Stacked autoencoders as new models for an accurate Alzheimer’s disease classification support using resting-state EEG and MRI measurements |
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| Institution: | 1. Department of Neurology I.C., Oasi Research Institute - IRCCS, Troina, Italy;2. Department of Physiology and Pharmacology “V. Erspamer”, Sapienza University of Rome, Rome, Italy;3. Hospital San Raffaele Cassino, Cassino (FR), Italy;4. Department of Pharmaceutical Sciences and Health Products, University of Camerino, Camerino, Italy;5. Department of Clinical and Experimental Medicine, University of Foggia, Foggia, Italy;6. IRCCS SDN, Napoli, Italy;7. Department of Motor Sciences and Healthiness, University of Naples Parthenope, Naples, Italy;8. Department of Neuroscience, Mental Health and Sensory Organs, Sapienza University of Rome, Rome, Italy;9. IRCCS San Raffaele Pisana, Rome, Italy;10. LENITEM (Laboratory of Epidemiology, Neuroimaging and Telemedicine), IRCCS Centro “S. Giovanni di Dio-F.B.F.”, Brescia, Italy;11. Memory Clinic and LANVIE - Laboratory of Neuroimaging of Aging, University Hospitals and University of Geneva, Geneva, Switzerland;12. Clinica Neurologica, IRCCS Ospedale Policlinico San Martino, Genova, Italy;13. Dipartimento di Neuroscienze, Oftalmologia, Genetica, Riabilitazione e Scienze Materno-infantili (DiNOGMI), Università di Genova, Italy;14. Dipartimento di Ingegneria, Università degli Studi di Messina, Messina, Italy;15. Dipartimento di Ingegneria Elettrica, Elettronica e Informatica, University of Catania, Catania, Italy |
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| Abstract: | ObjectiveThis retrospective and exploratory study tested the accuracy of artificial neural networks (ANNs) at detecting Alzheimer’s disease patients with dementia (ADD) based on input variables extracted from resting-state electroencephalogram (rsEEG), structural magnetic resonance imaging (sMRI) or both.MethodsFor the classification exercise, the ANNs had two architectures that included stacked (autoencoding) hidden layers recreating input data in the output. The classification was based on LORETA source estimates from rsEEG activity recorded with 10–20 montage system (19 electrodes) and standard sMRI variables in 89 ADD and 45 healthy control participants taken from a national database.ResultsThe ANN with stacked autoencoders and a deep leaning model representing both ADD and control participants showed classification accuracies in discriminating them of 80%, 85%, and 89% using rsEEG, sMRI, and rsEEG + sMRI features, respectively. The two ANNs with stacked autoencoders and a deep leaning model specialized for either ADD or control participants showed classification accuracies of 77%, 83%, and 86% using the same input features.ConclusionsThe two architectures of ANNs using stacked (autoencoding) hidden layers consistently reached moderate to high accuracy in the discrimination between ADD and healthy control participants as a function of the rsEEG and sMRI features employed.SignificanceThe present results encourage future multi-centric, prospective and longitudinal cross-validation studies using high resolution EEG techniques and harmonized clinical procedures towards clinical applications of the present ANNs. |
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| Keywords: | Alzheimer’s Disease (AD) Resting State Electroencephalography (rsEEG) Low-resolution brain electromagnetic tomography (LORETA) Stacked Artificial Neural Networks (ANNs) with Autoencoders |
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