This paper explores the efficacy of Mel Frequency Cepstral Coefficients (MFCCs) in detecting abnormal phonocardiograms using two classification strategies: a single-classifier and an ensemble-classifier approach. Phonocardiograms were segmented into S1, systole, S2, and diastole intervals, with thirteen MFCCs estimated from each segment, yielding 52 MFCCs per beat. In the single-classifier strategy, the MFCCs from nine consecutive beats were averaged to classify phonocardiograms. Conversely, the ensemble-classifier strategy employed nine classifiers 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. Results demonstrated that the ensemble-classifier strategy achieved higher accuracy compared to the single-classifier approach, 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）在检测异常心音图方面的效能，采用了两种分类策略：单分类器方法和集成分类器方法。心音图被分割为S1、收缩期、S2和舒张期区间，从每个区间估计出13个MFCCs，从而每个心搏产生52个MFCCs。在单分类器策略中，对连续九个心搏的MFCCs进行平均以对心音图进行分类。相反，集成分类器策略采用九个分类器分别评估每个心搏为正常或异常，整体分类基于多数投票原则。两种方法均在公开可用的心音图数据库上进行了测试。结果表明，与单分类器方法相比，集成分类器策略获得了更高的准确率，从而确立了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）具有更高的有效性。