Annu Int Conf IEEE Eng Med Biol Soc. 2021 Nov;2021:5527-5530. doi: 10.1109/EMBC46164.2021.9629519.
Cardiomyopathies diseases affects a great number of the elderly population. An adequate identification of the etiology of a cardiomyopathy patient is still a challenge. The aim of this study was to classify patients by their etiology in function of indexes extracted from the characterization of the pulse transit time (PTT). This time series represents the time taken by the pulse pressure to propagate through the length of the arterial tree and corresponding to the time between R peak of ECG and the mid-point of the diastolic to systolic slope in the blood pressure signal. For each patient, the PTT time series was extracted. Thirty cardiomyopathy patients (CMP) classified as ischemic (ICM - 15 patients) and dilated (DCM - 15 patients) were analyzed. Forty-three healthy subjects (CON) were used as a reference. The PTT time series was characterized through statistical descriptive indices and the joint symbolic dynamics method. The best indices were used to build support vector machine models. The optimal model to classify ICM versus DCM patients achieved 89.6% accuracy, 78.5% sensitivity, and 100% specificity. When comparing CMP patients and CON subjects, the best model achieved 91.3% accuracy, 91.3% sensitivity, and 88.3% specificity. Our results suggests a significantly lower pulse transit time in ischemic patients.Clinical relevance- This study analyzed the suitability of the pulse transit time for the classification of ICM and DCM patients.
心肌病影响着大量老年人群体。准确识别心肌病患者的病因仍然是一个挑战。本研究的目的是根据从脉搏传输时间（PTT）特征中提取的指标对患者进行病因分类。该时间序列表示脉搏压力在动脉树长度上传播所需的时间，对应于心电图 R 波峰值和血压信号中舒张期到收缩期斜率的中点之间的时间。为每位患者提取 PTT 时间序列。分析了 30 名心肌病患者（CMP），分为缺血性（ICM-15 名患者）和扩张型（DCM-15 名患者）。43 名健康受试者（CON）用作参考。通过统计描述性指标和联合符号动力学方法对 PTT 时间序列进行了特征描述。使用最佳指标构建支持向量机模型。用于区分 ICM 与 DCM 患者的最佳模型达到了 89.6%的准确率、78.5%的灵敏度和 100%的特异性。当比较 CMP 患者和 CON 受试者时，最佳模型达到了 91.3%的准确率、91.3%的灵敏度和 88.3%的特异性。我们的结果表明，缺血性患者的脉搏传输时间明显较低。临床意义-本研究分析了脉搏传输时间用于区分 ICM 和 DCM 患者的适宜性。
