Neural network technique for detecting emergency states in neurosurgical patients

• Published in: Medical & biological engineering & computing Vol. 36; no. 6; pp. 717 - 722
• Main Authors: SWIERCZ, M, MARIAK, Z, LEWKO, J, CHOJNACKI, K, KOZLOWSKI, A, PIEKARSKI, P
• Format: Journal Article
• Language: English
• Published: Heidelberg Springer 01.11.1998; Springer Nature B.V
• Subjects: Biological and medical sciences; Brain Diseases - surgery; Critical Care - methods; Emergencies; Humans; Intracranial Pressure; Medical computing; Medical sciences; Monitoring, Physiologic - methods; Neural networks; Neural Networks (Computer); Neurosurgery; Patient monitoring; Postoperative Care - methods; Signal processing; Skull, brain, vascular surgery; Studies; Surgery (general aspects). Transplantations, organ and tissue grafts. Graft diseases; Time series analysis; Trends; Wavelet transforms; Human; Central nervous system; Intracranial pressure; Neural network; Statistical method; Biomedical data processing; Spectrum analysis; Surgery; Wavelet base; Classification; Technique; Emergency; Information interpretation
• ISSN: 0140-0118; 1741-0444
• DOI: 10.1007/BF02518874

The problem of reliable detection of life-threatening situations in the neurosurgical patient undergoing treatment in the ICU is still far from reaching a satisfactory solution, although several methods of clinical and instrumental evaluation have recently been developed for the early detection of oncoming signs of danger. Continuous monitoring of intracranial pressure (ICP) provides neurosurgeons with valuable information about the current condition of the patient. However, it is increasingly felt that traditional methods of extracting information from the ICP signal have reached their natural limits, mostly because of difficulties in fitting the appropriate mathematical model to this non-linear and non-stationary process. Successful implementations of artificial neural networks in many medical tasks have encouraged the application of this method of ICP processing. Two problems are considered: the prediction of trends in ICP, and recognition of the configuration of unfavourable symptoms likely to signal danger for the neurosurgical patient. The construction of neural network predictors of ICP trends is based on wavelet pre-processing of the original signal. The approach to the second task involves pre-processing of the ICP with spectral and statistical methods and classification of the extracted features of the current signal on an arbitrarily selected scale of danger.