Yang Xuankai, Sun Jing, Yang Hongbo, Guo Tao, Pan Jiahua, Wang Weilian
School of Information Science and Engineering, Yunnan University, Kunming, 650504, China.
Cardiovascular Hospital Affiliated to Kunming Medical University (Fuwai Yunnan Cardiovascular Hospital), Kunming, 650102, China.
Med Biol Eng Comput. 2025 Jan;63(1):29-44. doi: 10.1007/s11517-024-03173-1. Epub 2024 Aug 5.
Heart sound signals are vital for the machine-assisted detection of congenital heart disease. However, the performance of diagnostic results is limited by noise during heart sound acquisition. A limitation of existing noise reduction schemes is that the pathological components of the signal are weak, which have the potential to be filtered out with the noise. In this research, a novel approach for classifying heart sounds based on median ensemble empirical mode decomposition (MEEMD), Hurst analysis, improved threshold denoising, and neural networks are presented. In decomposing the heart sound signal into several intrinsic mode functions (IMFs), mode mixing and mode splitting can be effectively suppressed by MEEMD. Hurst analysis is adopted for identifying the noisy content of IMFs. Then, the noise-dominated IMFs are denoised by an improved threshold function. Finally, the noise reduction signal is generated by reconstructing the processed components and the other components. A database of 5000 heart sounds from congenital heart disease and normal volunteers was constructed. The Mel spectral coefficients of the denoised signals were used as input vectors to the convolutional neural network for classification to verify the effectiveness of the preprocessing algorithm. An accuracy of 93.8%, a specificity of 93.1%, and a sensitivity of 94.6% were achieved for classifying the normal cases from abnormal one.
心音信号对于先天性心脏病的机器辅助检测至关重要。然而，心音采集过程中的噪声限制了诊断结果的性能。现有降噪方案的一个局限性在于信号的病理成分较弱，有可能与噪声一起被滤除。在本研究中，提出了一种基于中值总体经验模态分解（MEEMD）、赫斯特分析、改进的阈值去噪和神经网络的心音分类新方法。在将心音信号分解为多个固有模态函数（IMF）时，MEEMD可以有效抑制模态混叠和模态分裂。采用赫斯特分析来识别IMF中的噪声成分。然后，通过改进的阈值函数对以噪声为主的IMF进行去噪。最后，通过重构处理后的成分和其他成分生成降噪信号。构建了一个包含5000个先天性心脏病患者和正常志愿者心音的数据库。将去噪信号的梅尔频谱系数用作卷积神经网络的输入向量进行分类，以验证预处理算法的有效性。在区分正常病例和异常病例时，准确率达到93.8%，特异性为93.1%，灵敏度为94.6%。
