This paper explores the efficacy of Mel Frequency Cepstral Coefficients (MFCCs) in detecting abnormal heart sounds using two classification strategies: a single classifier and an ensemble classifier approach. Heart sounds were first pre-processed to remove noise and then segmented into S1, systole, S2, and diastole intervals, with thirteen MFCCs estimated from each segment, yielding 52 MFCCs per beat. Finally, MFCCs were used for heart sound classification. For that purpose, in the single classifier strategy, the MFCCs from nine consecutive beats were averaged to classify heart sounds by a single classifier (either a support vector machine (SVM), the k nearest neighbors (kNN), or a decision tree (DT)). Conversely, the ensemble classifier strategy employed nine classifiers (either nine SVMs, nine kNN classifiers, or nine DTs) to individually assess beats as normal or abnormal, with the overall classification based on the majority vote. Both methods were tested on a publicly available phonocardiogram database. The heart sound classification accuracy was 91.95% for the SVM, 91.9% for the kNN, and 87.33% for the DT in the single classifier strategy. Also, the accuracy was 93.59% for the SVM, 91.84% for the kNN, and 92.22% for the DT in the ensemble classifier strategy. Overall, the results demonstrated that the ensemble classifier strategy improved the accuracies of the DT and the SVM by 4.89% and 1.64%, establishing MFCCs as more effective than other features, including time, time-frequency, and statistical features, evaluated in similar studies.

翻译：本文探讨了梅尔频率倒谱系数（MFCCs）在检测异常心音方面的效能，采用了两种分类策略：单分类器与集成分类器方法。心音首先经过预处理以去除噪声，随后被分割为S1、收缩期、S2和舒张期区间，并从每个区间估计出13个MFCCs，从而每个心搏产生52个MFCCs。最终，这些MFCCs被用于心音分类。为此，在单分类器策略中，将连续九个心搏的MFCCs进行平均，然后通过单一分类器（支持向量机（SVM）、k近邻（kNN）或决策树（DT）之一）对心音进行分类。相反，集成分类器策略则采用九个分类器（九个SVM、九个kNN分类器或九个DT）来独立评估每个心搏为正常或异常，整体分类基于多数投票决定。两种方法均在一个公开的心音图数据库上进行了测试。在单分类器策略中，心音分类准确率分别为：SVM 91.95%、kNN 91.9%、DT 87.33%。而在集成分类器策略中，准确率分别为：SVM 93.59%、kNN 91.84%、DT 92.22%。总体而言，结果表明集成分类器策略将DT和SVM的准确率分别提高了4.89%和1.64%，并证实MFCCs相较于其他特征（包括在类似研究中评估的时域、时频域和统计特征）更为有效。