BACKGROUND: One of the key issues in electroencephalogram (EEG) monitoring is accurate signal acquisition with less cumbersome electrodes. In this study, the L2 phase electro-deposited nanoporous platinum (L2-ePt) electrode is introduced, which is a new type of electrode that utilizes a stable nanoporous platinum surface to reduce the skin-electrode impedance. METHODS: L2-ePt electrodes were fabricated using electro-deposition technique. Then, the effect of the nanoporous surface on the surface roughness and the electrode impedance were observed from the L2-ePt electrodes and the flat platinum (FlatPt) electrode. The skin-electrode impedances of the L2-ePt electrodes, a gold cup electrode, and the FlatPt electrode were evaluated when placed on the hairy occipital area of the head in ten subjects. For the validation of using the L2-ePt electrode, a correlational analysis of the alpha rhythms was performed in the same subjects for simultaneous EEG recordings using the L2-ePt and clinically-used EEG electrodes. RESULTS: The results indicated that the L2-ePt electrode with a roughness factor of 200 had the lowest mean impedance performance. Moreover, the proposed L2-ePt electrode showed a significantly lower mean skin-electrode impedance than the FlatPt electrode. Finally, the EEG signal quality recorded by the L2-ePt electrode (r = 0.94) was comparable to that of the clinically-used gold cup electrode. CONCLUSION: Based on these results, the proposed L2-ePt electrode is suitable for use in various high-quality EEG applications.
Alpha Rhythm ; Electric Impedance ; Electrodes* ; Electroencephalography* ; Head ; Platinum*

Objectives: . Because climatic and air-pollution factors are known to influence the occurrence of respiratory diseases, we used these factors to develop machine learning models for predicting the occurrence of respiratory diseases. Methods: . We obtained the daily number of respiratory disease patients in Seoul. We used climatic and air-pollution factors to predict the daily number of patients treated for respiratory diseases per 10,000 inhabitants. We applied the relief-based feature selection algorithm to evaluate the importance of feature selection. We used the gradient boosting and Gaussian process regression (GPR) methods, respectively, to develop two different prediction models. We also employed the holdout cross-validation method, in which 75% of the data was used to train the model, and the remaining 25% was used to test the trained model. We determined the estimated number of respiratory disease patients by applying the developed prediction models to the test set. To evaluate the performance of each model, we calculated the coefficient of determination (R2) and the root mean square error (RMSE) between the original and estimated numbers of respiratory disease patients. We used the Shapley Additive exPlanations (SHAP) approach to interpret the estimated output of each machine learning model. Results: . Features with negative weights in the relief-based algorithm were excluded. When applying gradient boosting to unseen test data, R2 and RMSE were 0.68 and 13.8, respectively. For GPR, the R2 and RMSE were 0.67 and 13.9, respectively. SHAP analysis showed that reductions in average temperature, daylight duration, average humidity, sulfur dioxide (SO2), total solar insolation amount, and temperature difference increased the number of respiratory disease patients, whereas increases in atmospheric pressure, carbon monoxide (CO), and particulate matter ≤2.5 μm in aerodynamic diameter (PM2.5) increased the number of respiratory disease patients. Conclusion . We successfully developed models for predicting the occurrence of respiratory diseases using climatic and air-pollution factors. These models could evolve into public warning systems.