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首页> 外文期刊>Journal of Electrical and Electronics Engineering Research >Detection of acute hypotensive episodes via a trained adaptive network-based fuzzy inference system (ANFIS)
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Detection of acute hypotensive episodes via a trained adaptive network-based fuzzy inference system (ANFIS)

机译：通过训练有素的基于自适应网络的模糊推理系统（ANFIS）来检测急性低血压发作

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The aim of this study is to detect acute hypotensive episodes (AHE) and mean arterial pressure dropping regimes (MAPDRs) using ECG signal and arterial blood pressure (ABP) waveforms. To meet this end, the QRS complexes and end-systolic end-diastolic pulses are first extracted using two innovative modified Hilbert transform-based algorithms namely as ECGMHT and BPMHT. The resulted systolic blood pressure (SBP) and diastolic blood pressure (DBP) pulses are then used to calculate the mean arterial pressure (MAP) trend. A new smoothing algorithm is then developed based on piecewise polynomial fitting (PPF) to smooth the fast fluctuations observed in RR-tachogram and MAP trend. The PPF algorithm operates by sequentially fitting N number of polynomials to the original signal and calculating the corresponding coefficients using the Best Linear Unbiased Estimation (BLUE) approach. Afterwards, in order to consider the mutual influence of parameters on the evaluation of shock probability, a Sugeno adaptive network-based fuzzy inference system-ANFIS is trained using Hasdai et al. parameters as input, with appropriate membership functions for each parameter. Using this network, it will be possible to incorporate the possible mutual influences between risk parameters such as heart rate (HR), systolic blood pressure (SBP), diastolic blood pressure (DBP), age, gender, weight and some miscellaneous factors to the calculation of shock occurrence probability. In the next step, the proposed algorithm is applied to 15 subjects of the MIMIC II database and AHE and MAPDRs (MAP ≤ 60 mmHg with a period of 30 min or more) are identified. As a result of this study, MAPDR is realized as a specific marker of cardiogenic shock. In that, for a sequence of MAPDRs; as long as 20 min or more, there will exist a consequent high peak with the duration of 3 to 4 min in the corresponding probability of cardiogenic shock diagram. The presented algorithm did not yield any inappropriate or wrong results on MIMICII database (that is false negative = false positive = 0).
机译：这项研究的目的是使用ECG信号和动脉血压（ABP）波形检测急性降压发作（AHE）和平均动脉压下降方案（MAPDR）。为了达到这个目的，首先使用两种创新的基于希尔伯特变换的改进算法（如ECGMHT和BPMHT）提取QRS络合物和收缩末期舒张末期脉冲。然后将所得的收缩压（SBP）和舒张压（DBP）脉冲用于计算平均动脉压（MAP）趋势。然后，基于分段多项式拟合（PPF）开发了一种新的平滑算法，以平滑RR轮速图和MAP趋势中观察到的快速波动。 PPF算法通过将N个多项式顺序拟合到原始信号并使用最佳线性无偏估计（BLUE）方法计算相应的系数来进行操作。然后，为了考虑参数对冲击概率评估的相互影响，使用Hasdai等人训练了基于Sugeno自适应网络的模糊推理系统ANFIS。参数作为输入，每个参数具有适当的隶属函数。使用该网络，可以将诸如心率（HR），收缩压（SBP），舒张压（DBP），年龄，性别，体重和一些其他因素等风险参数之间可能的相互影响合并在一起。计算冲击发生概率。在下一步中，将拟议的算法应用于MIMIC II数据库的15个对象，并识别出AHE和MAPDR（MAP≤60 mmHg，周期为30分钟或更长时间）。这项研究的结果是，MAPDR被认为是心源性休克的特定标志物。在此，对于一系列MAPDR;只要持续20分钟或更长时间，相应的心源性休克图表中就会出现一个持续时间为3-4分钟的高峰值。所提出的算法在MIMICII数据库上没有产生任何不适当或错误的结果（即假阴性=假阳性= 0）。

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