A non-parametric method is presented for modelling nonlinear dynamic mechanisms of respiratory sinus arrhythmia (RSA) in anesthesia caused by positive pressure ventilation. RR interval sequences are shown with Tsay's linearity test to contain both short-term and long-term nonlinear components, which cannot completely be modelled with optimal linear methods. The nonlinear approach is based on Wiener's theory for broad-band random input signal. The input-output model is formed for tracheal pressure and RR interval sequence. Second-order and third-order nonlinearities in RSA fluctuation are found and demonstrated.
Despite promising results reported in the literature for mental workload assessment using electroencephalography (EEG), most of the proposed methods rely on employing multiple EEG channels, limiting their practicality. However, the advent of wearable EEG technology provides the possibility of mental workload assessment for real-life applications. Yet, a few studies that considered consumer-oriented EEG headsets for mental workload assessment only used a single database for validating the proposed methods, overlooking the potential for portability. In this research, we studied 60 recordings of participants playing a three-level n-back game, utilizing data from two EEG devices, Enobio and Muse, with distinctive characteristics such as sampling rate and channel configuration. Following the denoising of the EEG signals, we segmented the signals and applied the discrete wavelet transform to decompose them into sub-bands. Then, we extracted Shannon entropy and wavelet log energy features from all sub-bands. Subsequently, we fed the extracted features into five classifiers: support vector machine, k-nearest neighbors, multi-layer perceptron, AdaBoost, and the transformer network. In comparing the results across all classifiers, the transformer network demonstrated superiority by achieving highest mean accuracy for Database M (88%) and Database E (85%). Given the consistent outcomes achieved with the transformer network classifier across both databases and utilizing a three-level n-back game, our findings indicate that the proposed method holds promise for real-life applications.
Objectives : Commercial systems for monitoring the depth of anesthesia (DoA) are often financially inaccessible to developing countries. As an alternative, a wearable single frontal electroencephalogram (EEG) device can be utilized. Nonetheless, most studies addressing DoA monitoring utilizing just one frontal EEG channel rely on nonlinear features that require parameter tuning before computation, overlooking the potential interchangeability of such features across different databases. Methods : Here, we present a parameter-free feature set for DoA monitoring using a single frontal EEG channel and evaluate its performance on two databases with different characteristics. First, the EEG signal is de-noised and split into its sub-bands. Second, several parameter-free features based on entropy, power and frequency, fractal, and variation are extracted from all sub-bands. Finally, the distinguished features are chosen and input into a random forest regressor to estimate the DoA index values. Results : The reliability of the proposed feature set for the DoA monitoring is indicated by achieving a comparable correlation coefficient of 0.80 and 0.79 and mean absolute error of 7.1 and 9.0 between the reference and estimated DoA index values for Databases I and II, respectively. Significance : The obtained results from this study confirm the possibility of affordable DoA monitoring using a portable EEG system. Given its simplicity and comparable results for both databases, the proposed feature set holds promise for practical application in real-world scenarios.
Feasibility of Random Forest and Support Vector Machine classifiers is tested for the discrimination of 7 types of vegetation near lake Poosjärvi in Western Finland. Four sets of features grouped as basic, textural, ICA or PCA based, and rotational features are applied. The results indicate that the Random Forest classification scheme outperforms the Support Vector Machine classifier. For both classifiers the textural features improve the performance significantly when added to the basic feature set while the ICA, PCA or rotational features have little effect. The best total classification accuracy of 87.5 % was obtained when all the considered feature sets were combined and fed to the Random Forest classifier.
Crop lodging is surveyed from different image sources and from the crop yield map. Crop lodging has effect on remotely sensed and field level measurements when indirectly measuring, for example, soil variation, nutrients, or crop condition. The interpretation of results may be incorrect especially in the case when it is not possible to detect lodging from the analyzed dataset. Such situation may arise, for example, when analyzing Sentinel-2 (S2) images or crop yield monitor data. In this study crop lodging is inspected from UAS-based or-thophoto mosaic taken 50 meters above the ground level, S2 image and crop yield monitor connected to a combine harvester.
The effects of microwave radiation on the electrical activity of human brain have been studied on a small group of young healthy volunteers. For estimation of the location of possible effects, the following EEG channels were utilized: F3, F4, T3, T4, O1, O2. The experiments included repeated microwave radiation at 450 MHz with modulating frequency 7 Hz and 16 Hz photic stimulation. For comparing the brain activity in different situations, three parameters from the EEG bispectrum were calculated. The results indicate that those parameters could not detect the influence of very low microwave radiation. However, the first parameter named normalized alpha could detect the onset and the end of photic stimulation in occipital region and the onset of photic stimulation in the frontal and temporal region. As far as microwave radiation is concerned, those parameters should be analyzed again while more intense fields are studied.
Treatment of patients suffering from severe traumatic brain injury (TBI) commonly involves sedation and mechanical ventilation during prolonged stay in the intensive care unit. Continuous EEG is often monitored in these patients to detect epileptic seizures. It has also been suggested that EEG has prognostic value regarding the outcome of the treatment. In this study the ability of 186 qEEG features to predict the outcome of the treatment of TBI patients is assessed. The features are based on the power spectrum of the EEG. The data underlying the study contains long term (over 24 h) recordings from 20 patients treated in the postoperative intensive care unit of the North Estonian Medical Center. 12 qEEG features were found to have predictive value when evaluated by calculating the area under the receiver operating curve constructed from feature probabilities.
