Omari Tahar, Bereksi-Reguig Fethi
1Department of Biomedical Engineering, Boumerdes university, 35000 Boumerdes, Algeria.
2Department of Biomedical Engineering, Tlemcen university, 13000 Tlemcen, Algeria.
Biomed Eng Lett. 2019 Jun 7;9(3):395-406. doi: 10.1007/s13534-019-00113-z. eCollection 2019 Aug.
Continuous and non-invasive measurement of blood pressure (BP) is of great importance particularly for patients in critical state. To achieve continuous and cuffless BP monitoring, pulse transit time (PTT) has been reported as a potential parameter. Nevertheless, this approach remains very sensitive, cumbersome and disagreeable in ambulatory measurement. This paper proposes a new approach to estimate blood pressure through PCG signal by exploring the correlation between PTT and diastolic duration (S21). In this purpose, an artificial neural network was developed using as input data: (systolic duration, diastolic duration, heart rate, sex, height and weight). According to the NN decision, the mean blood pressure was measured and consequently the systolic and the diastolic pressures were estimated. The proposed method is evaluated on 37 subjects. The obtained results are satisfactory, where, the error in the estimation of the systolic and the diastolic pressures compared to the commercial blood pressure device was in the order of mmHg and mmHg, respectively, which are very close to the AAMI standard, mmHg. This shows the feasibility of estimating of blood pressure using PCG.
连续无创血压测量对于危重症患者尤为重要。为实现连续无袖带血压监测，脉搏传输时间（PTT）已被报道为一个潜在参数。然而，这种方法在动态测量中仍然非常敏感、繁琐且令人不适。本文提出了一种通过心音图（PCG）信号估计血压的新方法，该方法通过探索PTT与舒张期时长（S21）之间的相关性来实现。为此，开发了一种人工神经网络，其输入数据包括（收缩期时长、舒张期时长、心率、性别、身高和体重）。根据神经网络的决策，测量平均血压，进而估计收缩压和舒张压。该方法在37名受试者身上进行了评估。获得的结果令人满意，与商用血压设备相比，收缩压和舒张压估计的误差分别约为 mmHg和 mmHg，非常接近美国医疗器械促进协会（AAMI）标准 mmHg。这表明利用PCG估计血压是可行的。
