Key result
Multiscale energy analysis of 12-lead ECGs evaluates various wavelet basis functions to optimize MI detection.
Why the study?
Accurate detection of myocardial infarction using 12-lead ECG requires selection of the most suitable wavelet basis function for signal decomposition.
Which wavelet basis function is most suitable for detecting myocardial infarction in 12-lead ECG signals?
Which wavelet basis function is most suitable for detecting myocardial infarction in 12-lead ECG signals?
This study investigates various wavelet basis functions to optimize the automated detection of myocardial infarction from 12-lead ECG signals.
Informs wavelet selection for ECG MI detection algorithms; leaves open prospective clinical validation before practice adoption.
Over the decades, electrocardiogram (ECG) has been proved as the chief diagnostic tool for assessment of the cardiovascular condition of human being. Myocardial Infarction (MI) is commonly known as heart attack, happens when blood supply stops to heart muscles causing occlusions in some portion or whole artery. MI is the result of three pathological changes such as elevation of ST‐segment, the appearance of wide pathological Q‐wave and inversion of T‐wave in ECG record. Detection of MI by considering few ECG leads generally requires prior information about the pathological behaviour of the disease. The present work considers 12 leads to view the cardiac condition from various angles in ECG signal for accurate detection of MI. This Letter investigates on various wavelet basis functions, i.e. Haar, Daubechies, Symlet, Coiflet and biorthogonal basis filters of different order for selecting the most suitable one for the detection of MI. Wavelet transform of 12‐lead ECG signal decomposes the signal into different subbands. A comparative study has been done based on the multiscale energy at different wavelet subbands for the selection of most suitable wavelet basis for the accurate detection of MI. The experimentation is carried out on different datasets from the PTB diagnostic ECG database.
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Swain et al. (2018) conducted a letter in Myocardial Infarction. Wavelet transform basis functions (Haar, Daubechies, Symlet, Coiflet, biorthogonal) was evaluated on Selection of the most suitable wavelet basis for accurate detection of MI. Various wavelet basis functions were evaluated using multiscale energy analysis of 12-lead ECG signals to identify the most suitable wavelet for detecting myocardial infarction.
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