Abstract
Exposure to high levels of hyperbaric oxygen (\(\hbox {HBO}_{2}\)) can induce central nervous system oxygen toxicity in humans and animals, a phenomenon characterized by repeated tonic–clonic seizures. The risk of developing this type of convulsions represents the limiting factor in using \(\hbox {HBO}_{2}\) for a number of clinical and diving applications. Previously, using radio-telemetry in awake rats, a significant increase in the mean value of the ventilatory responses in rats to \(\hbox {HBO}_{2}\) approximately 5–8 min before onset of seizures has been observed. This response has been termed “hyperoxic hyperpnea,” and it has been hypothesized it may serve as a predictor of an impending seizure while breathing \(\hbox {HBO}_{2}\). The purpose of the present study was to apply soft computing methods to determine whether there is a direct correlation between the onset of hyperoxic hyperpnea (i.e., the ventilatory response as defined by tidal volume and respiratory frequency) and the onset of seizure. In our experiments, we used Multilayer Perceptron, Naive Bayes, J48, and U-BRAIN aimed at evidencing the correlation between the respiratory feature vectors and the onset of an impending seizure in unanesthetized, freely behaving rats breathing 4, 5 or 6 atmospheres absolute (ATA) of oxygen. This strategy was also aimed to finding a set of rules relating physiological parameters and convulsive phenomena through inductive inference.
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References
Augusteijn M, Folkert B (2002) Neural network classification and novelty detection. Int J Remote Sens 23:2891–2902
Balentine J (1982) Pathology of oxygen toxicity. Academic Press, New York
Blogg SL, Gennser M, Loveman GA, Seddon FM, Thacker JC et al (2003) The effect of breathing hyperoxic gas during simulated submarine escape on venous gas emboli and decompression illness. Undersea Hyperb Med 30:163–174
Christ M, von Auenmueller KI, Brand M, Amirie S, Sasko BM, Trappe H-J (2016) Hyperoxia early after hospital admission in comatose patients with non-traumatic out-of-hospital cardiac arrest. Med Sci Monit 22:3296–3300
D’Angelo G, Rampone S (2014) Towards a HPC-oriented parallel implementation of a learning algorithm for bioinformatics applications. BMC Bioinform 15(Suppl 5):S2
D’Angelo G, Rampone S (2016) Feature extraction and soft computing methods for aerospace structure defect classification. Measurement 85:192–209
D’Angelo G, Palmieri F, Ficco M, Rampone S (2015) An uncertainty-managing batch relevance-based approach to network anomaly detection. Appl Soft Comput 35:408–418
Davis J, Goadrich M (2006) The relationship between precision-recall and ROC curves. In: Proceedings of the twenty-third international conference on machine learning (ICML’06). ACM, Pittsburgh, Pennsylvania, USA, pp 233–240
Dell’Anna AM, Lamanna I, Vincent J-L, Taccone FS (2014) How much oxygen in adult cardiac arrest? Crit Care 18:555–561
Fock A, Millar I (2008) Oxygen toxicity in recreational and technical diving. Diving Hyperb Med 38:86–90
Fukunaga K (1990) Introduction to statistical pattern recognition. Academic Press, London
Goldhahn RT Jr (1977) Scuba diving deaths: a review and approach for the pathologist. Leg Med Annu 1976:109–132
John GH (1996) Robust linear discriminant trees. Springer, Berlin
John GH, Langley P (1995) Estimating continuous distributions in Bayesian classifiers. In: Eleventh conference on uncertainty in artificial intelligence. San Mateo, Morgan Kaufmann
Kulikovsky M, Gil T, Mettanes I, Karmeli R, Har-Shai Y (2009) Hyperbaric oxygen therapy for non-healing wounds. Isr Med Assoc J 11:480–485
Mahon RT, Dainer HM, Gibellato MG, Soutiere SE (2009) Short oxygen prebreathe periods reduce or prevent severe decompression sickness in a 70-kg swine saturation model. J Appl Physiol 106:1459–1463
Mendelson E (1997) Introduction to mathematical logic. Chapman & Hall, London
Pilla R, Landon CS, Dean JB (2013) A potential early physiological marker for CNS oxygen toxicity: hyperoxic hyperpnea precedes seizure in unanesthetized rats breathing hyperbaric oxygen. J Appl Physiol (1985) 114:1009–1020
Rampone S (1998) Recognition of splice-junctions on DNA Sequences by BRAIN learning algorithm. Bioinform J 14(8):676–684
Rampone S, Russo C (2012) A fuzzified BRAIN algorithm for learning DNF from incomplete data. Electron J Appl Stat Anal 5:256–270
Rampone S, Pierro V, Troiano L, Pinto IM (2013) Neural network aided glitch-burst discrimination and glitch classification. Int J Mod Phys C 24(11):1350084
Sandroni C, D’Arrigo S (2014) Management of oxygen and carbon dioxide pressure after cardiac arrest. Minerva Anestesiologica 80(10):1105–1114
Sumathi S, Sivanandam SM (2006) Introduction to data mining and its applications. Springer, Berlin
Wilson JA, Williams JC (2009) Massively parallel signal processing using the graphics processing unit for real-time brain–computer interface feature extraction. Front Neuroeng 2(11):1–13
Acknowledgements
This research was supported by University of Sannio. We wish to thank the Office of Naval Research, Undersea Medicine Program.
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Communicated by V. Loia.
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D’Angelo, G., Pilla, R., Dean, J.B. et al. Toward a soft computing-based correlation between oxygen toxicity seizures and hyperoxic hyperpnea. Soft Comput 22, 2421–2427 (2018). https://doi.org/10.1007/s00500-017-2512-z
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DOI: https://doi.org/10.1007/s00500-017-2512-z