stimulation (DBS) electrodes. Based on our previous research, a two-part software package named DBSim and ELMA is presented. ELMA is used to classify brain tissue into grey matter, white matter, blood, and cerebrospinal fluid and assign electric conductivities accordingly. This data is then used in DBSim to generate patient-specific simulations of the electric field around currently implemented leads Medtronic 3387 and 3389, and Abbott 6180 and 6181. The software is available for free download at https://liu.se/en/article/ne-downloads Clinical Relevance- This is a tool meant for research and educational purposes for e.g. studies on optimal target areas for DBS.
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| 5. |
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| 6. |
- Lang, Victoria Ashley, et al.
(författare)
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Hand Temperature Is Not Consistent With Illusory Strength During the Rubber Hand Illusion.
- 2021
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Ingår i: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference. - 2694-0604. ; 2021, s. 1416-1418
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Tidskriftsartikel (refereegranskat)abstract
- The rubber hand illusion is known to invoke a sense of ownership of a rubber hand when a person watches the stroking of the rubber hand in synchrony with their own hidden hand. Quantification of the sense of ownership is traditionally performed with the rubber hand illusion questionnaire, but the search for reliable physiological measurements persists. Skin temperature has been previously suggested and debated as a biomarker for ownership. We investigated hand temperature as a measure of rubber hand illusory strength via thermal imaging of the hand during the rubber hand experiment. No relationship was found between reported illusory strength and skin temperature.Clinical Relevance- Our results indicate that skin temperature is not a suitable biomarker for rubber hand illusory strength.
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| 7. |
- Olsson, Alexander E., et al.
(författare)
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Can Deep Synthesis of EMG Overcome the Geometric Growth of Training Data Required to Recognize Multiarticulate Motions?
- 2021
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Ingår i: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference. - 2694-0604. ; 2021, s. 6380-6383
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Tidskriftsartikel (refereegranskat)abstract
- By being predicated on supervised machine learning, pattern recognition approaches to myoelectric prosthesis control require electromyography (EMG) training data collected concurrently with every detectable motion. Within this framework, calibration protocols for simultaneous control of multifunctional prosthetic hands rapidly become prohibitively long-the number of unique motions grows geometrically with the number of controllable degrees of freedom (DoFs). This paper proposes a technique intended to circumvent this combinatorial explosion. Using EMG windows from 1-DoF motions as input and EMG windows from 2-DoF motions as targets, we train generative deep learning models to synthesize EMG windows appertaining to multi-DoF motions. Once trained, such models can be used to complete datasets consisting of only 1-DoF motions, enabling simple calibration protocols with durations that scale linearly with the number of DoFs. We evaluated synthetic EMG produced in this way via a classification task using a database of forearm surface EMG collected during 1-DoF and 2-DoF motions. Multi-output classifiers were trained on either (I) real data from 1-DoF and 2-DoF motions, (II) real data from only 1-DoF motions, or (III) real data from 1-DoF motions appended with synthetic EMG from 2-DoF motions. When tested on data containing all possible motions, classifiers trained on synthetic-appended data (III) significantly outperformed classifiers trained on 1-DoF real data (II), although significantly underperformed classifiers trained on both 1- and 2-DoF real data (I) (I < 0.05). These findings suggest that it is feasible to model EMG concurrent with multiarticulate motions as nonlinear combinations of EMG from constituent 1-DoF motions, and that such modelling can be harnessed to synthesize realistic training data.
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| 8. |
- Papapanagiotou, V, et al.
(författare)
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Collecting big behavioral data for measuring behavior against obesity
- 2020
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Ingår i: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference. - 2694-0604. ; 2020, s. 5296-5299
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Tidskriftsartikel (refereegranskat)
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| 9. |
- Provenzale, Cecilia, et al.
(författare)
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Evaluating Handwriting Skills through Human-Machine Interaction : A New Digitalized System for Parameters Extraction
- 2022
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Ingår i: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). - 2694-0604. - 9781728127828 ; 44, s. 5128-5131
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Konferensbidrag (refereegranskat)abstract
- Handwriting is an important component of academic curricula and grapho-motor skills (GMS) support learning, reading, memory and self-confidence. Teachers and clinicians report increase in children experiencing problems with acquiring fluid and legible handwriting. To date gold-standard tests evaluating children's GMS, mostly rely on pen and paper tests, requiring extensive coding time and subject to high inter-rater variability. This work presents preliminary data on a new digital platform for Grapho-motor Handwriting Evaluation & Exercise (GHEE), attempting to overcome limitations of available digitalized methods for GMS evalution. In fact, contrary to previous systems, GHEE design originated from comparisons among multiple standardized tests and was based on a human-machine interaction approach. GHEE hardware and software is presented as well as data on preliminary testing. Cursive handwriting data from six adult volunteers was analyzed according to six parameters of relevance, both automatically (i.e., using GHEE software) and manually (i.e., by a human coder). Comparisons among machine and human data sets allowed parsing out parameters to be extracted automatically and parameters requiring human-machine interaction. Results confirmed platform efficacy and feasibility of the proposed approach.
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| 12. |
- Wahlquist, Ylva, et al.
(författare)
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Individualized closed-loop anesthesia through patient model partitioning
- 2020
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Ingår i: 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC'20). - 2694-0604. ; , s. 361-364
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Konferensbidrag (refereegranskat)abstract
- Closed-loop controlled drug dosing has the potential of revolutionizing clinical anesthesia. However, interpatient variability in drug sensitivity poses a central challenge to the synthesis of safe controllers. Identifying a full individual pharmacokinetic–pharmacodynamic (PKPD) model for this synthesis is clinically infeasible due to limited excitation of PKPD dynamics and presence of unmodeled disturbances. This work presents a novel method to mitigate inter-patient variability. It is based on: 1) partitioning an a priori known model set into subsets; 2) synthesizing an optimal robust controller for each subset; 3) classifying patients into one of the subsets online based on demographic or induction phase data; 4) applying the associated closed-loop controller. The method is investigated in a simulation study, utilizing a set of 47 clinically obtained patient models. Results are presented and discussed.Clinical relevance—The proposed method is easy to implement in clinical practice, and has potential to reduce the impact from surgical stimulation disturbances, and to result in safer closed-loop anesthesia with less risk of under- and over dosing.
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