Classification of Alzheimer’s Disease from EEG Signal Using Robust-PCA Feature Extraction
The encephalographic (EEG) signal is an electrical signal that measures the brain activity. Due to its noninvasive acquisition process, it is often used to investigate the presence of Alzheimer’s disease (AD) or other common forms of neurodegerative disorders due to brain changes, that occur most fr...
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| Published in | Procedia computer science Vol. 192; pp. 3114 - 3122 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
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Elsevier B.V
2021
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1877-0509 1877-0509 |
| DOI | 10.1016/j.procs.2021.09.084 |
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| Abstract | The encephalographic (EEG) signal is an electrical signal that measures the brain activity. Due to its noninvasive acquisition process, it is often used to investigate the presence of Alzheimer’s disease (AD) or other common forms of neurodegerative disorders due to brain changes, that occur most frequently in older adults. Early detection of prodromal stages of AD, in which an individual has mild but measurable cognitive deficiencies with no significant effect on the functional activity of daily living, may help to reduce mortality and morbidity. This paper proposes an investigation of the classification of AD from EEG signal using robust-principal component analysis (R-PCA) feature extraction algorithm. Four widely used machine learning algorithms such as k-nearest neighbor (kNN), decision tree (DT), support vector machine (SVM), and naive Bayes have been implemented and compared by using a custom dataset composed of 13 subjects healthy or affected by AD in order to asses their classification performance. |
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| AbstractList | The encephalographic (EEG) signal is an electrical signal that measures the brain activity. Due to its noninvasive acquisition process, it is often used to investigate the presence of Alzheimer’s disease (AD) or other common forms of neurodegerative disorders due to brain changes, that occur most frequently in older adults. Early detection of prodromal stages of AD, in which an individual has mild but measurable cognitive deficiencies with no significant effect on the functional activity of daily living, may help to reduce mortality and morbidity. This paper proposes an investigation of the classification of AD from EEG signal using robust-principal component analysis (R-PCA) feature extraction algorithm. Four widely used machine learning algorithms such as k-nearest neighbor (kNN), decision tree (DT), support vector machine (SVM), and naive Bayes have been implemented and compared by using a custom dataset composed of 13 subjects healthy or affected by AD in order to asses their classification performance. |
| Author | Falaschetti, Laura Biagetti, Giorgio Luzzi, Simona Turchetti, Claudio Crippa, Paolo |
| Author_xml | – sequence: 1 givenname: Giorgio surname: Biagetti fullname: Biagetti, Giorgio organization: DII - Department of Information Engineering, Università Politecnica delle Marche, via Brecce Bianche, 12, I-60131 Ancona, Italy – sequence: 2 givenname: Paolo surname: Crippa fullname: Crippa, Paolo organization: DII - Department of Information Engineering, Università Politecnica delle Marche, via Brecce Bianche, 12, I-60131 Ancona, Italy – sequence: 3 givenname: Laura surname: Falaschetti fullname: Falaschetti, Laura email: l.falaschetti@univpm.it organization: DII - Department of Information Engineering, Università Politecnica delle Marche, via Brecce Bianche, 12, I-60131 Ancona, Italy – sequence: 4 givenname: Simona surname: Luzzi fullname: Luzzi, Simona organization: Clinica Neurologica - Dipartimento di Medicina Sperimentale e Clinica, Università Politecnica delle Marche, via Conca 71, I-60020 Ancona, Italy – sequence: 5 givenname: Claudio surname: Turchetti fullname: Turchetti, Claudio organization: DII - Department of Information Engineering, Università Politecnica delle Marche, via Brecce Bianche, 12, I-60131 Ancona, Italy |
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| Cites_doi | 10.1371/journal.pone.0193607 10.1007/BF00116251 10.1109/ACCESS.2020.3031447 10.1109/ACCESS.2019.2927121 10.1002/gps.3946 10.1007/BF00994018 10.1186/2046-4053-2-17 10.1109/CIDM.2014.7008655 10.1093/bioinformatics/bth158 10.1016/j.jalz.2011.03.005 10.1109/ACCESS.2018.2876135 10.1109/TIT.2012.2212415 10.1016/j.jalz.2018.02.001 10.1098/rsta.2015.0202 10.1016/0013-4694(92)90009-7 10.1016/j.eswa.2010.06.065 10.1023/A:1007413511361 |
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| Keywords | robust-PCA Alzheimer’s disease classification EEG Machine learning |
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| Title | Classification of Alzheimer’s Disease from EEG Signal Using Robust-PCA Feature Extraction |
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