A Deep Learning Model for Stroke Patients' Motor Function Prediction

Abeer Abdulaziz AlArfaj; Hanan A Hosni Mahmoud; Alaaeldin M Hafez

doi:10.1155/2022/8645165

A Deep Learning Model for Stroke Patients' Motor Function Prediction

Appl Bionics Biomech. 2022 Aug 5:2022:8645165. doi: 10.1155/2022/8645165. eCollection 2022.

Authors

Abeer Abdulaziz AlArfaj¹, Hanan A Hosni Mahmoud¹, Alaaeldin M Hafez²

Affiliations

¹ Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.
² Department of Information Systems, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia.

Abstract

Deep learning models are effectively employed to transfer learning to adopt learning from other areas. This research utilizes several neural structures to interpret the electroencephalogram images (EEG) of brain-injured cases to plan operative imagery-computerized interface models for controlling left and right hand movements. This research proposed a model parameter tuning with less training time using transfer learning techniques. The precision of the proposed model is assessed by the aptitudes of motor imagery detection. The experiments depict that the best performance is attained with the incorporation of the proposed EEG-DenseNet and the transfer model. The prediction accuracy of the model reached 96.5% with reduced time computational cost. These high performance proves that the EEG-DenseNet model has high prospective for motor imagery brain-injured therapy systems. It also productively exhibited the effectiveness of transfer learning techniques for enhancing the accuracy of electroencephalogram brain-injured therapy models.