Soichi Ogishima
Biographic Data
| ID | 1920775 |
|---|---|
| NAME | Soichi Ogishima |
| GIVEN NAMES | Soichi |
| FAMILY NAME | Ogishima |
| SIGNATURE | OGISHIMA S |
| AFFILIATIONS | Tohoku Medical Megabank Organization |
| ORCID | 0000-0001-8613-2562 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 2 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2022 |
| LATEST PUBLICATION YEAR | 2023 |
| H-INDEX | 0 |
Comprehensive evaluation of machine learning algorithms for predicting sleep–wake conditions and differentiating between the wake conditions before and after sleep during pregnancy based on heart rate…
In the test for predicting three types of sleep-wake conditions, most of the algorithms, except for Naïve Bayes, showed higher areas under the curve (AUCs; 0.82-0.88) and accuracy (0.78-0.81). The test using four types of sleep-wake conditions with differentiation between the wake conditions before and after sleep also resulted in successful prediction by the gated recurrent unit with the highest AUC (0.86) and accuracy (0.79). Among the nine fea…
Heart Rate Information-Based Machine Learning Prediction of Emotions Among Pregnant Women
In this study, the extent to which different emotions of pregnant women can be predicted based on heart rate-relevant information as indicators of autonomic nervous system functioning was explored using various machine learning algorithms. Nine heart rate-relevant autonomic system indicators, including the coefficient of variation R-R interval (CVRR), standard deviation of all NN intervals (SDNN), and square root of the mean squared differences o…
No prominent works on this page.
Heart Rate Information-Based Machine Learning Prediction of Emotions Among Pregnant Women
In this study, the extent to which different emotions of pregnant women can be predicted based on heart rate-relevant information as indicators of autonomic nervous system functioning was explored using various machine learning algorithms. Nine heart rate-relevant autonomic system indicators, including the coefficient of variation R-R interval (CVRR), standard deviation of all NN intervals (SDNN), and square root of the mean squared differences o…
Comprehensive evaluation of machine learning algorithms for predicting sleep–wake conditions and differentiating between the wake conditions before and after sleep during pregnancy based on heart rate…
In the test for predicting three types of sleep-wake conditions, most of the algorithms, except for Naïve Bayes, showed higher areas under the curve (AUCs; 0.82-0.88) and accuracy (0.78-0.81). The test using four types of sleep-wake conditions with differentiation between the wake conditions before and after sleep also resulted in successful prediction by the gated recurrent unit with the highest AUC (0.86) and accuracy (0.79). Among the nine fea…
Artificial Intelligence (2 works) · Computer Science (2 works) · Heart rate (2 works) · Heart rate variability (2 works) · Heart Rate Variability and Autonomic Control (2 works) · Internal Medicine (2 works) · Machine learning (2 works) · Medicine (2 works) · Neonatal and fetal brain pathology (2 works) · Anxiety (1 work)