Multi-Class Electrogastrogram (EGG) Signal Classification Using Machine Learning Algorithms
| Autor: | Md. Mohsin Sarker Raihan, Rahat Bin Preo, Abdullah Bin Shams |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
medicine.diagnostic_test
business.industry 02 engineering and technology Function (mathematics) Logistic regression Machine learning computer.software_genre 01 natural sciences Signal k-nearest neighbors algorithm Support vector machine Statistical classification Electrogastrogram 0103 physical sciences 0202 electrical engineering electronic engineering information engineering medicine 020201 artificial intelligence & image processing Artificial intelligence 010306 general physics F1 score business Algorithm computer Mathematics |
| Zdroj: | 2020 23rd International Conference on Computer and Information Technology (ICCIT). |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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