基于时频组合特征与自适应模糊神经网络的心音分类算法

Heart sound classification algorithm based on time-frequency combination feature and adaptive fuzzy neural network

特征提取方法和分类器的选择是心音分类中的两个重要环节。为了充分捕捉心音信号中的病理性特征,研究中引入了一种结合梅尔频率倒谱系数(MFCC)和功率谱密度(PSD)的特征提取方法。与目前常规分类器不同,研究中选择了自适应模糊神经网络(ANFIS)为分类器。在实验设计方面,选取了不同时期、不同频率范围的PSD进行对比,选出分类效果最佳的特征,并采用均值PSD、标准差PSD、方差PSD和中位PSD四种不同的功率谱统计特性进行对比。通过实验比较,心音收缩期100~300 Hz的中位PSD和MFCC组合特征有最好的效果,在准确率、精确率、灵敏度、特异度和F1得分上分别达到96.50%、99.27%、93.35%、99.60%和96.35%。结果显示本研究所提算法对先心病辅助诊断具有较大帮助。

Feature extraction methods and classifier selection are two critical steps in heart sound classification.To capture the pathological features of heart sound signals,this paper introduces a feature extraction method that combines mel-frequency cepstral coefficients(MFCC)and power spectral density(PSD).Unlike conventional classifiers,the adaptive neuro-fuzzy inference system(ANFIS)was chosen as the classifier for this study.In terms of experimental design,we compared different PSDs across various time intervals and frequency ranges,selecting the characteristics with the most effective classification outcomes.We compared four statistical properties,including mean PSD,standard deviation PSD,variance PSD,and median PSD.Through experimental comparisons,we found that combining the features of median PSD and MFCC with heart sound systolic period of 100–300 Hz yielded the best results.The accuracy,precision,sensitivity,specificity,and F1 score were determined to be 96.50%,99.27%,93.35%,99.60%,and 96.35%,respectively.These results demonstrate the algorithm’s significant potential for aiding in the diagnosis of congenital heart disease.