Smartphone-based interventions for the diagnosis of epileptic seizures: A systematic review and meta-analysis.
Bibliographic record
Abstract
OBJECTIVE: The main objective of this study was to assess the utility of smartphone-based interventions for epilepsy diagnosis. METHODS: A systematic review was performed to evaluate the use of smartphone devices to diagnose epileptic seizures compared with encephalogram (EEG), using the MEDLINE, Scopus, Web of Science, and Embase databases. We plotted pooled sensitivity and specificity estimates on forest plots and on receiver operating characteristics curves. We evaluated evidence certainty using the GRADE (Grading of Recommendations Assessment, Development, and Evaluation) methodology. Also, we constructed a Fagan nomogram to guide clinical decision-making. RESULTS: We identified 10 studies that evaluated different smartphone-based interventions to diagnose epileptic seizures, including a mobile app of a clinical survey, smartphone-based studies assessing EEG recording, heart rate variability recording and classifier, and video of the epileptic seizure using a smartphone. However, we only performed a quantitative analysis for the smartphone videos and Smartphone Brain Scanner-2. We found that smartphone videos had sensitivity of 77% (95% confidence interval [CI] = 60%-88%) and specificity of 91% (95% CI = 88%-93%) to diagnostic epileptic seizure, with an area under the curve of .91. On the other hand, Smartphone Brain Scanner-2 had sensitivity of 44% (95% CI = 34%-55%) and specificity of 94% (95% CI = 89%-96%). The Epilepsy Diagnosis Aid app had good sensitivity of 76% (95% CI = 66%-84%) and specificity 100% (95% CI = .83%-1.00%); in addition, the RRBLE6:9123 device had sensitivity of 86%. SIGNIFICANCE: Our systematic review and meta-analysis demonstrate high specificity with the use of smartphone videos compared with studies assessing EEG. Smartphone-based interventions show promise for diagnosing epilepsy; however, further research is needed to assess the utility of other tools like symptom survey apps and heart rate variability recorders.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.028 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".