Key result
An instantaneous frequency estimation classifier for ECG signals achieved 97.82% sensitivity and 100% specificity in differentiating diseased from normal signals.
Why the study?
Does instantaneous frequency estimation of ECG signals accurately detect cardiac arrhythmias?
Does instantaneous frequency estimation of ECG signals accurately detect cardiac arrhythmias?
An instantaneous frequency-based ECG classifier demonstrates high sensitivity and specificity for detecting cardiac arrhythmias, suggesting utility for ambulatory ECG analysis.
May facilitate automated arrhythmia detection in ambulatory ECG; leaves open prospective clinical validation.
Preliminary detection and classification of cardiac arrhythmia is one of the most important problems in biomedical signal processing. This research proposes the estimation of instantaneous frequency (IF) of an ECG signal as a method for carrying out detection of cardiac disorder. Based on IF estimates, a classifier has been designed to differentiate a diseased signal from a normal one. Training, testing and validation of the classifier has been carried out using signals from MIT Arrhythmia, Normal Sinus Rhythm and Ventricular Arrhythmia databases, respectively. The sensitivity and specificity of the classifier comes out to be 97.82% and 100%, respectively and it is considered especially suitable for ambulatory ECG analysis.
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Bakhshi et al. (2010) studied Cardiac arrhythmia. Instantaneous frequency (IF) estimation of ECG signals vs. Normal signals was evaluated on Detection and classification of cardiac arrhythmia. An instantaneous frequency estimation classifier for ECG signals achieved 97.82% sensitivity and 100% specificity in differentiating diseased from normal signals.
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