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Gelbard-Sagiv H, Pardo S, Getter N, Guendelman M, Benninger F, Kraus D, Shriki O, Ben-Sasson S. Optimizing Electrode Configurations for Wearable EEG Seizure Detection Using Machine Learning. Sensors (Basel) 2023; 23:5805. [PMID: 37447653 DOI: 10.3390/s23135805] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 06/15/2023] [Accepted: 06/19/2023] [Indexed: 07/15/2023]
Abstract
Epilepsy, a prevalent neurological disorder, profoundly affects patients' quality of life due to the unpredictable nature of seizures. The development of a reliable and user-friendly wearable EEG system capable of detecting and predicting seizures has the potential to revolutionize epilepsy care. However, optimizing electrode configurations for such systems, which is crucial for balancing accuracy and practicality, remains to be explored. This study addresses this gap by developing a systematic approach to optimize electrode configurations for a seizure detection machine-learning algorithm. Our approach was applied to an extensive database of prolonged annotated EEG recordings from 158 epilepsy patients. Multiple electrode configurations ranging from one to eighteen were assessed to determine the optimal number of electrodes. Results indicated that the performance was initially maintained as the number of electrodes decreased, but a drop in performance was found to have occurred at around eight electrodes. Subsequently, a comprehensive analysis of all eight-electrode configurations was conducted using a computationally intensive workflow to identify the optimal configurations. This approach can inform the mechanical design process of an EEG system that balances seizure detection accuracy with the ease of use and portability. Additionally, this framework holds potential for optimizing hardware in other machine learning applications. The study presents a significant step towards the development of an efficient wearable EEG system for seizure detection.
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Affiliation(s)
| | - Snir Pardo
- NeuroHelp Ltd., Ramat-Gan 5252181, Israel
| | - Nir Getter
- NeuroHelp Ltd., Ramat-Gan 5252181, Israel
- Department of Cognitive and Brain Sciences, Ben-Gurion University of the Negev, Beer-Sheva 8410501, Israel
| | - Miriam Guendelman
- NeuroHelp Ltd., Ramat-Gan 5252181, Israel
- Department of Cognitive and Brain Sciences, Ben-Gurion University of the Negev, Beer-Sheva 8410501, Israel
| | - Felix Benninger
- Department of Neurology, Rabin Medical Center, Beilinson Hospital, Petach Tikva 4941492, Israel
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Dror Kraus
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
- Department of Pediatric Neurology, Schneider Children's Medical Center of Israel, Petach Tikva 4920235, Israel
| | - Oren Shriki
- NeuroHelp Ltd., Ramat-Gan 5252181, Israel
- Department of Cognitive and Brain Sciences, Ben-Gurion University of the Negev, Beer-Sheva 8410501, Israel
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Guendelman M, Reich L, Amedi A. ‘Visual’-parsing without visual experience: Is ‘seeing’ with sounds better than sight restoration in the early blind? Multisens Res 2013. [DOI: 10.1163/22134808-000s0147] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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Striem-Amit E, Guendelman M, Amedi A. 'Visual' acuity of the congenitally blind using visual-to-auditory sensory substitution. PLoS One 2012; 7:e33136. [PMID: 22438894 PMCID: PMC3306374 DOI: 10.1371/journal.pone.0033136] [Citation(s) in RCA: 90] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2011] [Accepted: 02/04/2012] [Indexed: 11/17/2022] Open
Abstract
Sensory Substitution Devices (SSDs) convey visual information through sounds or touch, thus theoretically enabling a form of visual rehabilitation in the blind. However, for clinical use, these devices must provide fine-detailed visual information which was not yet shown for this or other means of visual restoration. To test the possible functional acuity conveyed by such devices, we used the Snellen acuity test conveyed through a high-resolution visual-to-auditory SSD (The vOICe). We show that congenitally fully blind adults can exceed the World Health Organization (WHO) blindness acuity threshold using SSDs, reaching the highest acuity reported yet with any visual rehabilitation approach. This demonstrates the potential capacity of SSDs as inexpensive, non-invasive visual rehabilitation aids, alone or when supplementing visual prostheses.
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Affiliation(s)
- Ella Striem-Amit
- Department of Medical Neurobiology, Faculty of Medicine, The Institute for Medical Research Israel-Canada, The Hebrew University of Jerusalem, Jerusalem, Israel
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