OBJECTIVE: Intracranial pressure (ICP) pulse waveform analysis may provide valuable information about cerebrospinal pressure-volume compensation in patients with traumatic brain injury (TBI). The authors applied spectral methods to analyze ICP waveforms in terms of the pulse amplitude of ICP (AMP), high frequency centroid (HFC), and higher harmonics centroid (HHC) and also used a morphological classification approach to assess changes in the shape of ICP pulse waveforms using the pulse shape index (PSI). METHODS: The authors included 184 patients from the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) High-Resolution Sub-Study in the analysis. HFC was calculated as the average power-weighted frequency within the 4- to 15-Hz frequency range of the ICP power density spectrum. HHC was defined as the center of mass of the ICP pulse waveform harmonics from the 2nd to the 10th. PSI was defined as the weighted sum of artificial intelligence–based ICP pulse class numbers from 1 (normal pulse waveform) to 4 (pathological waveform). RESULTS: AMP and PSI increased linearly with mean ICP. HFC increased proportionally to ICP until the upper breakpoint (average ICP of 31 mm Hg), whereas HHC slightly increased with ICP and then decreased significantly when ICP exceeded 25 mm Hg. AMP (p < 0.001), HFC (p = 0.003), and PSI (p < 0.001) were significantly greater in patients who died than in patients who survived. Among those patients with low ICP (< 15 mm Hg), AMP, PSI, and HFC were greater in those with poor outcome than in those with good outcome (all p < 0.001). CONCLUSIONS: Whereas HFC, AMP, and PSI could be used as predictors of mortality, HHC may potentially serve as an early warning sign of intracranial hypertension. Elevated HFC, AMP, and PSI were associated with poor outcome in TBI patients with low ICP.

Objective. Mean intracranial pressure (ICP) is commonly used in the management of patients with intracranial pathologies. However, the shape of the ICP signal over a single cardiac cycle, called ICP pulse waveform, also contains information on the state of the craniospinal space. In this study we aimed to propose an end-to-end approach to classification of ICP waveforms and assess its potential clinical applicability. Methods. ICP pulse waveforms obtained from long-term ICP recordings of 50 neurointensive care unit (NICU) patients were manually classified into four classes ranging from normal to pathological. An additional class was introduced to simultaneously identify artifacts. Several deep learning models and data representations were evaluated. An independent testing dataset was used to assess the performance of final models. Occurrence of different waveform types was compared with the patients’ clinical outcome. Results. Residual Neural Network using 1-D ICP signal as input was identified as the best performing model with accuracy of 93% in the validation and 82% in the testing dataset. Patients with unfavorable outcome exhibited significantly lower incidence of normal waveforms compared to the favorable outcome group even at ICP levels below 20 mm Hg (median [first-third quartile]: 9 [1–36]% vs. 63 [52–88] %, p = 0.002). Conclusions. Results of this study confirm the possibility of analyzing ICP pulse waveform morphology in long-term recordings of NICU patients. Proposed approach could potentially be used to provide additional information on the state of patients with intracranial pathologies beyond mean ICP.

In this preliminary study we investigated the relationship between the time of cerebral desaturation episodes (CDEs), the severity of the haemorrhage, and the short-term outcome in patients with aneurysmal subarachnoid haemorrhage (aSAH). Thirty eight patents diagnosed with aneurysmal subarachnoid haemorrhage were analysed in this study. Regional cerebral oxygenation (rSO₂) was assessed using near infrared spectroscopy (NIRS). A CDE was defined as rSO₂ < 60% with a duration of at least 30 min. The severity of the aSAH was assessed using the Hunt and Hess scale and the short-term outcome was evaluated utilizing the Glasgow Outcome Scale. CDEs were found in 44% of the group. The total time of the CDEs and the time of the longest CDE on the contralateral side were longer in patients with severe versus moderate aSAH [h:min]: 8:15 (6:26–8:55) versus 1:24 (1:18–4:18), p = 0.038 and 2:05 (2:00–5:19) versus 0:48 (0:44–2:12), p = 0.038. The time of the longest CDE on the ipsilateral side was longer in patients with poor versus good short–term outcome [h:min]: 5:43 (3:05–9:36) versus 1:47 (0:42–2:10), p = 0.018. The logistic regression model for poor short-term outcome included median ABP, the extent of the haemorrhage in the Fisher scale and the time of the longest CDE. We have demonstrated that the time of a CDE is associated with the severity of haemorrhage and short-term outcome in aSAH patients. A NIRS measurement may provide valuable predictive information and could be considered as additional method of neuromonitoring of patients with aSAH.

Classic methods for assessing cerebral autoregulation involve a transfer function analysis performed using the Fourier transform to quantify relationship between fluctuations in arterial blood pressure (ABP) and cerebral blood flow velocity (CBFV). This approach usually assumes the signals and the system to be stationary. Such an presumption is restrictive and may lead to unreliable results. The aim of this study is to present an alternative method that accounts for intrinsic non-stationarity of cerebral autoregulation and the signals used for its assessment. Continuous recording of CBFV, ABP, ECG, and end-tidal CO₂ were performed in 50 young volunteers during normocapnia and hypercapnia. Hypercapnia served as a surrogate of the cerebral autoregulation impairment. Fluctuations in ABP, CBFV, and phase shift between them were tested for stationarity using sphericity based test. The Zhao-Atlas-Marks distribution was utilized to estimate the time—frequency coherence (TFCoh) and phase shift (TFPS) between ABP and CBFV in three frequency ranges: 0.02–0.07 Hz (VLF), 0.07–0.20 Hz (LF), and 0.20–0.35 Hz (HF). TFPS was estimated in regions locally validated by statistically justified value of TFCoh. The comparison of TFPS with spectral phase shift determined using transfer function approach was performed. The hypothesis of stationarity for ABP and CBFV fluctuations and the phase shift was rejected. Reduced TFPS was associated with hypercapnia in the VLF and the LF but not in the HF. Spectral phase shift was also decreased during hypercapnia in the VLF and the LF but increased in the HF. Time-frequency method led to lower dispersion of phase estimates than the spectral method, mainly during normocapnia in the VLF and the LF. The time—frequency method performed no worse than the classic one and yet may offer benefits from lower dispersion of phase shift as well as a more in-depth insight into the dynamic nature of cerebral autoregulation.