On the Role of Training Data for SVM-Based Microwave Brain Stroke Detection and Classification
The aim of this work was to test microwave brain stroke detection and classification using support vector machines (SVMs). We tested how the nature and variability of training data and system parameters impact the achieved classification accuracy. Using experimentally verified numerical models, a large database of synthetic training and test data was created. … full description
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- Sensors (Basel, Switzerland) Vol. 23; no. 4; p. 2031
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01. 02. 2023
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- 10.3390/s23042031
| Abstract | The aim of this work was to test microwave brain stroke detection and classification using support vector machines (SVMs). We tested how the nature and variability of training data and system parameters impact the achieved classification accuracy. Using experimentally verified numerical models, a large database of synthetic training and test data was created. The models consist of an antenna array surrounding reconfigurable geometrically and dielectrically realistic human head phantoms with virtually inserted strokes of arbitrary size, and different dielectric parameters in different positions. The generated synthetic data sets were used to test four different hypotheses, regarding the appropriate parameters of the training dataset, the appropriate frequency range and the number of frequency points, as well as the level of subject variability to reach the highest SVM classification accuracy. The results indicate that the SVM algorithm is able to detect the presence of the stroke and classify it (i.e., ischemic or hemorrhagic) even when trained with single-frequency data. Moreover, it is shown that data of subjects with smaller strokes appear to be the most suitable for training accurate SVM predictors with high generalization capabilities. Finally, the datasets created for this study are made available to the community for testing and developing their own algorithms. |
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| AbstractList | The aim of this work was to test microwave brain stroke detection and classification using support vector machines (SVMs). We tested how the nature and variability of training data and system parameters impact the achieved classification accuracy. Using experimentally verified numerical models, a large database of synthetic training and test data was created. The models consist of an antenna array surrounding reconfigurable geometrically and dielectrically realistic human head phantoms with virtually inserted strokes of arbitrary size, and different dielectric parameters in different positions. The generated synthetic data sets were used to test four different hypotheses, regarding the appropriate parameters of the training dataset, the appropriate frequency range and the number of frequency points, as well as the level of subject variability to reach the highest SVM classification accuracy. The results indicate that the SVM algorithm is able to detect the presence of the stroke and classify it (i.e., ischemic or hemorrhagic) even when trained with single-frequency data. Moreover, it is shown that data of subjects with smaller strokes appear to be the most suitable for training accurate SVM predictors with high generalization capabilities. Finally, the datasets created for this study are made available to the community for testing and developing their own algorithms. |
| ArticleNumber | 2031 |
| Audience | Academic |
| Author | Salucci, Marco Vrba, David Fiser, Ondrej Pokorny, Tomas Novak, Marek Polo, Alessandro Drizdal, Tomas Tosi, Luca Vrba, Jan |
| AuthorAffiliation | 1 Department of Biomedical Technology, Faculty of Biomedical Engineering, Czech Technical University in Prague, 166 00 Prague, Czech Republic 2 ELEDIA Research Center (ELEDIA@UniTN—University of Trento), DICAM—Department of Civil, Environmental, and Mechanical Engineering, Via Mesiano 77, 38123 Trento, Italy |
| AuthorAffiliation_xml | – name: 2 ELEDIA Research Center (ELEDIA@UniTN—University of Trento), DICAM—Department of Civil, Environmental, and Mechanical Engineering, Via Mesiano 77, 38123 Trento, Italy – name: 1 Department of Biomedical Technology, Faculty of Biomedical Engineering, Czech Technical University in Prague, 166 00 Prague, Czech Republic |
| Author_xml | – sequence: 1 givenname: Tomas orcidid: 0000-0001-7628-6031 surname: Pokorny fullname: Pokorny, Tomas organization: Department of Biomedical Technology, Faculty of Biomedical Engineering, Czech Technical University in Prague, 166 00 Prague, Czech Republic – sequence: 2 givenname: Jan orcidid: 0000-0001-6528-0187 surname: Vrba fullname: Vrba, Jan organization: Department of Biomedical Technology, Faculty of Biomedical Engineering, Czech Technical University in Prague, 166 00 Prague, Czech Republic – sequence: 3 givenname: Ondrej orcidid: 0000-0001-8259-0611 surname: Fiser fullname: Fiser, Ondrej organization: Department of Biomedical Technology, Faculty of Biomedical Engineering, Czech Technical University in Prague, 166 00 Prague, Czech Republic – sequence: 4 givenname: David orcidid: 0000-0002-8631-4283 surname: Vrba fullname: Vrba, David organization: Department of Biomedical Technology, Faculty of Biomedical Engineering, Czech Technical University in Prague, 166 00 Prague, Czech Republic – sequence: 5 givenname: Tomas orcidid: 0000-0001-9061-8231 surname: Drizdal fullname: Drizdal, Tomas organization: Department of Biomedical Technology, Faculty of Biomedical Engineering, Czech Technical University in Prague, 166 00 Prague, Czech Republic – sequence: 6 givenname: Marek orcidid: 0000-0001-9255-2651 surname: Novak fullname: Novak, Marek organization: Department of Biomedical Technology, Faculty of Biomedical Engineering, Czech Technical University in Prague, 166 00 Prague, Czech Republic – sequence: 7 givenname: Luca orcidid: 0000-0003-2089-2483 surname: Tosi fullname: Tosi, Luca email: ELEDIA@UniTN-University organization: ELEDIA Research Center (ELEDIA@UniTN-University of Trento), DICAM-Department of Civil, Environmental, and Mechanical Engineering, Via Mesiano 77, 38123 Trento, Italy – sequence: 8 givenname: Alessandro surname: Polo fullname: Polo, Alessandro email: ELEDIA@UniTN-University organization: ELEDIA Research Center (ELEDIA@UniTN-University of Trento), DICAM-Department of Civil, Environmental, and Mechanical Engineering, Via Mesiano 77, 38123 Trento, Italy – sequence: 9 givenname: Marco orcidid: 0000-0002-6948-8636 surname: Salucci fullname: Salucci, Marco email: ELEDIA@UniTN-University organization: ELEDIA Research Center (ELEDIA@UniTN-University of Trento), DICAM-Department of Civil, Environmental, and Mechanical Engineering, Via Mesiano 77, 38123 Trento, Italy |
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| Cites_doi | 10.3390/electronics10010095 10.1109/JERM.2020.2995329 10.1080/09205071.2017.1402713 10.1002/mop.30821 10.1007/s11517-016-1578-6 10.1017/S1759078720000835 10.1136/practneurol-2020-002763 10.1109/TBME.2014.2330554 10.1109/MWSYM.2009.5165979 10.1155/2012/252093 10.1109/TAP.2022.3177556 10.3390/s19163482 10.3390/diagnostics13010023 10.1155/2019/4074862 10.1007/978-981-10-9038-7 10.1109/APS.2012.6348759 10.1002/mop.31639 10.1155/2019/5459391 10.1002/bem.22024 |
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| References | Salucci (ref_3) 2022; 70 Persson (ref_6) 2014; 61 Zhu (ref_7) 2021; 5 ref_14 ref_11 Salucci (ref_13) 2019; 61 ref_19 ref_18 ref_16 ref_15 McGurgan (ref_23) 2021; 21 ref_25 ref_24 ref_22 ref_21 ref_20 ref_26 ref_9 ref_8 Meaney (ref_1) 2012; 2012 Semenov (ref_2) 2017; 38 Massa (ref_4) 2018; 32 ref_5 Salucci (ref_12) 2017; 59 Tesarik (ref_17) 2020; 12 Candefjord (ref_10) 2017; 55 |
| References_xml | – ident: ref_15 doi: 10.3390/electronics10010095 – volume: 5 start-page: 46 year: 2021 ident: ref_7 article-title: Stroke Classification in Simulated Electromagnetic Imaging Using Graph Approaches publication-title: IEEE J. Electromagn. RF Microw. Med. Biol. doi: 10.1109/JERM.2020.2995329 – ident: ref_16 – volume: 32 start-page: 516 year: 2018 ident: ref_4 article-title: Learning-by-examples techniques as applied to electromagnetics publication-title: J. Electromagn. Waves Appl. doi: 10.1080/09205071.2017.1402713 – volume: 59 start-page: 2796 year: 2017 ident: ref_12 article-title: Real-time brain stroke detection through a learning-by-examples technique—An experimental assessment publication-title: Microw. Opt. Technol. Lett. doi: 10.1002/mop.30821 – volume: 55 start-page: 1177 year: 2017 ident: ref_10 article-title: Microwave technology for detecting traumatic intracranial bleedings: Tests on phantom of subdural hematoma and numerical simulations publication-title: Med. Biol. Eng. Comput. doi: 10.1007/s11517-016-1578-6 – volume: 12 start-page: 982 year: 2020 ident: ref_17 article-title: Dielectric sensitivity of different antennas types for microwave-based head imaging: Numerical study and experimental verification publication-title: Int. J. Microw. Wirel. Technol. doi: 10.1017/S1759078720000835 – ident: ref_14 – volume: 21 start-page: 128 year: 2021 ident: ref_23 article-title: Acute intracerebral haemorrhage: Diagnosis and management publication-title: Pract. Neurol. doi: 10.1136/practneurol-2020-002763 – volume: 61 start-page: 2806 year: 2014 ident: ref_6 article-title: Microwave-Based Stroke Diagnosis Making Global Prehospital Thrombolytic Treatment Possible publication-title: IEEE Trans. Biomed. Eng. doi: 10.1109/TBME.2014.2330554 – ident: ref_18 – ident: ref_21 – ident: ref_20 doi: 10.1109/MWSYM.2009.5165979 – ident: ref_8 – volume: 2012 start-page: 252093 year: 2012 ident: ref_1 article-title: Microwave Imaging and Emerging Applications publication-title: Int. J. Biomed. Imaging doi: 10.1155/2012/252093 – volume: 70 start-page: 6349 year: 2022 ident: ref_3 article-title: Artificial Intelligence: New Frontiers in Real-Time Inverse Scattering and Electromagnetic Imaging publication-title: IEEE Trans. Antennas Propag. doi: 10.1109/TAP.2022.3177556 – ident: ref_9 doi: 10.3390/s19163482 – ident: ref_24 doi: 10.3390/diagnostics13010023 – ident: ref_11 doi: 10.1155/2019/4074862 – ident: ref_25 doi: 10.1007/978-981-10-9038-7 – ident: ref_5 doi: 10.1109/APS.2012.6348759 – ident: ref_19 – ident: ref_22 – volume: 61 start-page: 808 year: 2019 ident: ref_13 article-title: Instantaneous brain stroke classification and localization from real scattering data publication-title: Microw. Opt. Technol. Lett. doi: 10.1002/mop.31639 – ident: ref_26 doi: 10.1155/2019/5459391 – volume: 38 start-page: 158 year: 2017 ident: ref_2 article-title: Dielectric properties of brain tissue at 1 GHz in acute ischemic stroke: Experimental study on swine publication-title: Bioelectromagnetics doi: 10.1002/bem.22024 |
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| SubjectTerms | Accuracy Algorithms Antenna arrays Antennas Antennas (Electronics) Brain brain stroke Classification Datasets Dielectric properties Discriminant analysis Frequency ranges Geometry Humans Hypotheses Laboratories Machine learning microwave devices Microwaves numerical model Numerical models Patients Simulation Stroke Stroke (Disease) Stroke - diagnosis Support Vector Machine Support vector machines SVM Tissues |
| Title | On the Role of Training Data for SVM-Based Microwave Brain Stroke Detection and Classification |
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