Intracranial hypertension is a key factor in the treatment and prevention of secondary brain injury in patients with traumatic brain injury. We aimed to develop a prediction model based on changes in intracranial pressure waveform morphology. A convolutional neural network with 10 hidden layers was trained on the dominant intracranial pressure waveform, computed over 1 minute of data, from control and pre-intracranial hypertension segments up to 1 hour prior to intracranial hypertension. The model obtained an accuracy, sensitivity, specificity and an area under the receiver operating characteristics curve of 0.70, 0.68, 0.72 and 0.74, respectively, for the time window 0-10 minutes before the onset of intracranial hypertension.