Bayesian supervised machine learning classification of neural networks with pathological perturbations

Extraction of temporal features of neuronal activity from electrophysiological data can be used for accurate classification of neural networks in healthy and pathologically perturbed conditions. In this study, we provide an extensive approach for the classification of human neural networks with and...

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Bibliographic Details
Published in:Biomedical physics & engineering express 2021-11, Vol.7 (6), p.65021
Main Authors: Levi, Riccardo, Valderhaug, Vibeke Devold, Castelbuono, Salvatore, Sandvig, Axel, Sandvig, Ioanna, Barbieri, Riccardo
Format: Article
Language:English
Subjects:
Algorithms
Bayes Theorem
Machine Learning
multi electrode array
neural networks
Neural Networks, Computer
neurophysiology
point process
Supervised Machine Learning
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Summary:Extraction of temporal features of neuronal activity from electrophysiological data can be used for accurate classification of neural networks in healthy and pathologically perturbed conditions. In this study, we provide an extensive approach for the classification of human neural networks with and without an underlying pathology, from electrophysiological recordings obtained using a microelectrode array (MEA) platform. We developed a Dirichlet mixture (DM) Point Process statistical model able to extract temporal features related to neurons. We then applied a machine learning algorithm to discriminate between healthy control and pathologically perturbed neural networks. We found a high degree of separability between the classes using DM point process features (p-value
ISSN:2057-1976
2057-1976
DOI:10.1088/2057-1976/ac2935