Graduate School of Biomedical Engineering, University of New South Wales, Sydney, NSW 2052, Australia.
Physiol Meas. 2012 Mar;33(3):465-86. doi: 10.1088/0967-3334/33/3/465. Epub 2012 Feb 28.
Accurate systolic and diastolic pressure estimation, using automated blood pressure measurement, is difficult to achieve when the transduced signals are contaminated with noise or interference, such as movement artifact. This study presents an algorithm for automated signal quality assessment in blood pressure measurement by determining the feasibility of accurately detecting systolic and diastolic pressures when corrupted with various levels of movement artifact. The performance of the proposed algorithm is compared to a manually annotated reference scoring (RS). Based on visual representations and audible playback of Korotkoff sounds, the creation of the RS involved two experts identifying sections of the recorded sounds and annotating sections of noise contamination. The experts determined the systolic and diastolic pressure in 100 recorded Korotkoff sound recordings, using a simultaneous electrocardiograph as a reference signal. The recorded Korotkoff sounds were acquired from 25 healthy subjects (16 men and 9 women) with a total of four measurements per subject. Two of these measurements contained purposely induced noise artifact caused by subject movement. Morphological changes in the cuff pressure signal and the width of the Korotkoff pulse were extracted features which were believed to be correlated with the noise presence in the recorded Korotkoff sounds. Verification of reliable Korotkoff pulses was also performed using extracted features from the oscillometric waveform as recorded from the inflatable cuff. The time between an identified noise section and a verified Korotkoff pulse was the key feature used to determine the validity of possible systolic and diastolic pressures in noise contaminated Korotkoff sounds. The performance of the algorithm was assessed based on the ability to: verify if a signal was contaminated with any noise; the accuracy, sensitivity and specificity of this noise classification, and the systolic and diastolic pressure differences between the result obtained from the algorithm and the RS. 90% of the actual noise contaminated signals were correctly identified, and a sample-wise accuracy, sensitivity and specificity of 97.0%, 80.61% and 98.16%, respectively, were obtained from 100 pooled signals. The mean systolic and diastolic differences were 0.37 ± 3.31 and 3.10 ± 5.46 mmHg, respectively, when the artifact detection algorithm was utilized, with the algorithm correctly determined if the signal was clean enough to attempt an estimation of systolic or diastolic pressures in 93% of blood pressure measurements.
当转换后的信号受到噪声或干扰（如运动伪影）的污染时，使用自动血压测量很难准确估计收缩压和舒张压。本研究提出了一种用于自动信号质量评估的算法，通过确定在受到不同程度运动伪影污染时准确检测收缩压和舒张压的可行性来评估血压测量中的信号质量。将所提出的算法的性能与手动注释参考评分（RS）进行了比较。基于柯氏音的可视表示和可听回放，创建 RS 涉及两名专家识别记录声音的部分并注释噪声污染的部分。专家们使用同时记录的心电图作为参考信号，确定了 100 个记录的柯氏音录音中的收缩压和舒张压。记录的柯氏音声音是从 25 名健康受试者（16 名男性和 9 名女性）中获得的，每个受试者有 4 次测量。其中两次测量包含由于受试者运动引起的故意噪声伪影。提取的特征包括袖带压力信号的形态变化和柯氏音脉冲的宽度，这些特征被认为与记录的柯氏音声音中的噪声存在相关。还使用从充气袖带记录的示波法波形中提取的特征来验证可靠的柯氏音脉冲。确定噪声部分和验证的柯氏音脉冲之间的时间是确定噪声污染的柯氏音声音中可能的收缩压和舒张压的有效性的关键特征。算法的性能评估基于以下能力：验证信号是否受到任何噪声污染；这种噪声分类的准确性、灵敏度和特异性，以及从算法获得的收缩压和舒张压与 RS 结果之间的差异。90%的实际噪声污染信号被正确识别，从 100 个混合信号中获得了 97.0%、80.61%和 98.16%的样本准确率、灵敏度和特异性。当使用人工制品检测算法时，平均收缩压和舒张压差异分别为 0.37 ± 3.31 和 3.10 ± 5.46 mmHg，在 93%的血压测量中，算法正确确定信号是否足够干净以尝试估计收缩压或舒张压。
