End-to-End Automatic Morphological Classification of Intracranial Pressure Pulse Waveforms Using Deep Learning


Objective. The mean intracranial pressure, also known as ICP, is a measurement that is frequently used in the clinical care of patients who have intracranial pathologies. On the other hand, the form that the ICP signal takes over the course of a single cardiac cycle, which is referred to as the ICP pulse waveform, also contains information on the condition of the craniospinal space. In this study, our objectives were to (1) propose an end-to-end approach to classifying ICP waveforms and (2) evaluate the potential clinical applicability of such an approach. Methods. ICP pulse waveforms that were obtained from long-term recordings of ICP taken from fifty patients who were being treated in a neurointensive care unit (NICU) were manually categorized into four classes, ranging from normal to pathological. For the purpose of simultaneously identifying artifacts, an additional class was developed. A number of different Deep Learning models and representations of the data were examined. For the purpose of evaluating the effectiveness of the final models, an external testing dataset was utilized. The occurrence of various waveform types was analyzed in conjunction with the clinical outcome of the patients. Results. With an accuracy of 93% in the validation dataset and 82% in the testing dataset, the Residual Neural Network that used the 1-D ICP signal as input was discovered to be the model that performed the best overall. Even at ICP levels lower than 20 mm Hg, patients who had an unfavorable outcome had a significantly lower incidence of normal waveforms when compared to patients who had a favorable outcome (median [first-third quartile]: 9 [1–36]% vs. 63 [52–88]%, p = 0.002) Patients who had a favorable outcome had a significantly higher incidence of normal waveforms (median [first-third quartile]: Conclusions. The findings of this study provide evidence that long-term recordings of NICU patients offer the opportunity to conduct an analysis of the morphology of the ICP pulse waveform. The proposed method has the potential to be utilized in order to supply additional information on the condition of patients suffering from intracranial pathologies in addition to the mean ICP.

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