Key result
Walsh-Hadamard transformation and Fast Fourier Transformation were utilized to extract primary attributes, segments, and intervals from ECG signals for the detection of cardiac arrhythmias.
Walsh-Hadamard and Fast Fourier transformations can be used to extract key features from ECG signals for arrhythmia detection.
Provides a signal-processing method for arrhythmia detection; leaves open prospective clinical validation before adoption.
In today's world, irrespective of age, cardiac arrhythmia (heart abnormalities) is common among people, which must be detected and treated at the very earliest stage. Electrocardiograph (ECG) plays a vital role in detecting various cardiac arrhythmias. ECG waveform provides entire information about the electrical activity of the heart. In this paper, we are mainly focusing on the primary attributes P, Q, R, S, T, their segments and intervals of ECG Signals. Walsh-Hadamard transformation (WHT) and Fast Fourier Transformation (FFT) are performed to extract the required features from ECG signal.
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Akilandeswari et al. (2014) studied Cardiac arrhythmia. Walsh-Hadamard transformation (WHT) and Fast Fourier Transformation (FFT) was evaluated on Extraction of primary attributes P, Q, R, S, T, their segments and intervals of ECG Signals. Walsh-Hadamard transformation and Fast Fourier Transformation were utilized to extract primary attributes, segments, and intervals from ECG signals for the detection of cardiac arrhythmias.
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