Clinical Utility and Feasibility of Smartphone-based EEG in Kenya: A Multicenter Observational Study
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| Autores principales: | , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| author | Enkhtsetseg, Nomin Lehn-Schiøler, William Storgaard, Anton Mosquera Pedersen, Magnus Guldberg Rice, Dylan Wambugu, George Fidele, Nshimiyimana Jules Hribljan, Melita Cacic Arbune, Anca Alina Larsen, Sidsel Armand Beniczky, Sandor Mateen, Farrah J. |
| author_facet | Enkhtsetseg, Nomin Lehn-Schiøler, William Storgaard, Anton Mosquera Pedersen, Magnus Guldberg Rice, Dylan Wambugu, George Fidele, Nshimiyimana Jules Hribljan, Melita Cacic Arbune, Anca Alina Larsen, Sidsel Armand Beniczky, Sandor Mateen, Farrah J. |
| contents | Purpose: Access to electroencephalography (EEG) remains limited across low- and middle-income countries (LMICs) due to cost, infrastructure requirements, and a shortage of trained staff. This study evaluated the feasibility and clinical utility of a smartphone-based EEG system in a real-world setting.
Methods: We conducted a multicenter observational study (November 2023 to April 2026) across 29 clinical sites in Kenya. A smartphone-based 27-lead EEG system enabled trained healthcare workers to acquire standardized recordings with remote expert interpretation.
Results: 3,036 EEG sessions were performed. Male patients constituted 57.8% of the cohort, with representation across pediatric and adult populations. The most common referral indication was seizures or convulsions (68.5%). Overall, 2,915 (96%) recordings were interpretable, while 121 (4%) were uninterpretable, primarily due to high electrode impedance and insufficient recording duration. Uninterpretable recordings were significantly shorter than interpretable recordings (mean 18.5 vs. 33.8 minutes; median 15.1 vs. 31.6 minutes; p < 0.0001). Mean turnaround time for interpretation was 107 minutes.
Among interpretable recordings, 917 (30.2%) were abnormal, including 701 (76.4%) with epileptiform abnormalities, 215 (23.4%) with non-epileptiform findings, and 1 (0.1%) indeterminate finding. Epileptiform abnormalities were highest in children aged 4-9 years (33.1%) and less frequent in adults (14-21%). Non-epileptiform abnormalities were more common in patients aged 60+ years (19.2%) compared to younger age groups (3-9%).
Conclusion: Large-scale, point-of-care EEG acquisition by non-specialist operators in a resource-limited setting is feasible. Expansion of smartphone-based EEG systems may improve equitable access to neurological diagnosis and care in LMICs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_08157 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Clinical Utility and Feasibility of Smartphone-based EEG in Kenya: A Multicenter Observational Study Enkhtsetseg, Nomin Lehn-Schiøler, William Storgaard, Anton Mosquera Pedersen, Magnus Guldberg Rice, Dylan Wambugu, George Fidele, Nshimiyimana Jules Hribljan, Melita Cacic Arbune, Anca Alina Larsen, Sidsel Armand Beniczky, Sandor Mateen, Farrah J. Signal Processing Computers and Society Purpose: Access to electroencephalography (EEG) remains limited across low- and middle-income countries (LMICs) due to cost, infrastructure requirements, and a shortage of trained staff. This study evaluated the feasibility and clinical utility of a smartphone-based EEG system in a real-world setting. Methods: We conducted a multicenter observational study (November 2023 to April 2026) across 29 clinical sites in Kenya. A smartphone-based 27-lead EEG system enabled trained healthcare workers to acquire standardized recordings with remote expert interpretation. Results: 3,036 EEG sessions were performed. Male patients constituted 57.8% of the cohort, with representation across pediatric and adult populations. The most common referral indication was seizures or convulsions (68.5%). Overall, 2,915 (96%) recordings were interpretable, while 121 (4%) were uninterpretable, primarily due to high electrode impedance and insufficient recording duration. Uninterpretable recordings were significantly shorter than interpretable recordings (mean 18.5 vs. 33.8 minutes; median 15.1 vs. 31.6 minutes; p < 0.0001). Mean turnaround time for interpretation was 107 minutes. Among interpretable recordings, 917 (30.2%) were abnormal, including 701 (76.4%) with epileptiform abnormalities, 215 (23.4%) with non-epileptiform findings, and 1 (0.1%) indeterminate finding. Epileptiform abnormalities were highest in children aged 4-9 years (33.1%) and less frequent in adults (14-21%). Non-epileptiform abnormalities were more common in patients aged 60+ years (19.2%) compared to younger age groups (3-9%). Conclusion: Large-scale, point-of-care EEG acquisition by non-specialist operators in a resource-limited setting is feasible. Expansion of smartphone-based EEG systems may improve equitable access to neurological diagnosis and care in LMICs. |
| title | Clinical Utility and Feasibility of Smartphone-based EEG in Kenya: A Multicenter Observational Study |
| topic | Signal Processing Computers and Society |
| url | https://arxiv.org/abs/2605.08157 |