Effective Learning and Filtering of Faulty Heart-Beats for Advanced ECG Arrhythmia Detection using MIT-BIH Database
| Autor: | Dimitra Azariadi, Dimitrios Soudris, Vasileios Tsoutsouras, Sotirios Xydis |
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| Rok vydání: | 2015 |
| Předmět: |
Arrhythmia detection
support-vector-machine (svm) lcsh:Medical technology Point of interest Computer science Speech recognition lcsh:R lcsh:Medicine Support vector machine QRS complex machine learning lcsh:R855-855.5 Mit bih database Heart beat ecg analysis false heart-beat filtering ECG analysis cardiovascular diseases ECG arrhythmia machine learning MIT-BIH Database Classifier (UML) |
| Zdroj: | EAI Endorsed Transactions on Pervasive Health and Technology, Vol 2, Iss 8 (2016) |
| DOI: | 10.4108/eai.14-10-2015.2261640 |
| Popis: | Electrocardiogram (ECG) signal has been established as one of the most fundamental bio-signals for monitoring and assessing the health status of a person. ECG analysis flow relies on the detection of points of interest on the signal with the QRS complex, located around an R peak of the heart beat, being the most commonly used. Using the MIT-BIH arrhythmia database, we evaluate the accuracy of various R peak detectors, showing a large number, i.e. several thousands, of falsely detected peaks. Considering the medical significance of the ECG analysis, we propose a machine learning based classifier to be incorporated in the ECG analysis flow aiming at identifying and discarding heart beats based on erroneously detected R peaks. Using Support Vector Machines (SVMs) and extensive exploration, we deliver a tuned classifier that i) successfully filters up to 75% of the false beats, ii) while keeping the correct beats mis-classified as false lower than 0.01% and iii) the computational overhead of the classifier sufficiently low. |
| Databáze: | OpenAIRE |
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