Key result
Wavelet packet transform and error backpropagation neural networks were proposed to analyze and classify phonocardiography signals for cardiac diagnosis.
Why the study?
While cardiac auscultation is a useful clinical tool, PCG signals are difficult to analyze in the time or frequency domain without computational techniques.
The study proposes a computational method using wavelet packet transform and backpropagation neural networks to analyze and classify phonocardiography signals for diagnosing cardiac structural issues.
Answers are grounded in this paper and cite their sources.
May aid automated PCG classification; hypothesis-generating and requires prospective validation before clinical use.
Phonocardiography signals (PCG) show the enrolment of sounds and distortions generated from cardiac auscultation. Cardiac signal analysis is crucial for the diagnosis of various diseases. In previous years, there are several groups and different techniques, freshness, and methods have been proposed to analyze the cardiac signal. Auscultation is a manner in which a stethoscope is used to hearken to the cardiac sound. Structural trouble of the heart is often reflected in the cardiac sound created and Listening to the heart’s sound helped doctors diagnose and predict diseases. Whilst a cardiac sound examine via Listening is appropriate as a scientific tool, it is tough to analysis PCG signals in the time(T) or frequency(F) scale. The PCG signals have many benefits over conventional Listening, in that they may be rebuilt and analyzed for time(T) and frequency(F) information. Using a wavelet packet transform(WPT). Where the signal is decomposed and rebuild without the first-rate loss of data within the signal content. Reconstruction mistakes can be thoughtfulness an important piece of information in classifying the pathological severity of phonocardiography signals. In this paper we will focus on how to choose the level and the mother wave of the wave to cry out so that it is appropriate to analyze the cardiac signal in good mathematical and analytical ways to train the neural network (error backpropagation) on it. It will be explained in the following details.
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Abed et al. (2020) studied Cardiac diseases (PCG signal analysis). Wavelet packet transform and error backpropagation neural network was evaluated on Classification of pathological severity of phonocardiography signals. Wavelet packet transform and error backpropagation neural networks were proposed to analyze and classify phonocardiography signals for cardiac diagnosis.
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