Key result
A machine learning approach based on instantaneous frequency achieved 100% precision for detecting diseased ECG signals and 77.27% precision for classifying ventricular arrhythmias.
Why the study?
Does a machine learning classifier based on instantaneous frequency accurately detect and classify ventricular arrhythmias on ECG?
Does a machine learning classifier based on instantaneous frequency accurately detect and classify ventricular arrhythmias on ECG?
A machine learning model based on instantaneous frequency can detect normal versus diseased ECG signals with 100% precision and classify ventricular arrhythmias with 77.27% precision.
May support instantaneous frequency ML for ECG arrhythmia detection; leaves open clinical utility pending larger validation.
This paper focuses on detecting diseased signals and arrhythmias classification into two classes: ventricular tachycardia and premature ventricular contraction. The sole purpose of the signal detection is used to determine if a signal has been collected from a healthy or sick person. The proposed research approach presents a mathematical model for the signal detector based on calculating the instantaneous frequency (IF). Once a signal taken from a patient is detected, then the classifier takes that signal as input and classifies the target disease by predicting the class label. While applying the classifier, templates are designed separately for ventricular tachycardia and premature ventricular contraction. Similarities of a given signal with both the templates are computed in the spectral domain. The empirical analysis reveals precisions for the detector and the applied classifier are 100% and 77.27%, respectively. Moreover, instantaneous frequency analysis provides a benchmark that IF of a normal signal ranges from 0.8 to 1.1 Hz whereas IF range for ventricular tachycardia and premature ventricular contraction is 0.08–0.6 Hz. This indicates a serious loss of high-frequency contents in the spectrum, implying that the heart’s overall activity is slowed down. This study may help medical practitioners in detecting the heart disease type based on signal analysis.
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Abrar M. Alajlan (2021) studied Ventricular arrhythmias (ventricular tachycardia and premature ventricular contraction). Machine learning classification based on instantaneous frequency (IF) and spectral domain templates was evaluated on Precision of the signal detector and classifier. A machine learning approach based on instantaneous frequency achieved 100% precision for detecting diseased ECG signals and 77.27% precision for classifying ventricular arrhythmias.
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