论文阅读和分析:Applying a Deep Learning Network in Continuous Physiological Parameter Estimation

论文阅读和分析:Applying a Deep Learning Network in Continuous Physiological Parameter Estimation

论文阅读和分析：Applying a Deep Learning Network in Continuous Physiological Parameter Estimation Based on Photoplethysmography Sensor Signals

1、使用CNN-LSTM神经网络架构同时计算HR、SBP、DBP、MAP（心率和血压）；

2、通过Advancement of Medical Instrumentation (AAMI) and the British Hypertension Society (BHS)的标准；

3、在MIMIC II数据集上使用10-fold交叉验证；

4、采样频率125HZ；

5、预处理，将数据集中的数据长度过短、数据值异常的去掉；

网络架构：

网络层配置：

损失函数：

H δ ( y , f ( x ) ) = { 1 2 ( y − f ( x ) ) 2 , i f ∣ y − f ( x ) ∣ ≤ δ , δ ∣ y − f ( x ) ∣ − 1 2 δ 2 , o t h e r w i s e . H_delta(y,f(x))=begin{cases}frac{1}{2}(y-f(x))^2,&if~|y-f(x)|ledelta,\[6pt]delta|y-f(x)|-frac{1}{2}delta^2,&otherwise.end{cases} Hδ​(y,f(x))=⎩ ⎨ ⎧​21​(y−f(x))2,δ∣y−f(x)∣−21​δ2,​if ∣y−f(x)∣≤δ,otherwise.​
其中 δ = 1 delta=1 δ=1

评价指标：

M A E = 1 n ∑ i = 1 n ∣ y i − y ^ i ∣ M E = 1 n ∑ i = 1 n ( y i − y ^ i ) S D = 1 n ∑ i = 1 n ( x i − M E ) 2 begin{aligned} {MAE}& =frac{1}{n}sumlimits_{i=1}^nleft|y_i-hat{y}_iright| \ ME& =frac{1}{n}sumlimits_{i=1}^nleft(y_i-hat{y}_iright) \ SD& =sqrt{frac{1}{n}sumlimits_{i=1}^nleft(x_i-MEright)^2} end{aligned} MAEMESD​=n1​i=1∑n​∣yi​−y^​i​∣=n1​i=1∑n​(yi​−y^​i​)=n1​i=1∑n​(xi​−ME)2 ​​

实验结果：

参考：

Applying a Deep Learning Network in Continuous Physiological Parameter Estimation Based on Photoplethysmography Sensor Signals