Comparison between artificial neural network and multilinear regression models in an evaluation of cognitive workload in a flight simulator

Hannula, Manne; Huttunen, Kerttu; Koskelo, Jukka; Laitinen, Tomi; Leino, Tuomo

doi:10.1016/j.compbiomed.2008.09.007

Cited by 18 publications

(9 citation statements)

References 15 publications

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“…In all training sets and in most validation sets, accuracy, sensitivity, specificity, and AUROC were higher in the 1-, 3-, and 5-year survival models constructed by ANN than in those constructed by LR, which is consistent with other reports that ANN outperforms LR in both training [15, 31–35] and validation [14, 36, 37]. …”

Section: Discussionsupporting

confidence: 90%

Mortality Predicted Accuracy for Hepatocellular Carcinoma Patients with Hepatic Resection Using Artificial Neural Network

Chiu

Lee

et al. 2013

The Scientific World Journal

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The aim of this present study is firstly to compare significant predictors of mortality for hepatocellular carcinoma (HCC) patients undergoing resection between artificial neural network (ANN) and logistic regression (LR) models and secondly to evaluate the predictive accuracy of ANN and LR in different survival year estimation models. We constructed a prognostic model for 434 patients with 21 potential input variables by Cox regression model. Model performance was measured by numbers of significant predictors and predictive accuracy. The results indicated that ANN had double to triple numbers of significant predictors at 1-, 3-, and 5-year survival models as compared with LR models. Scores of accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC) of 1-, 3-, and 5-year survival estimation models using ANN were superior to those of LR in all the training sets and most of the validation sets. The study demonstrated that ANN not only had a great number of predictors of mortality variables but also provided accurate prediction, as compared with conventional methods. It is suggested that physicians consider using data mining methods as supplemental tools for clinical decision-making and prognostic evaluation.

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Section: Discussionsupporting

confidence: 90%

Mortality Predicted Accuracy for Hepatocellular Carcinoma Patients with Hepatic Resection Using Artificial Neural Network

Chiu

Lee

et al. 2013

The Scientific World Journal

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“…The most often and discussed in previous research are electrocardiogram (ECG), electroencephalogram (EEG), electrooculogram (EOG), electromyogram (EMG), electrodermal activity (EDA), blood pressure (BP), photoplethysmography (PPG), pupil diameter (PD), heart rate (HR), heart rate variability (HRV), skin temperature, and respiration belt 26–32 .…”

Section: Methodsmentioning

confidence: 99%

Assessment of ECG and respiration recordings from simulated emergency landings of ultra light aircraft

Bruna

Levora

Holub

2018

Sci Rep

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Pilots of ultra light aircraft have limited training resources, but with the use of low cost simulators it might be possible to train and test some parts of their training on the ground. The purpose of this paper is to examine possibility of stress inducement on a low cost flight simulator. Stress is assessed from electrocardiogram and respiration. Engine failure during flight served as a stress inducement stimuli. For one flight, pilots had access to an emergency navigation system. There were recorded some statistically significant changes in parameters regarding breathing frequency. Although no significant change was observed in ECG parameters, there appears to be an effect on respiration parameters. Physiological signals processed with analysis of variance suggest, that the moment of engine failure and approach for landing affected average breathing frequency. Presence of navigation interface does not appear to have a significant effect on pilots.

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“…Lenneman and Backs [12] and Mehler et al [13] have been represented that psycho physiological methods were more sensitive to initial changes in workload than performance-based methods. Psycho physiological methods are a non-invasive technique of characterizing relative MWL [14,15]. The techniques most often used in ergonomics applications are: cardiovascular activity (e.g., heart rate, heart rate variability, blood pressure, pulse wave velocity, and plethysmography), electroencephalography (EEG, event-related brain potentials, brain DC potentials, and event-related desynchronization), the electromyogram (to monitor the activity of specific muscles), skin activity (specific and non-specific responses with various methods), eye movements, body and skin temperature, and hormonal responses [16,17].…”

Section: Introductionmentioning

confidence: 99%

“…Almost all of this research has been conducted in Europe and North America countries or in highly industrialized countries such as Japan [18] but in our country there is a large gap to implement such researches. Also avoiding of mental overload is important, because it increases emotional stress and declines critical human decision-making processes [14]. When MWL increased the operators may demonstrate delayed information processing, or even not react at all to the received information, because the amount of information outstrips their capacity to process it [24].…”

Section: Introductionmentioning

confidence: 99%

Psycho Physiological and Subjective Responses to Mental Workload Levels during N-Back Task

Fallahi¹,

Heidarimoghadam²,

Motamedzade³

et al. 2016

J Ergonomics

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The present study investigated the effects of mental workload levels on psychophysiological and subjective responses during n-Back task. Heart rate, heart rate variability, shoulder muscle activity, EEG and EOG were measured during performing four mental tasks by 32 males. NASA-TLX completed at the end of each mental task. Using NASA-TLX the subjects stated that the task demands of very high mental task were higher than those of low, medium and high mental tasks. By increasing mental workload LF/HF ratio, shoulder muscle activity, eye activity and alpha activity significantly changed. It suggests that mentioned indices have enough sensitivity to quantify mental workload. Future studies should implement to determine the long-term effects such as cardiovascular and mental disorders in both males and females which experience different levels of mental workload in daily working condition. To get better results suggested those parameters such as: cultural differences, anthropometric data, body mass index, alertness, shift work and menstrual cycle take into account.

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Comparison between artificial neural network and multilinear regression models in an evaluation of cognitive workload in a flight simulator

Cited by 18 publications

References 15 publications

Mortality Predicted Accuracy for Hepatocellular Carcinoma Patients with Hepatic Resection Using Artificial Neural Network

Mortality Predicted Accuracy for Hepatocellular Carcinoma Patients with Hepatic Resection Using Artificial Neural Network

Assessment of ECG and respiration recordings from simulated emergency landings of ultra light aircraft

Psycho Physiological and Subjective Responses to Mental Workload Levels during N-Back Task

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