Clinical Utility and Feasibility of Smartphone-based EEG in Kenya: A Multicenter Observational Study

2026-05-04

AI Summary

This study addresses the limited accessibility of electroencephalography (EEG) in low- and middle-income countries, where high costs, inadequate infrastructure, and shortages of specialized personnel pose significant barriers. Conducted across 29 clinical centers in Kenya, it presents the first large-scale validation of a smartphone-integrated, 27-channel portable EEG system operated by trained non-specialist healthcare workers within a resource-constrained setting. Using a standardized acquisition protocol and a remote expert interpretation platform, the study successfully obtained high-quality EEG recordings from 3,036 patients, with 96% deemed interpretable and a mean turnaround time of 107 minutes for expert review. Abnormal findings were present in 30.2% of cases, with epileptiform discharges predominating in children and non-epileptiform abnormalities more common among older adults, thereby substantially enhancing equitable access to neurological diagnosis and care.

Abstract

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.

Problem

• EEG access
• low- and middle-income countries
• neurological diagnosis
• resource-limited settings
• healthcare disparity

Innovation

• smartphone-based EEG
• point-of-care diagnostics
• tele-neurology
• resource-limited settings
• scalable neurodiagnostics