Multi-Class Electrogastrogram (EGG) Signal Classification Using Machine Learning Algorithms
Bibliographic record
Abstract
Electrogastrogram (EGG) is a simple and non-invasive method in clinical practices for assessing the stomach function by observing the gastric myoelectrical activity extracted using the electrodes placed on the abdominal surface. EGG is a slow wave propagation. Based on the dominant frequency or cycle per minute, there are three types of EGG signals: Normogastria, Bradygastria, and Tachygastria. In this study, we used the Logistic Regression (LG), Support Vector Machine (SVM) and K Nearest Neighbor (KNN) Machine Learning (ML) algorithms to successfully classify two and three types (classes in ML terminology) of EGG signal with high accuracy. Our results show that the SVM algorithm performs best to classify the two and three class signals with an accuracy of 100% and 92.11% respectively, while logistic regression and the KNN algorithms demonstrate similar lower performances. SVM algorithm also achieved a maximum F1 score, precision, and recall value of 100% and 92% for the two and three classes of EGG signal respectively. An Area Under the Curve (AUC) score of 100% and 92% are observed in the two-class and three-class problem respectively in EGG signal classification using the SVM algorithm. Based on our analysis, we can conclude that SVM can be implemented successfully to accurately classify multi-class EGG signals.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".