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.

通过使用单一分类器和集成分类器方法，研究了使用 Mel 频率倒谱系数 (MFCCs) 在检测异常心脏音图中的功效。结果表明，与单一分类器方法相比，集成分类器方法获得了更高的准确性，从而将 MFCCs 确定为比其他特征（如时间、时间频率和统计特征）更有效的特征。