Characterization and Classification of Electrophysiological Signals Represented as Visibility Graphs Using the Maxclique Graph
Detection, characterization and classification of patterns within time series from electrophysiological signals have been a challenge for neuroscientists due to their complexity and variability. Here, we aimed to use graph theory to characterize and classify waveforms within biological signals using...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2020-04-01
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| Series: | Frontiers in Bioengineering and Biotechnology |
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| Online Access: | https://www.frontiersin.org/article/10.3389/fbioe.2020.00324/full |
| _version_ | 1818327027922501632 |
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| author | Erika Elizabeth Rodriguez-Torres Ulises Paredes-Hernandez Enrique Vazquez-Mendoza Margarita Tetlalmatzi-Montiel Consuelo Morgado-Valle Luis Beltran-Parrazal Rafael Villarroel-Flores |
| author_facet | Erika Elizabeth Rodriguez-Torres Ulises Paredes-Hernandez Enrique Vazquez-Mendoza Margarita Tetlalmatzi-Montiel Consuelo Morgado-Valle Luis Beltran-Parrazal Rafael Villarroel-Flores |
| author_sort | Erika Elizabeth Rodriguez-Torres |
| collection | DOAJ |
| description | Detection, characterization and classification of patterns within time series from electrophysiological signals have been a challenge for neuroscientists due to their complexity and variability. Here, we aimed to use graph theory to characterize and classify waveforms within biological signals using maxcliques as a feature for a deep learning method. We implemented a compact and easy to visualize algorithm and interface in Python. This software uses time series as input. We applied the maxclique graph operator in order to obtain further graph parameters. We extracted features of the time series by processing all graph parameters through K-means, one of the simplest unsupervised machine learning algorithms. As proof of principle, we analyzed integrated electrical activity of XII nerve to identify waveforms. Our results show that the use of maxcliques allows identification of two distinct types of waveforms that match expert classification. We propose that our method can be a useful tool to characterize and classify other electrophysiological signals in a short time and objectively. Reducing the classification time improves efficiency for further analysis in order to compare between treatments or conditions, e.g., pharmacological trials, injuries, or neurodegenerative diseases. |
| first_indexed | 2024-12-13T12:09:45Z |
| format | Article |
| id | doaj.art-8afe4fe8621e4aba98595c9523e0c936 |
| institution | Directory Open Access Journal |
| issn | 2296-4185 |
| language | English |
| last_indexed | 2024-12-13T12:09:45Z |
| publishDate | 2020-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Bioengineering and Biotechnology |
| spelling | doaj.art-8afe4fe8621e4aba98595c9523e0c9362022-12-21T23:46:52ZengFrontiers Media S.A.Frontiers in Bioengineering and Biotechnology2296-41852020-04-01810.3389/fbioe.2020.00324527815Characterization and Classification of Electrophysiological Signals Represented as Visibility Graphs Using the Maxclique GraphErika Elizabeth Rodriguez-Torres0Ulises Paredes-Hernandez1Enrique Vazquez-Mendoza2Margarita Tetlalmatzi-Montiel3Consuelo Morgado-Valle4Luis Beltran-Parrazal5Rafael Villarroel-Flores6Área Académica de Matemáticas y Física, Universidad Autónoma del Estado de Hidalgo, Pachuca, MexicoÁrea Académica de Matemáticas y Física, Universidad Autónoma del Estado de Hidalgo, Pachuca, MexicoCentro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional, Mexico City, MexicoÁrea Académica de Matemáticas y Física, Universidad Autónoma del Estado de Hidalgo, Pachuca, MexicoCentro de Investigaciones Cerebrales, Dirección General de Investigaciones, Universidad Veracruzana, Xalapa, MexicoCentro de Investigaciones Cerebrales, Dirección General de Investigaciones, Universidad Veracruzana, Xalapa, MexicoÁrea Académica de Matemáticas y Física, Universidad Autónoma del Estado de Hidalgo, Pachuca, MexicoDetection, characterization and classification of patterns within time series from electrophysiological signals have been a challenge for neuroscientists due to their complexity and variability. Here, we aimed to use graph theory to characterize and classify waveforms within biological signals using maxcliques as a feature for a deep learning method. We implemented a compact and easy to visualize algorithm and interface in Python. This software uses time series as input. We applied the maxclique graph operator in order to obtain further graph parameters. We extracted features of the time series by processing all graph parameters through K-means, one of the simplest unsupervised machine learning algorithms. As proof of principle, we analyzed integrated electrical activity of XII nerve to identify waveforms. Our results show that the use of maxcliques allows identification of two distinct types of waveforms that match expert classification. We propose that our method can be a useful tool to characterize and classify other electrophysiological signals in a short time and objectively. Reducing the classification time improves efficiency for further analysis in order to compare between treatments or conditions, e.g., pharmacological trials, injuries, or neurodegenerative diseases.https://www.frontiersin.org/article/10.3389/fbioe.2020.00324/fullvisibility graphsgraph theorymaxcliqueselectrophysiological signalsdeep learningpre-Bötzinger complex |
| spellingShingle | Erika Elizabeth Rodriguez-Torres Ulises Paredes-Hernandez Enrique Vazquez-Mendoza Margarita Tetlalmatzi-Montiel Consuelo Morgado-Valle Luis Beltran-Parrazal Rafael Villarroel-Flores Characterization and Classification of Electrophysiological Signals Represented as Visibility Graphs Using the Maxclique Graph Frontiers in Bioengineering and Biotechnology visibility graphs graph theory maxcliques electrophysiological signals deep learning pre-Bötzinger complex |
| title | Characterization and Classification of Electrophysiological Signals Represented as Visibility Graphs Using the Maxclique Graph |
| title_full | Characterization and Classification of Electrophysiological Signals Represented as Visibility Graphs Using the Maxclique Graph |
| title_fullStr | Characterization and Classification of Electrophysiological Signals Represented as Visibility Graphs Using the Maxclique Graph |
| title_full_unstemmed | Characterization and Classification of Electrophysiological Signals Represented as Visibility Graphs Using the Maxclique Graph |
| title_short | Characterization and Classification of Electrophysiological Signals Represented as Visibility Graphs Using the Maxclique Graph |
| title_sort | characterization and classification of electrophysiological signals represented as visibility graphs using the maxclique graph |
| topic | visibility graphs graph theory maxcliques electrophysiological signals deep learning pre-Bötzinger complex |
| url | https://www.frontiersin.org/article/10.3389/fbioe.2020.00324/full |
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