EEG-based emergency calling system for neurological disorder patient
Neurological disorders are diseases of the central and peripheral nervous systems, which include the brain, spinal cord, and nerves. Patients with this kind of disease might face difficulty in communicating with others, movement, sensation, hearing, and thinking. Insufficient monitoring of the healt...
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| Main Authors: | , , , , , |
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| Format: | Conference or Workshop Item |
| Language: | en |
| Published: |
IEEE Xplore
2025
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| Subjects: | |
| Online Access: | https://umpir.ump.edu.my/id/eprint/46496/1/MUCET2025_ICoCET2025_EEG-Based_Emergency_Calling_System_for_Neurological_Disorder_Patient.pdf https://umpir.ump.edu.my/id/eprint/46496/ |
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| Summary: | Neurological disorders are diseases of the central and peripheral nervous systems, which include the brain, spinal cord, and nerves. Patients with this kind of disease might face difficulty in communicating with others, movement, sensation, hearing, and thinking. Insufficient monitoring of the health status of neurological disorders patients can delay emergency responses, putting their lives in danger. Thus, the main objective of the study is to construct an assistive emergency calling system that is capable of analyzing the brain signals of neurological disorder patients (Epilepsy & Amyotrophic Lateral Sclerosis) and determining their health status. The study involved the analysis of the public datasets and data from experimental works that are related to neurological disorder. The experimental works have involved several brain stimulation exercises such as color blind exercise, hand gesture exercise and blindfolded exercise to mimic the neurological disorder where the measurement is done using Electroencephalogram (EEG) device. The MATLAB Graphical User Interface (GUI) algorithm is constructed to display the EEG data analysis and health status of the Neurological disorder patients. The mean, standard deviation, and variance of the analysis of the Power Spectral Density (PSD) is feed to k -NN classifier to determine the classification accuracy with the testing and training ratios of 50:50, 70:30, and 80:20 respectively. The classification accuracy is achieved at 87.3% using Mean PSD of blind folded activity where the classification ratio is at 80:20. Meanwhile, the emergency calling system is created using Arduino microcontroller and GSM module where the communication protocol between MATLAB GUI and GSM module is configured to establish the communication with local community practitioner. |
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