Treo Ernesto F, Herrera Myriam C, Valentinuzzi Max E
Departamento de Bioingeniería, Instituto Superior de Investigaciones Biológicas, Consejo Nacional de Investigaciones Científicas y Técnicas, Universidad Nacional de Tucumán, Argentina.
Biomed Eng Online. 2005 Aug 11;4:48. doi: 10.1186/1475-925X-4-48.
This project was designed as an epidemiological aid-selecting tool for a small country health center with the general objective of screening out possible coronary patients. Peripheral artery function can be non-invasively evaluated by impedance plethysmography. Changes in these vessels appear as good predictors of future coronary behavior. Impedance plethysmography detects volume variations after simple occlusive maneuvers that may show indicative modifications in arterial/venous responses. Averaging of a series of pulses is needed and this, in turn, requires proper determination of the beginning and end of each beat. Thus, the objective here is to describe an algorithm to identify and separate out beats from a plethysmographic record. A secondary objective was to compare the output given by human operators against the algorithm.
The identification algorithm detected the beat's onset and end on the basis of the maximum rising phase, the choice of possible ventricular systolic starting points considering cardiac frequency, and the adjustment of some tolerance values to optimize the behavior. Out of 800 patients in the study, 40 occlusive records (supradiastolic- subsystolic) were randomly selected without any preliminary diagnosis. Radial impedance plethysmographic pulse and standard ECG were recorded digitizing and storing the data. Cardiac frequency was estimated with the Power Density Function and, thereafter, the signal was derived twice, followed by binarization of the first derivative and rectification of the second derivative. The product of the two latter results led to a weighing signal from which the cycles' onsets and ends were established. Weighed and frequency filters are needed along with the pre-establishment of their respective tolerances. Out of the 40 records, 30 seconds strands were randomly chosen to be analyzed by the algorithm and by two operators. Sensitivity and accuracy were calculated by means of the true/false and positive/negative criteria. Synchronization ability was measured through the coefficient of variation and the median value of correlation for each patient. These parameters were assessed by means of Friedman's ANOVA and Kendall Concordance test.
Sensitivity was 97% and 91% for the two operators, respectively, while accuracy was cero for both of them. The synchronism variability analysis was significant (p < 0.01) for the two statistics, showing that the algorithm produced the best result.
The proposed algorithm showed good performance as expressed by its high sensitivity. The correlation analysis demonstrated that, from the synchronism point of view, the algorithm performed the best detection. Patients with marked arrhythmic processes are not good candidates for this kind of analysis. At most, they would be singled out by the algorithm and, thereafter, to be checked by an operator.
本项目旨在为一个小国的健康中心设计一种流行病学辅助筛选工具，总体目标是筛查出可能的冠心病患者。外周动脉功能可通过阻抗体积描记法进行无创评估。这些血管的变化似乎是未来冠心病病情的良好预测指标。阻抗体积描记法通过简单的闭塞操作检测体积变化，这些变化可能显示出动脉/静脉反应的指示性改变。需要对一系列脉搏进行平均，这反过来又需要正确确定每个搏动的开始和结束。因此，这里的目标是描述一种从体积描记记录中识别和分离搏动的算法。第二个目标是将人工操作员给出的输出与该算法进行比较。
识别算法基于最大上升阶段、考虑心率的可能心室收缩起始点的选择以及调整一些容差值以优化性能来检测搏动的起始和结束。在该研究的800名患者中，随机选择40份闭塞记录（舒张末期 - 收缩期），没有任何初步诊断。记录桡动脉阻抗体积描记脉搏和标准心电图，将数据数字化并存储。用功率密度函数估计心率，然后对信号进行两次求导，接着对一阶导数进行二值化处理，对二阶导数进行整流。后两个结果的乘积产生一个加权信号，从该信号中确定周期的起始和结束。需要加权滤波器和频率滤波器，并预先设定它们各自的容差。在这40份记录中，随机选择30秒的片段由该算法和两名操作员进行分析。通过真/假和正/负标准计算灵敏度和准确性。通过变异系数和每个患者的相关中位数来测量同步能力。这些参数通过弗里德曼方差分析和肯德尔和谐检验进行评估。
两名操作员的灵敏度分别为97%和91%，而两人的准确性均为零。对于这两个统计量，同步性变异性分析具有显著性（p < 0.01），表明该算法产生了最佳结果。
所提出的算法表现出良好的性能，其高灵敏度表明了这一点。相关性分析表明，从同步性角度来看，该算法的检测效果最佳。有明显心律失常过程的患者不适合进行这种分析。最多，他们会被该算法筛选出来，然后由操作员进行检查。
