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用于检测病理性心脏杂音的自动心脏听诊

Automated cardiac auscultation for detection of pathologic heart murmurs.

作者信息

Thompson W R, Hayek C S, Tuchinda C, Telford J K, Lombardo J S

机构信息

Department of Pediatrics, Division of Pediatric Cardiology, Johns Hopkins University School of Medicine, Baltimore, Maryland 21287, USA.

出版信息

Pediatr Cardiol. 2001 Sep-Oct;22(5):373-9. doi: 10.1007/s002460010253.

Abstract

Experienced cardiologists can usually recognize pathologic heart murmurs with high sensitivity and specificity, although nonspecialists with less clinical experience may have more difficulty. Harsh, pansystolic murmurs of intensity grade > or = 3 at the left upper sternal border (LUSB) are likely to be associated with pathology. In this study, we designed a system for automatically detecting systolic murmurs due to a variety of conditions and examined the correlation between relative murmur intensity and likelihood of pathology. Cardiac auscultatory examinations of 194 children and young adults were recorded, digitized, and stored along with corresponding echocardiographic diagnoses, and automated spectral analysis using continuous wavelet transforms was performed. Patients without heart disease and either no murmur or an innocent murmur (n = 95) were compared to patients with a variety of cardiac diagnoses and a pathologic systolic murmur present at the LUSB (n = 99). The sensitivity and specificity of the automated system for detecting pathologic murmurs with intensity grade > or = 2 were both 96%, and for grade > or = 3 murmurs they were 100%. Automated cardiac auscultation and interpretation may be useful as a diagnostic aid to support clinical decision making.

摘要

经验丰富的心脏病专家通常能够以较高的敏感性和特异性识别病理性心脏杂音,不过临床经验较少的非专科医生可能会遇到更多困难。胸骨左缘上部(LUSB)出现强度≥3级的粗糙全收缩期杂音很可能与病变有关。在本研究中,我们设计了一个用于自动检测各种情况下收缩期杂音的系统,并研究了相对杂音强度与病变可能性之间的相关性。记录了194名儿童和青年的心脏听诊检查,将其数字化并与相应的超声心动图诊断结果一起存储,然后使用连续小波变换进行自动频谱分析。将无心脏病且无杂音或有生理性杂音的患者(n = 95)与患有各种心脏疾病且在LUSB处出现病理性收缩期杂音的患者(n = 99)进行比较。该自动系统检测强度≥2级病理性杂音的敏感性和特异性均为96%,对于≥3级杂音,敏感性和特异性均为100%。自动心脏听诊和解读作为一种诊断辅助手段,可能有助于支持临床决策。

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