Key result
A proposed technique using local context windows around R peaks achieved a mean accuracy of about 99% in identifying different segments in ECG signals from the standard QT database.
A proposed computational technique using local context windows around R peaks achieved approximately 99% accuracy in identifying key ECG segments on the standard QT database.
Electrocardiography (ECG or EKG) is a widely employed non invasive technique to determine the condition of human heart and detect any abnormal cardiac behavior. Computer systems for ECG analysis can aid physicians in prompt detection of dangerous events such as ventricular fibrillation in patients with high cardiac risks. The first and crucial part of automatic analysis of ECG signals is to accurately identify and measure characteristic features of ECG signal, which is to locate exact position of the onset and offset points of P, QRS and T-waves. In this paper, a fast and robust technique is proposed that can accurately identify these key reference points using local windows around R peaks. The proposed method has been tested on standard QT database and a mean accuracy of about 99% is achieved on identifying different segments in ECG signal.
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Bisharad et al. (2018) studied Normal ECG Signals. Local context windows around R peaks was evaluated on Accuracy of identifying different segments in ECG signal. A proposed technique using local context windows around R peaks achieved a mean accuracy of about 99% in identifying different segments in ECG signals from the standard QT database.
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