Key result
A novel classification method using Short-Time Fourier Transform and Gray Level Difference Method with a linear Support Vector Machine achieved a maximum accuracy of 83% in distinguishing normal heart sounds from murmurs.
Why the study?
Because auscultation relies on physician experience and knowledge, automated methods for analyzing heart sounds are needed to classify normal heart sounds and murmurs.
A novel method combining STFT spectrograms and GLDM texture analysis with SVM can classify normal heart sounds and murmurs with up to 83% accuracy.
Offers new image-processing approach for heart sound classification; leaves open clinical utility without prospective validation.
The heart sound coming from the patient is observed using a stethoscope, which is a medical tool to determine the patient's condition. The technique for this observation is called auscultation. This sound describes the condition of a person's heart. Because auscultation relies on the experience and knowledge of doctors, various methods for analyzing heart sounds are automatically developed by researchers. In this study, a method for classifying normal heart sounds and murmurs is proposed using the grey-level difference matrix (GLDM) feature taken from the short-time Fourier transform (STFT) plot. The STFT plot is converted into an image then the GLDM characteristics are calculated as input for the support vector machine as a classification. The experimental result shows that the highest accuracy of 83% is achieved using STFT 200-100 in four directions of GLDM. Even though this accuracy is not as high as the previous research, the proposed method is still open for exploration, such as distance selection in GLDM or other image analysis methods.
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Rizal et al. (2022) studied Heart murmurs (n=100). Short-Time Fourier Transform (STFT) and Gray Level Difference Method (GLDM) with Support Vector Machine (SVM) vs. Single direction GLDM features or other statistical parameters was evaluated on Classification accuracy. A novel classification method using Short-Time Fourier Transform and Gray Level Difference Method with a linear Support Vector Machine achieved a maximum accuracy of 83% in distinguishing normal heart sounds from murmurs.
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