Publications by authors named "Chao-Chen Chen"
Article Synopsis
- The review aimed to assess which EEG-based machine learning models, specifically Random Forest and Convolutional Neural Networks, had the highest effectiveness in predicting neurologic outcomes after cardiac arrest.
- It involved a systematic search of medical and engineering literature, identifying 17 relevant studies and extracting key EEG features used in these models.
- The results showed that Random Forest had an AUC range of 0.8 to 0.97 and was the most common conventional ML model, while combining EEG features with electronic health record data could enhance predictive performance.
Comparing different wavelet transforms on removing electrocardiogram baseline wanders and special trends.
BMC Med Inform Decis Mak
December 2020
Background: Electrocardiogram (ECG) signal, an important indicator for heart problems, is commonly corrupted by a low-frequency baseline wander (BW) artifact, which may cause interpretation difficulty or inaccurate analysis. Unlike current state-of-the-art approach using band-pass filters, wavelet transforms can accurately capture both time and frequency information of a signal. However, extant literature is limited in applying wavelet transforms (WTs) for baseline wander removal.
View Article and Find Full Text PDFThe present study evaluated the ability of humans to discriminate temperature decreases in the noxious and innocuous cold range. Two groups of five subjects detected changes in cold stimuli applied to the maxillary face. For five subjects, adapting temperatures of 22 degrees, 16 degrees, 6 degrees and 0 degrees C were used, and thresholds for detecting temperature decreases were determined using an adaptive psychophysical paradigm.
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