Classification of intracranial pressure epochs using a novel machine learning framework

Figures

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  Fig. 1. EVD functionality and stop-cock mechanism with waveform output. The figure illustrates an external ventricular drain (EVD) surgically inserted into a patient’s intracranial ventricular system. The EVD comprises several key components, including a flexible catheter that connects the brain’s ventricles to an external drainage system. This system features a graduated collection chamber to measure cerebrospinal fluid (CSF) volume, a pressure transducer for monitoring intracranial pressure (ICP), and a leveling device to ensure accurate alignment with the patient’s head for precise readings. A critical element of the EVD system is the three-way stopcock, a small valve with three ports and a rotatable valve (red) that controls CSF flow. When the stopcock is in position B (clamped), CSF flows from the ventricles to the pressure transducer only, allowing for accurate ICP waveform measurement without drainage. In positions A or C (draining), CSF flows from the ventricles to the collection chamber, generating an inaccurate ICP waveform at the transducer. Position D, rarely used, results in clamping without ICP waveform generation as the CSF bypasses the transducer. At regular intervals…

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  Fig. 2. Box plot of mean ICP values for each dataset. A box plot of mean ICP values for each dataset, with the red crosses showing average mean ICP value and the lines inside boxes showing median value. The lines at top of figure denote statistical significance between any two datasets. The presence of *** above the lines indicates significant differences (alpha value 0.05, p value extremely close to zero) in the mean ICP values among the datasets. The pairwise comparison was conducted using the Wilcoxon rank-sum test, as the datasets do not follow a normal distribution, necessitating a non-parametric alternative, and they differ in size. It is shown that all three datasets have significantly different distribution, underscoring and validating the model’s ability to generalize to unseen dataset with different mean ICP distribution.

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  Fig. 3. Performance comparison of XGBoost model with different feature sets. This plot shows the performance of the XGBoost model using different feature sets - and it was found that the expanded feature space improved the generalizability test results. It is interesting to note that the model performance on dataset 1 (10 fold CV test set) remains almost constant. However, performance in generalizing to dataset 2 improves significantly with increasing the number of features. This suggests that the model is robust and consistent in its prediction within the training set. The improvement in generalization performance highlights the importance of feature quality and relevance, and that inclusion of more informative features do boost the model’s ability to generalize.

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  Fig. 4. Confusion matrices and model performances across three datasets. This figure shows the confusion matrices on the outputs of the model on each Dataset to demonstrate model performance. a Dataset 1 - model training data performance using 10-fold stratified cross-validation b Trained model performance on dataset 2 - generalizability test data c Trained model performance on dataset 3 - `ground truth' validation data. A number following the ` ± ' symbol represents the standard deviation (s.d.) across 10 folds for (a) and the standard error of the mean (s.e.m.) for (b, c).

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  Fig. 5. Model feature importance and SHAP analysis on model output. This figure provides information about the features indentified as important by the model, as well as SHAP analysis of the outputs of the model. a Model feature importance plot. Top 10 most important features identified by the model, plotted in descending order of importance. The feature at the top of the chart represents the most significant contributor to the model’s predictions, with decreasing importance observed for the subsequent features. b The SHAP summary plot for binary-class evaluation. The y-axis list features that have the highest contribution to SHAP value in descending order of importance. Each dot on the plot represents a single epoch, with the color indicating the feature value --- blue for lower values and red for higher values, as shown on the color bar. The SHAP values on the x-axis measure each feature’s impact on the model’s prediction, where positive SHAP values (towards the right) indicate a feature’s contribution to classifying a segment as “Clamped,” and negative SHAP values (towards the left) suggest a contribution to the “Not Clamped” classification. c The SHAP force plot. This figure shows…

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  Fig. 6. CICL framework in action. A visualization of testing data from a single day for seven individual patients. The waveform is color-coded based on the corresponding CICL classification label, with a gray vertical line (a–g) marking specific region in the waveform where a zoomed-in 5-second segment and its mean values are observed. This visualization reveals valid clamped ICP data, including negative values. Additionally, the same waveform shape can occur at different scales, highlighting the variability in ICP waveform patterns across the dataset.

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  Fig. 7. Summary of methods. Each panel of the figure provides a summary of a key step in the overall methodology of the paper. a ICP waveform data from all three datasets underwent cleaning to remove outliers, correct inaccuracies, and handle missing values. The cleaned data was segmented into “epochs” using an established change-point detection tool with parallel processing of the segments. b Dataset 1 served as the training dataset. An initial set of seven features was extracted for each epoch in this dataset. Using unsupervised k-means clustering, these epochs were grouped into 100 clusters based on the extracted features. A neurointensivist manually labeled a large number of epochs using a rigorous sampling methodology which providing labeled data for model training. These epochs were then split into a train/test split. A grid search with 5-fold cross validation was used to optimize parameters. We used these labeled epochs, with the initial seven features, to train and compare six different supervised machine learning methods based on performance and accuracy. The best performing model, in this case XGBoost, was identified. To train the final model, an expanded set of 383 features…

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  Fig. 8. Overview of the labeling pipeline. This figure showcases the methodology used for labeling of the PELT segmented epochs. These labels were later used to train the models. In this figure, D, C, and N refer to “draining EVD,” “clamped ICP,” and “noise,” respectively. Following change point detection and k-means++ clustering, data epochs are grouped into 100 clusters. For each cluster, the pipeline performs two key steps: (1) Rapid Labeling: If 98% of the epochs within a cluster meet the criterion of having a standard deviation <0.75, the entire cluster is classified as `draining EVD'. Otherwise, the pipeline proceeds with further labeling steps. In the training data, ≈70– 80% of the epochs per EVD recording were rapidly labeled using this threshold, found by hours of manual verification. (2) Cluster Categorization: Clusters are categorized based on size. The top 10 largest clusters are labeled as `large clusters', clusters with fewer than 100 epochs are labeled as `small clusters', and the remaining clusters are labeled as `mid-size clusters'. Each category undergoes a specific sampling process as illustrated in the figure. (3) Label Propagation: The sampled data are then labeled…