Key result
A zero cross QRS detection algorithm using support vector machine techniques achieved an average accuracy of 92.48% for ECG signal analysis across relaxed and typing subject conditions.
An application of a support vector machine technique for ECG signal analysis achieved a QRS detection average accuracy of 92.48% across different subject conditions.
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May support automated ECG QRS detection; leaves open prospective clinical validation before any practice change.
Interpretation of electrocardiogram (ECG) is one of the main research interests in bio-medical signal processing. The reason for the development of research on this interest are the growth in the heart health care all over the word, on the other hand the growth of digital technology makes it easier to detect disease from bio-signals. An application of support vector machine technique for ECG signals analysis is applied. To evaluate the proposed method, a practical experiment with two conditions of subjects (relax and typing) is conducted. With selected feature, the QRS detection average accuracy of 92.48% is achieved. It can be conclude that the applied method with various raw data provides a good results to diagnose pattern of cardiac arrhythmia disease.
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Turnip et al. (2018) studied Cardiac arrhythmia. Zero Cross QRS Detection Algorithm and Support Vector Machine was evaluated on QRS detection average accuracy. A zero cross QRS detection algorithm using support vector machine techniques achieved an average accuracy of 92.48% for ECG signal analysis across relaxed and typing subject conditions.
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