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- W3091723865 abstract "Auscultation of heart sounds is an effective clinical diagnosis of heart disease. The use of big data analysis and artificial intelligence algorithms for auxiliary diagnosis and treatment is of significance for solving the uneven distribution of medical resources. Based on the analysis of children's heart sound data collected in a third-class hospital in Shanghai, this paper proposes a method for classifying adolescent heart sounds based on wavelet analysis and random forest classifiers in view of the relatively high third heart sounds of adolescent heart sound data. This method first uses a bi-orthogonal wavelet transform to denoise the heart sound signal, then uses heart sound segmentation algorithm to segment the positions of S1, systole, S2, and diastole, S3. Finally, based on the time and frequency features as feature vectors, the random forest method is used to classify the youth heart sound detection data to predict normal and abnormal heart sounds. The experimental results show that the accuracy of the method in this paper can reach 87.5%. Compared with the conventional method, which only extracts S1, systole, S2, and diastole, the accuracy is improved by 3.1%." @default.
- W3091723865 created "2020-10-08" @default.
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- W3091723865 date "2020-06-01" @default.
- W3091723865 modified "2023-09-26" @default.
- W3091723865 title "A method for diagnosing heart sounds in adolescents based on wavelet analysis and random forest" @default.
- W3091723865 cites W2020459286 @default.
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- W3091723865 doi "https://doi.org/10.1109/icbaie49996.2020.00021" @default.
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