Md-Billal Hossain , Kia Golzari , Youngsun Kong , Bruce J. Derrick , Richard E. Moon , Michael J. Natoli , M. Claire Ellis , Christopher Winstead-Derlega , Sara I. Gonzalez , Christopher M. Allen , Mathew S. Makowski , Brian M. Keuski , John J. Freiberger , Hugo F. Posada-Quintero , Ki H. Chon
{"title":"利用皮电活动和机器学习预测中枢神经系统氧中毒症状","authors":"Md-Billal Hossain , Kia Golzari , Youngsun Kong , Bruce J. Derrick , Richard E. Moon , Michael J. Natoli , M. Claire Ellis , Christopher Winstead-Derlega , Sara I. Gonzalez , Christopher M. Allen , Mathew S. Makowski , Brian M. Keuski , John J. Freiberger , Hugo F. Posada-Quintero , Ki H. Chon","doi":"10.1016/j.bbe.2024.03.004","DOIUrl":null,"url":null,"abstract":"<div><h3>Objective</h3><p>Breathing elevated oxygen partial pressures (PO<sub>2</sub>) prior to SCUBA diving increases the risk of developing central nervous system oxygen toxicity (CNS-OT), which could impair performance or result in seizure and subsequent drowning. We aimed to study the dynamics of electrodermal activity (EDA) while breathing elevated PO<sub>2</sub> in the hyperbaric environment (HBO<sub>2</sub>) as a possible means to predict impending CNS-OT. To this end, we used machine learning to automatically detect and predict the onset of symptoms associated with CNS-OT in humans by using features derived from EDA in both time and frequency domains.</p></div><div><h3>Methods</h3><p>We collected electrodermal activity (EDA) data from forty-nine exposures to HBO<sub>2</sub> while subjects were undergoing cognitive load and exercise in a hyperbaric oxygen chamber. Four independent experts were present during the experiment to monitor and classify any symptoms associated with hyperbaric oxygen toxicity. We computed a highly sensitive time varying spectral EDA index, named TVSymp, and extracted informative features from skin conductance responses (SCRs). Machine learning algorithms were trained and validated for classifying features from SCRs and TVSymp as CNS-OT related or non-CNS-OT related. Machine learning models were validated using a subject-independent leave one subject out (LOSO) validation scheme.</p></div><div><h3>Results</h3><p>Our machine learning model was able to classify EDA dynamics related to CNS-OT with 100 % sensitivity and 84 % specificity via LOSO validation. Moreover, the median prediction time for CNS-OT symptoms was ∼ 250 s preceding the occurrence of actual symptoms.</p></div><div><h3>Significance</h3><p>This study shows that EDA can potentially be used for early prediction of CNS-OT in divers with a high sensitivity and sufficient prediction time for countermeasures. While the study results are promising, independent validation datasets are warranted to confirm the findings. However, the current results are well corroborated in an animal study, which consistently showed seizure prediction time of 2 min prior to seizure.</p></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":5.3000,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0208521624000226/pdfft?md5=6ad239b693b9abf97054efb9220f787c&pid=1-s2.0-S0208521624000226-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Prediction of central nervous system oxygen toxicity symptoms using electrodermal activity and machine learning\",\"authors\":\"Md-Billal Hossain , Kia Golzari , Youngsun Kong , Bruce J. Derrick , Richard E. Moon , Michael J. Natoli , M. Claire Ellis , Christopher Winstead-Derlega , Sara I. Gonzalez , Christopher M. Allen , Mathew S. Makowski , Brian M. Keuski , John J. Freiberger , Hugo F. Posada-Quintero , Ki H. Chon\",\"doi\":\"10.1016/j.bbe.2024.03.004\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Objective</h3><p>Breathing elevated oxygen partial pressures (PO<sub>2</sub>) prior to SCUBA diving increases the risk of developing central nervous system oxygen toxicity (CNS-OT), which could impair performance or result in seizure and subsequent drowning. We aimed to study the dynamics of electrodermal activity (EDA) while breathing elevated PO<sub>2</sub> in the hyperbaric environment (HBO<sub>2</sub>) as a possible means to predict impending CNS-OT. To this end, we used machine learning to automatically detect and predict the onset of symptoms associated with CNS-OT in humans by using features derived from EDA in both time and frequency domains.</p></div><div><h3>Methods</h3><p>We collected electrodermal activity (EDA) data from forty-nine exposures to HBO<sub>2</sub> while subjects were undergoing cognitive load and exercise in a hyperbaric oxygen chamber. Four independent experts were present during the experiment to monitor and classify any symptoms associated with hyperbaric oxygen toxicity. We computed a highly sensitive time varying spectral EDA index, named TVSymp, and extracted informative features from skin conductance responses (SCRs). Machine learning algorithms were trained and validated for classifying features from SCRs and TVSymp as CNS-OT related or non-CNS-OT related. Machine learning models were validated using a subject-independent leave one subject out (LOSO) validation scheme.</p></div><div><h3>Results</h3><p>Our machine learning model was able to classify EDA dynamics related to CNS-OT with 100 % sensitivity and 84 % specificity via LOSO validation. Moreover, the median prediction time for CNS-OT symptoms was ∼ 250 s preceding the occurrence of actual symptoms.</p></div><div><h3>Significance</h3><p>This study shows that EDA can potentially be used for early prediction of CNS-OT in divers with a high sensitivity and sufficient prediction time for countermeasures. While the study results are promising, independent validation datasets are warranted to confirm the findings. 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Prediction of central nervous system oxygen toxicity symptoms using electrodermal activity and machine learning
Objective
Breathing elevated oxygen partial pressures (PO2) prior to SCUBA diving increases the risk of developing central nervous system oxygen toxicity (CNS-OT), which could impair performance or result in seizure and subsequent drowning. We aimed to study the dynamics of electrodermal activity (EDA) while breathing elevated PO2 in the hyperbaric environment (HBO2) as a possible means to predict impending CNS-OT. To this end, we used machine learning to automatically detect and predict the onset of symptoms associated with CNS-OT in humans by using features derived from EDA in both time and frequency domains.
Methods
We collected electrodermal activity (EDA) data from forty-nine exposures to HBO2 while subjects were undergoing cognitive load and exercise in a hyperbaric oxygen chamber. Four independent experts were present during the experiment to monitor and classify any symptoms associated with hyperbaric oxygen toxicity. We computed a highly sensitive time varying spectral EDA index, named TVSymp, and extracted informative features from skin conductance responses (SCRs). Machine learning algorithms were trained and validated for classifying features from SCRs and TVSymp as CNS-OT related or non-CNS-OT related. Machine learning models were validated using a subject-independent leave one subject out (LOSO) validation scheme.
Results
Our machine learning model was able to classify EDA dynamics related to CNS-OT with 100 % sensitivity and 84 % specificity via LOSO validation. Moreover, the median prediction time for CNS-OT symptoms was ∼ 250 s preceding the occurrence of actual symptoms.
Significance
This study shows that EDA can potentially be used for early prediction of CNS-OT in divers with a high sensitivity and sufficient prediction time for countermeasures. While the study results are promising, independent validation datasets are warranted to confirm the findings. However, the current results are well corroborated in an animal study, which consistently showed seizure prediction time of 2 min prior to seizure.
期刊介绍：
Biocybernetics and Biomedical Engineering is a quarterly journal, founded in 1981, devoted to publishing the results of original, innovative and creative research investigations in the field of Biocybernetics and biomedical engineering, which bridges mathematical, physical, chemical and engineering methods and technology to analyse physiological processes in living organisms as well as to develop methods, devices and systems used in biology and medicine, mainly in medical diagnosis, monitoring systems and therapy. The Journal''s mission is to advance scientific discovery into new or improved standards of care, and promotion a wide-ranging exchange between science and its application to humans.
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