Possibilities in the application of machine learning on bioimpedance time-series
et | 02 juil. 2019
À propos de cet article
Publié en ligne: 02 juil. 2019
Pages: 24 - 33
Reçu: 30 nov. 2018
DOI: https://doi.org/10.2478/joeb-2019-0004
Mots clés
© 2019 Tronstad, Strand-Amundsen, published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.
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List of all variables used for comparing different varieties of the bioimpedance input data and hyperparameters in the machine learning for prediction of ischemic duration.
| Variable | Description | Tested levels | Values |
|---|---|---|---|
| Measurement noise | Setting for simulated input data | 3 | 0, 10, 30 |
| Liver variance | Setting for simulated input data | 2 | 5, 20 |
| Drift | Setting for simulated input data | 3 | 0 ±50 +100 |
| Frequencies | Selection of input variables | 3 | {102 104 106}, 101:7, 101:0.1:7 |
| Sample size | Training and testing data size | 2 | 20, 100 |
| Regularization | Neural network hyperparameter | 3 | 10-1, 10-2, 10-3 |
| Hidden layer size | Neural network hyperparameter | 3 | 2, 5, 25 |
| Minibatch size | Neural network hyperparameter | 2 | 16, 32 |
| Epochs | Neural network hyperparameter | 2 | 250, 500 |
Comparison of prediction performance for the different ANN architectures in three different cases of difficulty based on the liver-to-liver variance, noise and drift in the measurement. The selection of input frequencies and hyperparameters for the best prediction performance of the different ANN architectures is provided in the rows below the prediction performances. The last row presents the best prediction performance when all 70 frequencies are used as input to the ANN. RMSEP=root mean square error of prediction, RMSEC=root mean square error of calibration, both having units of ischemia duration in hours.
| Case | Easy | Medium | Hard | ||||||
|---|---|---|---|---|---|---|---|---|---|
| Liver variance | |||||||||
| Noise | |||||||||
| Drift | |||||||||
| Drift direction | |||||||||
| Training examples | |||||||||
| FNN | LSTM | 2LSTM | FNN | LSTM | 2LSTM | FNN | LSTM | 2LSTM | |
| Mean RMSEP | |||||||||
| Std RMSEP | |||||||||
| Mean RMSEC | |||||||||
| Std RMSEC | |||||||||
| Frequencies | |||||||||
| Hidden layer size | |||||||||
| l2 regularization | |||||||||
| Training epochs | |||||||||
| Minibatch size | |||||||||
| Mean RMSEP (freq=70) | |||||||||