Predicting Mini-Mental State Examination Scores Using Electroencephalography Signal Features
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Abstract
Purpose: The purpose of this study is to use linear and non-linear features extracted from Electroencephalography (EEG) signal to predict the Mini-Mental State Examination (MMSE) test score by machine learning algorithms.
Materials and Methods: First, the MMSE test was taken from 20 subjects that were referred with the initial diagnosis of dementia. Then, the brain activity of subjects was recorded via EEG signal. After preprocessing this signal, various linear and non-linear features are extracted from it that are used as input to machine learning algorithms to predict MMSE test scores in three levels.
Results: Based on the experiments, the best classification result is related to the Long Short-Term Memory (LSTM) network with 68% accuracy.
Conclusion: Findings show that by using machine learning algorithms and features extracted from EEG signal the MMSE scores are predicted in three levels. Although deep neural networks require a lot of data for training, the LSTM network has been able to achieve the best performance. By increasing the number of subjects, it is expected that the classification results will also increase.
1- Min Ju Kang et al., "Prediction of cognitive impairment via deep learning trained with multi-center neuropsychological test data." BMC medical informatics and decision making, Vol. 19 (No. 1), pp. 1-9, (2019).
2- Dieu Ni Thi Doan et al., "Predicting Dementia With Prefrontal Electroencephalography and Event-Related Potential." Frontiers in Aging Neuroscience, Vol. 13p. 180, (2021).
3- World Health Organization, Global tuberculosis report 2013. World Health Organization, (2013).
4- Ronald C Petersen, James C Stevens, Mary Ganguli, Eric G Tangalos, Jeffrey L Cummings, and Steven T DeKosky, "Practice parameter: early detection of dementia: mild cognitive impairment (an evidence-based review): report of the Quality Standards Subcommittee of the American Academy of Neurology." Neurology, Vol. 56 (No. 9), pp. 1133-42, (2001).
5- Shahla Farzana and Natalie Parde, "Exploring MMSE Score Prediction Using Verbal and Non-Verbal Cues." in INTERSPEECH, (2020), pp. 2207-11.
6- Eduardo Perez-Valero, Miguel A Lopez-Gordo, Christian Morillas, Francisco Pelayo, and Miguel A Vaquero-Blasco, "A Review of Automated Techniques for Assisting the Early Detection of Alzheimer’s Disease with a Focus on EEG." Journal of Alzheimer's Disease, (No. Preprint), pp. 1-14, (2021).
7- Noor Kamal Al-Qazzaz, Sawal Hamid Bin Ali, Siti Anom Ahmad, Kalaivani Chellappan, Md Islam, and Javier Escudero, "Role of EEG as biomarker in the early detection and classification of dementia." The Scientific World Journal, Vol. 2014(2014).
8- Saraswati Sridhar and Vidya Manian, "EEG and Deep Learning Based Brain Cognitive Function Classification." Computers, Vol. 9 (No. 4), p. 104, (2020).
9- G Anuradha and D Najumnissa Jamal, "Classification of Dementia in EEG with a Two-Layered Feed Forward Artificial Neural Network." Engineering, Technology & Applied Science Research, Vol. 11 (No. 3), pp. 7135-39, (2021).
10- Raymundo Cassani, Mar Estarellas, Rodrigo San-Martin, Francisco J Fraga, and Tiago H Falk, "Systematic review on resting-state EEG for Alzheimer’s disease diagnosis and progression assessment." Disease markers, Vol. 2018(2018).
11- Su Yang, Jose Miguel Sanchez Bornot, Kongfatt Wong-Lin, and Girijesh Prasad, "M/EEG-based bio-markers to predict the MCI and Alzheimer's disease: a review from the ML perspective." IEEE Transactions on Biomedical Engineering, Vol. 66 (No. 10), pp. 2924-35, (2019).
12- Lucas R Trambaiolli, Ana C Lorena, Francisco J Fraga, Paulo AM Kanda, Renato Anghinah, and Ricardo Nitrini, "Improving Alzheimer's disease diagnosis with machine learning techniques." Clinical EEG and neuroscience, Vol. 42 (No. 3), pp. 160-65, (2011).
13- Ruofan Wang, Jiang Wang, Haitao Yu, Xile Wei, Chen Yang, and Bin Deng, "Power spectral density and coherence analysis of Alzheimer’s EEG." Cognitive neurodynamics, Vol. 9 (No. 3), pp. 291-304, (2015).
14- Raymundo Cassani, Tiago H Falk, Francisco J Fraga, Marco Cecchi, Dennis K Moore, and Renato Anghinah, "Towards automated electroencephalography-based Alzheimer’s disease diagnosis using portable low-density devices." Biomedical Signal Processing and Control, Vol. 33pp. 261-71, (2017).
15- LR Trambaiolli, N Spolaôr, AC Lorena, R Anghinah, and JR Sato, "Feature selection before EEG classification supports the diagnosis of Alzheimer’s disease." Clinical Neurophysiology, Vol. 128 (No. 10), pp. 2058-67, (2017).
16- Haleh Aghajani, Edmond Zahedi, Mahdi Jalili, Adib Keikhosravi, and Bijan Vosoughi Vahdat, "Diagnosis of early Alzheimer's disease based on EEG source localization and a standardized realistic head model." IEEE journal of biomedical and health informatics, Vol. 17 (No. 6), pp. 1039-45, (2013).
17- NN Kulkarni and VK Bairagi, "Extracting salient features for EEG-based diagnosis of Alzheimer's disease using support vector machine classifier." IETE Journal of Research, Vol. 63 (No. 1), pp. 11-22, (2017).
18- Giulia Fiscon et al., "Combining EEG signal processing with supervised methods for Alzheimer’s patients classification." BMC medical informatics and decision making, Vol. 18 (No. 1), pp. 1-10, (2018).
19- Pholpat Durongbhan et al., "A dementia classification framework using frequency and time-frequency features based on EEG signals." IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 27 (No. 5), pp. 826-35, (2019).
20- Cosimo Ieracitano, Nadia Mammone, Amir Hussain, and Francesco C Morabito, "A novel multi-modal machine learning based approach for automatic classification of EEG recordings in dementia." Neural Networks, Vol. 123pp. 176-90, (2020).
21- Giulia Fiscon et al., "Alzheimer's disease patients classification through EEG signals processing." in 2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM), (2014): IEEE, pp. 105-12.
22- Joseph C McBride et al., "Spectral and complexity analysis of scalp EEG characteristics for mild cognitive impairment and early Alzheimer's disease." Computer methods and programs in biomedicine, Vol. 114 (No. 2), pp. 153-63, (2014).
23- Francesco Carlo Morabito et al., "Deep convolutional neural networks for classification of mild cognitive impaired and Alzheimer's disease patients from scalp EEG recordings." in 2016 IEEE 2nd International Forum on Research and Technologies for Society and Industry Leveraging a better tomorrow (RTSI), (2016): IEEE, pp. 1-6.
24- Saúl J Ruiz-Gómez et al., "Automated multiclass classification of spontaneous EEG activity in Alzheimer’s disease and mild cognitive impairment." Entropy, Vol. 20 (No. 1), p. 35, (2018).
25- Ma Jian, "Predicting MMSE Score from Finger-Tapping Measurement." bioRxiv, p. 817338, (2019).
26- Jungmi Choi et al., "Resting-state prefrontal EEG biomarkers in correlation with MMSE scores in elderly individuals." Scientific reports, Vol. 9 (No. 1), pp. 1-15, (2019).
27- Marshal F Folstein, Susan E Folstein, and Paul R McHugh, "“Mini-mental state”: a practical method for grading the cognitive state of patients for the clinician." Journal of psychiatric research, Vol. 12 (No. 3), pp. 189-98, (1975).
28- Tzyy-Ping Jung et al., "Removing electroencephalographic artifacts by blind source separation." Psychophysiology, Vol. 37 (No. 2), pp. 163-78, (2000).
29- Hugh Nolan, Robert Whelan, and Richard B Reilly, "FASTER: fully automated statistical thresholding for EEG artifact rejection." Journal of neuroscience methods, Vol. 192 (No. 1), pp. 152-62, (2010).
30- Katerina D Tzimourta et al., "EEG window length evaluation for the detection of Alzheimer’s disease over different brain regions." Brain sciences, Vol. 9 (No. 4), p. 81, (2019).
31- Ramya Priya Mudhiganti, "A Comparative Analysis of Feature Extraction Techniques for EEG Signals from Alzheimer patients." (2012).
32- M Bedeeuzzaman, Omar Farooq, and Yusuf Uzzaman Khan, "Automatic seizure detection using inter quartile range." International Journal of Computer Applications, Vol. 44 (No. 11), pp. 1-5, (2012).
33- Markos G Tsipouras, "Spectral information of EEG signals with respect to epilepsy classification." EURASIP Journal on Advances in Signal Processing, Vol. 2019 (No. 1), pp. 1-17, (2019).
34- Poomipat Boonyakitanont, Apiwat Lek-Uthai, Krisnachai Chomtho, and Jitkomut Songsiri, "A review of feature extraction and performance evaluation in epileptic seizure detection using EEG." Biomedical Signal Processing and Control, Vol. 57p. 101702, (2020).
35- Jaeseung Jeong, "EEG dynamics in patients with Alzheimer's disease." Clinical Neurophysiology, Vol. 115 (No. 7), pp. 1490-505, (2004).
36- William S Noble, "What is a support vector machine?" Nature biotechnology, Vol. 24 (No. 12), pp. 1565-67, (2006).
37- Frank Rosenblatt, "The perceptron: a probabilistic model for information storage and organization in the brain." Psychological review, Vol. 65 (No. 6), p. 386, (1958).
38- Sepp Hochreiter, "JA1 4 rgen Schmidhuber (1997).“Long Short-Term Memory”." Neural Computation, Vol. 9 (No. 8).
39- Rikiya Yamashita, Mizuho Nishio, Richard Kinh Gian Do, and Kaori Togashi, "Convolutional neural networks: an overview and application in radiology." Insights into imaging, Vol. 9 (No. 4), pp. 611-29, (2018).
40- David W Hosmer Jr, Stanley Lemeshow, and Rodney X Sturdivant, Applied logistic regression. John Wiley & Sons, (2013).
41- Betty M Tijms et al., "Alzheimer's disease: connecting findings from graph theoretical studies of brain networks." Neurobiology of aging, Vol. 34 (No. 8), pp. 2023-36, (2013).
2- Dieu Ni Thi Doan et al., "Predicting Dementia With Prefrontal Electroencephalography and Event-Related Potential." Frontiers in Aging Neuroscience, Vol. 13p. 180, (2021).
3- World Health Organization, Global tuberculosis report 2013. World Health Organization, (2013).
4- Ronald C Petersen, James C Stevens, Mary Ganguli, Eric G Tangalos, Jeffrey L Cummings, and Steven T DeKosky, "Practice parameter: early detection of dementia: mild cognitive impairment (an evidence-based review): report of the Quality Standards Subcommittee of the American Academy of Neurology." Neurology, Vol. 56 (No. 9), pp. 1133-42, (2001).
5- Shahla Farzana and Natalie Parde, "Exploring MMSE Score Prediction Using Verbal and Non-Verbal Cues." in INTERSPEECH, (2020), pp. 2207-11.
6- Eduardo Perez-Valero, Miguel A Lopez-Gordo, Christian Morillas, Francisco Pelayo, and Miguel A Vaquero-Blasco, "A Review of Automated Techniques for Assisting the Early Detection of Alzheimer’s Disease with a Focus on EEG." Journal of Alzheimer's Disease, (No. Preprint), pp. 1-14, (2021).
7- Noor Kamal Al-Qazzaz, Sawal Hamid Bin Ali, Siti Anom Ahmad, Kalaivani Chellappan, Md Islam, and Javier Escudero, "Role of EEG as biomarker in the early detection and classification of dementia." The Scientific World Journal, Vol. 2014(2014).
8- Saraswati Sridhar and Vidya Manian, "EEG and Deep Learning Based Brain Cognitive Function Classification." Computers, Vol. 9 (No. 4), p. 104, (2020).
9- G Anuradha and D Najumnissa Jamal, "Classification of Dementia in EEG with a Two-Layered Feed Forward Artificial Neural Network." Engineering, Technology & Applied Science Research, Vol. 11 (No. 3), pp. 7135-39, (2021).
10- Raymundo Cassani, Mar Estarellas, Rodrigo San-Martin, Francisco J Fraga, and Tiago H Falk, "Systematic review on resting-state EEG for Alzheimer’s disease diagnosis and progression assessment." Disease markers, Vol. 2018(2018).
11- Su Yang, Jose Miguel Sanchez Bornot, Kongfatt Wong-Lin, and Girijesh Prasad, "M/EEG-based bio-markers to predict the MCI and Alzheimer's disease: a review from the ML perspective." IEEE Transactions on Biomedical Engineering, Vol. 66 (No. 10), pp. 2924-35, (2019).
12- Lucas R Trambaiolli, Ana C Lorena, Francisco J Fraga, Paulo AM Kanda, Renato Anghinah, and Ricardo Nitrini, "Improving Alzheimer's disease diagnosis with machine learning techniques." Clinical EEG and neuroscience, Vol. 42 (No. 3), pp. 160-65, (2011).
13- Ruofan Wang, Jiang Wang, Haitao Yu, Xile Wei, Chen Yang, and Bin Deng, "Power spectral density and coherence analysis of Alzheimer’s EEG." Cognitive neurodynamics, Vol. 9 (No. 3), pp. 291-304, (2015).
14- Raymundo Cassani, Tiago H Falk, Francisco J Fraga, Marco Cecchi, Dennis K Moore, and Renato Anghinah, "Towards automated electroencephalography-based Alzheimer’s disease diagnosis using portable low-density devices." Biomedical Signal Processing and Control, Vol. 33pp. 261-71, (2017).
15- LR Trambaiolli, N Spolaôr, AC Lorena, R Anghinah, and JR Sato, "Feature selection before EEG classification supports the diagnosis of Alzheimer’s disease." Clinical Neurophysiology, Vol. 128 (No. 10), pp. 2058-67, (2017).
16- Haleh Aghajani, Edmond Zahedi, Mahdi Jalili, Adib Keikhosravi, and Bijan Vosoughi Vahdat, "Diagnosis of early Alzheimer's disease based on EEG source localization and a standardized realistic head model." IEEE journal of biomedical and health informatics, Vol. 17 (No. 6), pp. 1039-45, (2013).
17- NN Kulkarni and VK Bairagi, "Extracting salient features for EEG-based diagnosis of Alzheimer's disease using support vector machine classifier." IETE Journal of Research, Vol. 63 (No. 1), pp. 11-22, (2017).
18- Giulia Fiscon et al., "Combining EEG signal processing with supervised methods for Alzheimer’s patients classification." BMC medical informatics and decision making, Vol. 18 (No. 1), pp. 1-10, (2018).
19- Pholpat Durongbhan et al., "A dementia classification framework using frequency and time-frequency features based on EEG signals." IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 27 (No. 5), pp. 826-35, (2019).
20- Cosimo Ieracitano, Nadia Mammone, Amir Hussain, and Francesco C Morabito, "A novel multi-modal machine learning based approach for automatic classification of EEG recordings in dementia." Neural Networks, Vol. 123pp. 176-90, (2020).
21- Giulia Fiscon et al., "Alzheimer's disease patients classification through EEG signals processing." in 2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM), (2014): IEEE, pp. 105-12.
22- Joseph C McBride et al., "Spectral and complexity analysis of scalp EEG characteristics for mild cognitive impairment and early Alzheimer's disease." Computer methods and programs in biomedicine, Vol. 114 (No. 2), pp. 153-63, (2014).
23- Francesco Carlo Morabito et al., "Deep convolutional neural networks for classification of mild cognitive impaired and Alzheimer's disease patients from scalp EEG recordings." in 2016 IEEE 2nd International Forum on Research and Technologies for Society and Industry Leveraging a better tomorrow (RTSI), (2016): IEEE, pp. 1-6.
24- Saúl J Ruiz-Gómez et al., "Automated multiclass classification of spontaneous EEG activity in Alzheimer’s disease and mild cognitive impairment." Entropy, Vol. 20 (No. 1), p. 35, (2018).
25- Ma Jian, "Predicting MMSE Score from Finger-Tapping Measurement." bioRxiv, p. 817338, (2019).
26- Jungmi Choi et al., "Resting-state prefrontal EEG biomarkers in correlation with MMSE scores in elderly individuals." Scientific reports, Vol. 9 (No. 1), pp. 1-15, (2019).
27- Marshal F Folstein, Susan E Folstein, and Paul R McHugh, "“Mini-mental state”: a practical method for grading the cognitive state of patients for the clinician." Journal of psychiatric research, Vol. 12 (No. 3), pp. 189-98, (1975).
28- Tzyy-Ping Jung et al., "Removing electroencephalographic artifacts by blind source separation." Psychophysiology, Vol. 37 (No. 2), pp. 163-78, (2000).
29- Hugh Nolan, Robert Whelan, and Richard B Reilly, "FASTER: fully automated statistical thresholding for EEG artifact rejection." Journal of neuroscience methods, Vol. 192 (No. 1), pp. 152-62, (2010).
30- Katerina D Tzimourta et al., "EEG window length evaluation for the detection of Alzheimer’s disease over different brain regions." Brain sciences, Vol. 9 (No. 4), p. 81, (2019).
31- Ramya Priya Mudhiganti, "A Comparative Analysis of Feature Extraction Techniques for EEG Signals from Alzheimer patients." (2012).
32- M Bedeeuzzaman, Omar Farooq, and Yusuf Uzzaman Khan, "Automatic seizure detection using inter quartile range." International Journal of Computer Applications, Vol. 44 (No. 11), pp. 1-5, (2012).
33- Markos G Tsipouras, "Spectral information of EEG signals with respect to epilepsy classification." EURASIP Journal on Advances in Signal Processing, Vol. 2019 (No. 1), pp. 1-17, (2019).
34- Poomipat Boonyakitanont, Apiwat Lek-Uthai, Krisnachai Chomtho, and Jitkomut Songsiri, "A review of feature extraction and performance evaluation in epileptic seizure detection using EEG." Biomedical Signal Processing and Control, Vol. 57p. 101702, (2020).
35- Jaeseung Jeong, "EEG dynamics in patients with Alzheimer's disease." Clinical Neurophysiology, Vol. 115 (No. 7), pp. 1490-505, (2004).
36- William S Noble, "What is a support vector machine?" Nature biotechnology, Vol. 24 (No. 12), pp. 1565-67, (2006).
37- Frank Rosenblatt, "The perceptron: a probabilistic model for information storage and organization in the brain." Psychological review, Vol. 65 (No. 6), p. 386, (1958).
38- Sepp Hochreiter, "JA1 4 rgen Schmidhuber (1997).“Long Short-Term Memory”." Neural Computation, Vol. 9 (No. 8).
39- Rikiya Yamashita, Mizuho Nishio, Richard Kinh Gian Do, and Kaori Togashi, "Convolutional neural networks: an overview and application in radiology." Insights into imaging, Vol. 9 (No. 4), pp. 611-29, (2018).
40- David W Hosmer Jr, Stanley Lemeshow, and Rodney X Sturdivant, Applied logistic regression. John Wiley & Sons, (2013).
41- Betty M Tijms et al., "Alzheimer's disease: connecting findings from graph theoretical studies of brain networks." Neurobiology of aging, Vol. 34 (No. 8), pp. 2023-36, (2013).
| Files | ||
| Issue | Vol 9 No 4 (2022) | |
| Section | Original Article(s) | |
| DOI | https://doi.org/10.18502/fbt.v9i4.10385 | |
| Keywords | ||
| Mini-Mental State Examination Electroencephalography Signal Machine Learning Algorithms Electroencephalography Feature Extraction | ||
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How to Cite
1.
Asayesh V, Dehghani M, Torabi M, Akhtari S, Gharibzadeh S. Predicting Mini-Mental State Examination Scores Using Electroencephalography Signal Features. Frontiers Biomed Technol. 2022;9(4):274-282.
