Differentiation of Saccadic Eye Movement Signals
Saccadic electrooculograms are discrete biosignals that contain the instantaneous angular position of the human eyes as a response to saccadic visual stimuli. These signals are essential to monitor and evaluate several neurological diseases, such as Spinocerebellar Ataxia type 2 (SCA2). For this, bi...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-07-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/15/5021 |
| _version_ | 1827686390036430848 |
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| author | Roberto A. Becerra-García Rodolfo García-Bermúdez Gonzalo Joya |
| author_facet | Roberto A. Becerra-García Rodolfo García-Bermúdez Gonzalo Joya |
| author_sort | Roberto A. Becerra-García |
| collection | DOAJ |
| description | Saccadic electrooculograms are discrete biosignals that contain the instantaneous angular position of the human eyes as a response to saccadic visual stimuli. These signals are essential to monitor and evaluate several neurological diseases, such as Spinocerebellar Ataxia type 2 (SCA2). For this, biomarkers such as peak velocity, latency and duration are computed. To compute these biomarkers, we need to obtain the velocity profile of the signals using numerical differentiation methods. These methods are affected by the noise present in the electrooculograms, specially in subjects that suffer neurological diseases. This noise complicates the comparison of the differentiation methods using real saccadic signals because of the impossibility of establishing exact saccadic onset and offset points. In this work, we evaluate 16 differentiation methods by the design of an experiment that uses synthetic saccadic electrooculograms generated from parametric models of both healthy subjects and subjects suffering from Spinocerebellar Ataxia type 2 (SCA2). For these synthetic electrooculograms the exact velocity profile is known, hence we can use them as a reference for comparison and error computing for the tasks of saccade identification and saccade biomarker computing. Finally, we identify the best fitting method or methods for each evaluated task. |
| first_indexed | 2024-03-10T09:09:32Z |
| format | Article |
| id | doaj.art-cf7b50fa9d3145ccb9cd2c93e2a401cb |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T09:09:32Z |
| publishDate | 2021-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-cf7b50fa9d3145ccb9cd2c93e2a401cb2023-11-22T06:09:21ZengMDPI AGSensors1424-82202021-07-012115502110.3390/s21155021Differentiation of Saccadic Eye Movement SignalsRoberto A. Becerra-García0Rodolfo García-Bermúdez1Gonzalo Joya2Departamento de Tecnología Electrónica, Universidad de Málaga, CEI Andalucía Tech, 29071 Málaga, SpainDepartamento de Informática y Electrónica, Universidad Técnica de Manabí, Portoviejo 130105, EcuadorDepartamento de Tecnología Electrónica, Universidad de Málaga, CEI Andalucía Tech, 29071 Málaga, SpainSaccadic electrooculograms are discrete biosignals that contain the instantaneous angular position of the human eyes as a response to saccadic visual stimuli. These signals are essential to monitor and evaluate several neurological diseases, such as Spinocerebellar Ataxia type 2 (SCA2). For this, biomarkers such as peak velocity, latency and duration are computed. To compute these biomarkers, we need to obtain the velocity profile of the signals using numerical differentiation methods. These methods are affected by the noise present in the electrooculograms, specially in subjects that suffer neurological diseases. This noise complicates the comparison of the differentiation methods using real saccadic signals because of the impossibility of establishing exact saccadic onset and offset points. In this work, we evaluate 16 differentiation methods by the design of an experiment that uses synthetic saccadic electrooculograms generated from parametric models of both healthy subjects and subjects suffering from Spinocerebellar Ataxia type 2 (SCA2). For these synthetic electrooculograms the exact velocity profile is known, hence we can use them as a reference for comparison and error computing for the tasks of saccade identification and saccade biomarker computing. Finally, we identify the best fitting method or methods for each evaluated task.https://www.mdpi.com/1424-8220/21/15/5021numerical differentiationelectrooculogramssaccades identificationsaccades biomarkers computing |
| spellingShingle | Roberto A. Becerra-García Rodolfo García-Bermúdez Gonzalo Joya Differentiation of Saccadic Eye Movement Signals Sensors numerical differentiation electrooculograms saccades identification saccades biomarkers computing |
| title | Differentiation of Saccadic Eye Movement Signals |
| title_full | Differentiation of Saccadic Eye Movement Signals |
| title_fullStr | Differentiation of Saccadic Eye Movement Signals |
| title_full_unstemmed | Differentiation of Saccadic Eye Movement Signals |
| title_short | Differentiation of Saccadic Eye Movement Signals |
| title_sort | differentiation of saccadic eye movement signals |
| topic | numerical differentiation electrooculograms saccades identification saccades biomarkers computing |
| url | https://www.mdpi.com/1424-8220/21/15/5021 |
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