# In Vitro Evaluation of a Non-Invasive Photoplethysmography Based Intracranial Pressure Sensor
## 2 Material and Methods
### Data Analysis and Features Extraction  
The PPG signals acquired by the nICP monitor were filtered from noise and dc offset using a 10 th order band-pass Butterworth filter with a passband of 0.5-7 Hz. The invasive ICP signals collected by the pressure sensor in the intracranial chamber were low pass filtered at 10 Hz to remove high-frequency noise.  
The features illustrated in Figure 4 were then extracted from the filtered PPG signal in a 15 s rolling window. The features were extracted for each cardiac cycle (systole/diastole) and averaged to obtain a single value for the entire window. The Pulse Amplitude was calculated as the difference in magnitude between a minimum (or valley) and the succeeding peak. The Half Peak Width was estimated from the PPG signal as the horizontal distance (in number of sample points) between the points in the signal intercepting the reference line, set at half of the peak height (amplitude). The Systolic Risetime was derived as the time, in seconds, between each pulse peak and the preceding minimum point. Conversely, the Systolic Decay time was estimated as the time, in seconds, between each pulse peak and the following minimum point. The Systolic Upslope was estimated as the slope of  
Figure 3: Stepwise increases in intracranial pressure (ICP) measured inside the intracranial chamber. The zoom-in part at the top right shows the in vitro modelling of the cardio-synchronous vasogenic arterial pulsations normally observed in the ICP waveform measured in humans.  
the systolic phase of each pulse by determining the gradient in V/s (Volts/seconds) of the twenty sample points preceding and following the mid-point between each peak, and each minimum point of the PPG signal.  
As per its definition, the invasive (reference) ICP was estimated from the ICP waveform as the mean value across the rolling window. The signals were then time-segmented for each protocol step, outliers were removed, and, for each pressure step, the data were split in an 80/20% ratio to train and test the model, respectively.  
After standardising the features, a Partial Least Square Regression (PLSR) model was used to estimate the response variable (i.e., ICP). The PLSR regression model transforms the features into a combination of components (predictors) in which the correlation between predictors (i.e., collinearity) is minimised and the variance maximised with respect to the observed response variable. After training the PLSR model with the train data, the PLSR model's coefficients were used to compute the new components and estimate the non-invasive ICP (nICP) from the test data. The coefficient of determination, R\({}^{2}\), was computed to assess the adequacy of the model.  
The estimated nICP was compared with the reference invasive ICP for correlation and accuracy. Correlation between the two measures was assessed by computing the [PERSON]'s correlation coefficient. The accuracy of the measurement was assessed by calculating the Root Mean Square Error (RMSE) and analysis of agreement by [PERSON] and [PERSON] plots. A \(p\)-value \(<\) 0.05 was considered statistically significant.