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Peak detection in intracranial pressure signal waveforms: a comparative study.
BACKGROUND: The monitoring and analysis of quasi-periodic biological signals such as electrocardiography (ECG), intracranial pressure (ICP), and cerebral blood flow velocity (CBFV) waveforms plays an important role in the early detection of adverse patient events and contributes to improved care management in the intensive care unit (ICU). This work quantitatively evaluates existing computational frameworks for automatically extracting peaks within ICP waveforms. METHODS: Peak detection techniques based on state-of-the-art machine learning models were evaluated in terms of robustness to varying noise levels. The evaluation was performed on a dataset of ICP signals assembled from 700 h of monitoring from 64 neurosurgical patients. The groundtruth of the peak locations was established manually on a subset of 13, 611 pulses. Additional evaluation was performed using a simulated dataset of ICP with controlled temporal dynamics and noise. RESULTS: The quantitative analysis of peak detection algorithms applied to individual waveforms indicates that most techniques provide acceptable accuracy with a mean absolute error (MAE) ≤ 10 ms without noise. In the presence of a higher noise level, however, only kernel spectral regression and random forest remain below that error threshold while the performance of other techniques deteriorates. Our experiments also demonstrated that tracking methods such as Bayesian inference and long short-term memory (LSTM) can be applied continuously and provide additional robustness in situations where single pulse analysis methods fail, such as missing data. CONCLUSION: While machine learning-based peak detection methods require manually labeled data for training, these models outperform conventional signal processing ones based on handcrafted rules and should be considered for peak detection in modern frameworks. In particular, peak tracking methods that incorporate temporal information between successive periods of the signals have demonstrated in our experiments to provide more robustness to noise and temporary artifacts that commonly arise as part of the monitoring setup in the clinical setting
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NON-INVASIVE MONITORING OF INTRACRANIAL PRESSURE USING TRANSCRANIAL DOPPLER ULTRASONOGRAPHY
Intracranial pressure (ICP) is an important monitoring modality in the clinical management of several neurological diseases carrying the risk of fatal intracranial hypertension. However, this parameter is not always considered due to its invasive assessment. In this scenario, a non-invasive estimation of ICP (nICP) may be essential, and indeed it has become a Holy Grail in Clinical Neurosciences: extensively searched, albeit never found. This thesis is devoted to the assessment, applications and development of transcranial Doppler (TCD)-based non-invasive methods for ICP and cerebral perfusion pressure (CPP) monitoring.
The thesis is divided into three sections: I) The accuracy of existing TCD-based nICP estimators in various scenarios of varying ICP (traumatic brain injury, rise of ICP during plateau waves, and rise in ICP induced by infusion of cerebrospinal fluid during infusion test). The estimators of nICP consisted of a mathematical black box model, methods based on non-invasive CPP, and a method based on TCD pulsatility index. II) The feasibility of the best performing nICP estimator in clinical practice, including patients with closed TBI and brain midline shift, patients with acute liver failure during liver transplant surgery, and patients during non-neurosurgical surgery in the beach chair position. III) The description and assessment of a novel methodology for non-invasive assessment of cerebral perfusion pressure (nCPP) based on spectral arterial blood volume accounting.
As main results, TCD-based non-invasive methods could replicate changes in direct ICP across time confidently, and could provide reasonable accuracy in comparison to the standard invasive techniques. Furthermore, in feasibility studies, nICP in association with other TCD physiological parameters provided a comprehensive interpretation of cerebral haemodynamics in conditions presenting impairment of cerebral blood flow circulation. The new method of nCPP estimation could identify changes in CPP across time reliably in conditions of decreasing and increasing CPP.
These findings support the use of TCD-based nICP methods in a variety of clinical conditions requiring management of ICP and brain perfusion. More importantly, the low costs associated with nICP methods, since TCD is a widely available medical device, could contribute to its widespread use as a reliable alternative for ICP monitoring in everyday clinical practice.Cambridge Commonwealth European and Internation Trus