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A new HCM heart sound classification method based on weighted bispectrum features
Fang Yu1, Huang Zhiyuan1, Leng Hongxia1
1School of Electrical Engineering and Electronic Information, Xihua University, Chengdu, China.
Insights
Early diagnosis of hypertrophic cardiomyopathy (HCM) is crucial for preventing sudden cardiac arrest. This study introduces a novel heart sound analysis method for accurate HCM classification in young patients.
Area of Science:
- Cardiology
- Biomedical Engineering
- Signal Processing
Background:
- Hypertrophic cardiomyopathy (HCM), encompassing obstructive and non-obstructive forms, poses a risk of sudden cardiac arrest in adolescents and athletes.
- Early detection via heart sound auscultation is vital for preventing adverse events, yet differentiating HCM types based on pressure gradients is challenging.
- Current diagnostic methods may lack the efficiency and cost-effectiveness required for widespread screening.
Purpose of the Study:
- To develop an efficient and cost-effective classification method for hypertrophic cardiomyopathy (HCM) using heart sound (HS) analysis.
- To accurately distinguish between normal, obstructive HCM, and non-obstructive HCM using advanced signal processing techniques.
- To provide a reliable tool for the early diagnosis of HCM in young individuals.
Main Methods:
- Heart sounds (HSs) were preprocessed to remove background noise.
- Bispectrum contour maps were generated, and 56-dimensional features were extracted to capture pathological information.
- An adaptive threshold weighting mutual information method was employed for feature selection and weighted fusion.
- A Convolutional Neural Network-Random Forest (CNN-RF) classifier was developed for automated HCM type identification.
Main Results:
- The proposed method achieved a high classification accuracy of 94.4% on a clinical dataset.
- The technique successfully differentiated between normal heart sounds and the two types of HCM.
- Feature extraction and selection methods effectively represented pathological information from heart sounds.
Conclusions:
- The developed heart sound analysis method offers a reliable and accurate approach for diagnosing hypertrophic cardiomyopathy (HCM) in clinical settings.
- This technique provides a cost-effective solution for early HCM detection, particularly in young patients.
- The findings support the use of advanced signal processing and machine learning for non-invasive cardiac diagnostics.
Abstract:
Hypertrophic cardiomyopathy (HCM), including obstructive HCM and non-obstructive HCM, can lead to sudden cardiac arrest in adolescents and athletes. Early diagnosis and treatment through auscultation of different types of HCM can prevent the occurrence of malignant events. However, it is challenging to distinguish the pathological information of HCM related to differential left ventricular outflow tract pressure gradients. To address this issue, a classification method based on weighted bispectrum features of heart sounds (HSs) is proposed for efficient and cost-effective HCM analysis. Preprocessing is first applied to remove background noise during HS acquisition. Then, the bispectrum contour map is calculated, and 56-dimensional features are extracted to represent the pathological information of HCM. Next, an adaptive threshold weighting mutual information method is proposed for feature selection and weighted fusion. Finally, the CNN-RF classifier model is built to automatically identify different types of HCM cases. A clinical dataset of normal and two types of HCM HSs is utilized for validation. The results show that the proposed method performs well, with a classification accuracy reaching 94.4%. It provides a reliable reference for HCM diagnosis in young patients in clinical settings.
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