Suppr超能文献

血压测量过程中柯氏音的变化:卷积神经网络分析。

Variation of the Korotkoff Stethoscope Sounds During Blood Pressure Measurement: Analysis Using a Convolutional Neural Network.

出版信息

IEEE J Biomed Health Inform. 2017 Nov;21(6):1593-1598. doi: 10.1109/JBHI.2017.2703115.

Abstract

Korotkoff sounds are known to change their characteristics during blood pressure (BP) measurement, resulting in some uncertainties for systolic and diastolic pressure (SBP and DBP) determinations. The aim of this study was to assess the variation of Korotkoff sounds during BP measurement by examining all stethoscope sounds associated with each heartbeat from above systole to below diastole during linear cuff deflation. Three repeat BP measurements were taken from 140 healthy subjects (age 21 to 73 years; 62 female and 78 male) by a trained observer, giving 420 measurements. During the BP measurements, the cuff pressure and stethoscope signals were simultaneously recorded digitally to a computer for subsequent analysis. Heartbeats were identified from the oscillometric cuff pressure pulses. The presence of each beat was used to create a time window (1 s, 2000 samples) centered on the oscillometric pulse peak for extracting beat-by-beat stethoscope sounds. A time-frequency two-dimensional matrix was obtained for the stethoscope sounds associated with each beat, and all beats between the manually determined SBPs and DBPs were labeled as "Korotkoff." A convolutional neural network was then used to analyze consistency in sound patterns that were associated with Korotkoff sounds. A 10-fold cross-validation strategy was applied to the stethoscope sounds from all 140 subjects, with the data from ten groups of 14 subjects being analyzed separately, allowing consistency to be evaluated between groups. Next, within-subject variation of the Korotkoff sounds analyzed from the three repeats was quantified, separately for each stethoscope sound beat. There was consistency between folds with no significant differences between groups of 14 subjects (P = 0.09 to P = 0.62). Our results showed that 80.7% beats at SBP and 69.5% at DBP were analyzed as Korotkoff sounds, with significant differences between adjacent beats at systole (13.1%, P = 0.001) and diastole (17.4%, P < 0.001). Results reached stability for SBP (97.8%, at sixth beat below SBP) and DBP (98.1%, at sixth beat above DBP) with no significant differences between adjacent beats (SBP P = 0.74; DBP P = 0.88). There were no significant differences at high-cuff pressures, but at low pressures close to diastole there was a small difference (3.3%, P = 0.02). In addition, greater within subject variability was observed at SBP (21.4%) and DBP (28.9%), with a significant difference between both (P < 0.02). In conclusion, this study has demonstrated that Korotkoff sounds can be consistently identified during the period below SBP and above DBP, but that at systole and diastole there can be substantial variations that are associated with high variation in the three repeat measurements in each subject.

摘要

柯氏音在血压(BP)测量过程中特征发生变化,导致收缩压和舒张压(SBP 和 DBP)的测定存在一些不确定性。本研究的目的是通过检查线性袖带放气过程中每个心跳从收缩期到舒张期的所有听诊器声音,评估 BP 测量过程中柯氏音的变化。由一名经过培训的观察者对 140 名健康受试者(年龄 21 至 73 岁;62 名女性和 78 名男性)进行三次重复 BP 测量,共获得 420 次测量。在 BP 测量过程中,袖带压力和听诊器信号同时被数字记录到计算机中,以便随后进行分析。使用振荡式袖带压力脉冲识别心跳。每个心跳的存在用于创建一个时间窗口(1 秒,2000 个样本),该时间窗口以振荡式脉搏峰值为中心,用于提取逐拍听诊器声音。为每个心跳获得一个听诊器声音的时频二维矩阵,并将手动确定的 SBP 和 DBP 之间的所有心跳标记为“柯氏音”。然后使用卷积神经网络分析与柯氏音相关的声音模式的一致性。应用 10 折交叉验证策略对来自所有 140 名受试者的听诊器声音进行分析,将 10 组 14 名受试者的数据分别进行分析,从而可以评估组之间的一致性。接下来,分别对每个听诊器声音的三次重复分析的柯氏音变化进行了量化。各袖带组之间的一致性没有显著差异(P=0.09 至 P=0.62)。我们的结果表明,80.7%的 SBP 收缩期和 69.5%的 DBP 舒张期分析为柯氏音,收缩期(13.1%,P=0.001)和舒张期(17.4%,P<0.001)相邻心跳之间有显著差异。SBP(第六次低于 SBP 时达到 97.8%)和 DBP(第六次高于 DBP 时达到 98.1%)的结果达到稳定,相邻心跳之间没有显著差异(SBP P=0.74;DBP P=0.88)。在较高的袖带压力下没有显著差异,但在接近舒张压的较低压力下有较小的差异(3.3%,P=0.02)。此外,在 SBP(21.4%)和 DBP(28.9%)中观察到更大的受试者内变异性,两者之间存在显著差异(P<0.02)。总之,本研究表明,在 SBP 以下和 DBP 以上期间可以一致地识别柯氏音,但在收缩期和舒张期,会有与每个受试者三次重复测量中高变异性相关的明显变化。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验