Ljungvall Ingrid, Ahlstrom Christer, Höglund Katja, Hult Peter, Kvart Clarence, Borgarelli Michele, Ask Per, Häggström Jens
Department of Clinical Sciences, Faculty of Veterinary Medicine and Animal Science, Swedish University of Agricultural Sciences, 750 07 Uppsala, Sweden.
Am J Vet Res. 2009 May;70(5):604-13. doi: 10.2460/ajvr.70.5.604.
To investigate use of signal analysis of heart sounds and murmurs in assessing severity of mitral valve regurgitation (mitral regurgitation [MR]) in dogs with myxomatous mitral valve disease (MMVD).
77 client-owned dogs.
Cardiac sounds were recorded from dogs evaluated by use of auscultatory and echocardiographic classification systems. Signal analysis techniques were developed to extract 7 sound variables (first frequency peak, murmur energy ratio, murmur duration > 200 Hz, sample entropy and first minimum of the auto mutual information function of the murmurs, and energy ratios of the first heart sound [S1] and second heart sound [S2]).
Significant associations were detected between severity of MR and all sound variables, except the energy ratio of S1. An increase in severity of MR resulted in greater contribution of higher frequencies, increased signal irregularity, and decreased energy ratio of S2. The optimal combination of variables for distinguishing dogs with high-intensity murmurs from other dogs was energy ratio of S2 and murmur duration > 200 Hz (sensitivity, 79%; specificity, 71%) by use of the auscultatory classification. By use of the echocardiographic classification, corresponding variables were auto mutual information, first frequency peak, and energy ratio of S2 (sensitivity, 88%; specificity, 82%).
Most of the investigated sound variables were significantly associated with severity of MR, which indicated a powerful diagnostic potential for monitoring MMVD. Signal analysis techniques could be valuable for clinicians when performing risk assessment or determining whether special care and more extensive examinations are required.
研究心音和杂音信号分析在评估患有黏液瘤性二尖瓣疾病(MMVD)的犬二尖瓣反流(二尖瓣反流[MR])严重程度中的应用。
77只宠物犬。
通过听诊和超声心动图分类系统对犬进行评估并记录心音。开发信号分析技术以提取7个声音变量(第一频率峰值、杂音能量比、杂音持续时间>200Hz、样本熵以及杂音自互信息函数的第一个最小值,以及第一心音[S1]和第二心音[S2]的能量比)。
除S1能量比外,在MR严重程度与所有声音变量之间均检测到显著相关性。MR严重程度增加导致高频成分贡献增加、信号不规则性增加以及S2能量比降低。通过听诊分类,区分高强度杂音犬与其他犬的最佳变量组合是S2能量比和杂音持续时间>200Hz(敏感性为79%;特异性为71%)。通过超声心动图分类,相应变量是自互信息、第一频率峰值和S2能量比(敏感性为88%;特异性为82%)。
大多数研究的声音变量与MR严重程度显著相关,这表明在监测MMVD方面具有强大的诊断潜力。信号分析技术对临床医生进行风险评估或确定是否需要特殊护理和更广泛检查可能很有价值。