Deep learning approach for automatic assessment of schizophrenia and bipolar disorder in patients using R-R intervals
Fig 9
The overview of R-R values and predictions of XGBoost, Ensemble of SVMs, and GRU + FCN classifiers for consecutive time windows for the two individuals.
Green areas correspond to the correctly classified periods, while red areas refer to the opposite case. (a) corresponds to the control group individual for whom selected contiguous time windows were classified incorrectly. All three tested methods made errors for periods corresponding to lower R-R interval lengths, while for the remaining time windows, the Ensemble of SVMs achieved the highest performance. (b) depicts the individual from the treatment group whose signal is demanding for classifiers. Only the middle part of the measurements mostly led to the correct labelling (except for GRU + FCN, making many prediction errors but still less than for the remaining parts of the signal).