Remote Epilepsy Monitoring: Signal Quality and Reliability Comparative Analysis
Epilepsy, a neurological condition affecting approximately 50 million individuals globally, is among the most common nervous system disorders. Electroencephalography (EEG) is vital for evaluating epilepsy, yet its intricate nature often restricts its application to specialized clinical environments....
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2025-01-01
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| author | Theeban Raj Shivaraja Rabani Remli Wan Asyraf Wan Zaidi Kalaivani Chellappan |
| author_facet | Theeban Raj Shivaraja Rabani Remli Wan Asyraf Wan Zaidi Kalaivani Chellappan |
| author_sort | Theeban Raj Shivaraja |
| collection | DOAJ |
| description | Epilepsy, a neurological condition affecting approximately 50 million individuals globally, is among the most common nervous system disorders. Electroencephalography (EEG) is vital for evaluating epilepsy, yet its intricate nature often restricts its application to specialized clinical environments. OptiEEG, a remote monitoring system incorporating OpenBCI’s EEG technology, addresses challenges by integrating a communication gateway and a mobile application for user-friendly operation. This study benchmarks OptiEEG’s performance against the clinically validated Natus NicoletOne EEG System through three routine EEG tests: Eye Open Close, Hyperventilation, and Photic Stimulation. Signal quality, component analysis, and reliability were evaluated using error metrics, time-frequency analysis, Bland-Altman plots, repeatability, Pearson Correlation Coefficient (PCC) and also EEG characteristics analysis of individual channels. OptiEEG demonstrated comparable signal quality to Natus, with average standard deviations for signal-to-noise ratio (Natus: 3.27 vs. OptiEEG: 2.95), peak signal-to-noise ratio (Natus: 2.76 vs. OptiEEG: 2.16), and mean squared error (Natus: 0.01 vs. OptiEEG: 0.04). Time-frequency analysis revealed less than 10% differences across alpha, theta, and delta bands. Reliability tests confirmed repeatability, with intra-system differences lower than inter-system differences, and Bland-Altman plots meeting 83% agreement criteria. PCC analysis highlighted moderate signal alignment, confirming similar EEG patterns across systems. Channel-specific analysis showed median differences as low as 0.80%, validating OptiEEG’s ability to capture critical EEG features. The results establish OptiEEG as a reliable alternative to traditional systems, combining clinically comparable performance with a portable design. These findings highlight its potential as a robust remote monitoring tool for epilepsy, enabling broader access to EEG diagnostics and management. |
| format | Article |
| id | doaj-art-96007711d0b24471a038b9bd9d6c4963 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-96007711d0b24471a038b9bd9d6c49632025-01-31T23:04:40ZengIEEEIEEE Access2169-35362025-01-0113198451986110.1109/ACCESS.2025.353113010844263Remote Epilepsy Monitoring: Signal Quality and Reliability Comparative AnalysisTheeban Raj Shivaraja0https://orcid.org/0000-0003-3656-8960Rabani Remli1Wan Asyraf Wan Zaidi2Kalaivani Chellappan3https://orcid.org/0000-0002-2618-216XDepartment of Electrical, Electronics and System Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi, MalaysiaDepartment of Medicine, Faculty of Medicine, Universiti Kebangsaan Malaysia, Cheras, Kuala Lumpur, MalaysiaDepartment of Medicine, Faculty of Medicine, Universiti Kebangsaan Malaysia, Cheras, Kuala Lumpur, MalaysiaDepartment of Electrical, Electronics and System Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi, MalaysiaEpilepsy, a neurological condition affecting approximately 50 million individuals globally, is among the most common nervous system disorders. Electroencephalography (EEG) is vital for evaluating epilepsy, yet its intricate nature often restricts its application to specialized clinical environments. OptiEEG, a remote monitoring system incorporating OpenBCI’s EEG technology, addresses challenges by integrating a communication gateway and a mobile application for user-friendly operation. This study benchmarks OptiEEG’s performance against the clinically validated Natus NicoletOne EEG System through three routine EEG tests: Eye Open Close, Hyperventilation, and Photic Stimulation. Signal quality, component analysis, and reliability were evaluated using error metrics, time-frequency analysis, Bland-Altman plots, repeatability, Pearson Correlation Coefficient (PCC) and also EEG characteristics analysis of individual channels. OptiEEG demonstrated comparable signal quality to Natus, with average standard deviations for signal-to-noise ratio (Natus: 3.27 vs. OptiEEG: 2.95), peak signal-to-noise ratio (Natus: 2.76 vs. OptiEEG: 2.16), and mean squared error (Natus: 0.01 vs. OptiEEG: 0.04). Time-frequency analysis revealed less than 10% differences across alpha, theta, and delta bands. Reliability tests confirmed repeatability, with intra-system differences lower than inter-system differences, and Bland-Altman plots meeting 83% agreement criteria. PCC analysis highlighted moderate signal alignment, confirming similar EEG patterns across systems. Channel-specific analysis showed median differences as low as 0.80%, validating OptiEEG’s ability to capture critical EEG features. The results establish OptiEEG as a reliable alternative to traditional systems, combining clinically comparable performance with a portable design. These findings highlight its potential as a robust remote monitoring tool for epilepsy, enabling broader access to EEG diagnostics and management.https://ieeexplore.ieee.org/document/10844263/Electroencephalographyepilepsyremote monitoringsignal analysiswearable technology |
| spellingShingle | Theeban Raj Shivaraja Rabani Remli Wan Asyraf Wan Zaidi Kalaivani Chellappan Remote Epilepsy Monitoring: Signal Quality and Reliability Comparative Analysis IEEE Access Electroencephalography epilepsy remote monitoring signal analysis wearable technology |
| title | Remote Epilepsy Monitoring: Signal Quality and Reliability Comparative Analysis |
| title_full | Remote Epilepsy Monitoring: Signal Quality and Reliability Comparative Analysis |
| title_fullStr | Remote Epilepsy Monitoring: Signal Quality and Reliability Comparative Analysis |
| title_full_unstemmed | Remote Epilepsy Monitoring: Signal Quality and Reliability Comparative Analysis |
| title_short | Remote Epilepsy Monitoring: Signal Quality and Reliability Comparative Analysis |
| title_sort | remote epilepsy monitoring signal quality and reliability comparative analysis |
| topic | Electroencephalography epilepsy remote monitoring signal analysis wearable technology |
| url | https://ieeexplore.ieee.org/document/10844263/ |
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