Bayesian Neural Networks for Uncertainty-aware Tissue Identification using Bioimpedance-based Frequency Response Similarity Metrics.

This work sought to improve bioimpedance-based tissue identification model performance on an eight-tissue dataset by employing frequency response function similarity metrics as feature generators and Bayesian neural networks for accurate uncertainty estimations. Inspired by structural dynamics, frequency response function similarity metrics were applied in a new context to generate features from mean baseline measurements, thereby extracting information of how unclassified measurements compare to previous ones. Additionally, Bayesian neural networks were constructed to directly estimate model parameter uncertainty effects on classification uncertainty. Finally, a stacking ensemble technique combined base model outputs to train a meta-learner for improving performance. Models trained with similarity metric features achieved higher mean accuracies and better tissue specific F1-scores than those trained with measurements. Bayesian neural networks with temperature scaling reduced the expected calibration error of the standard feedforward networks by 19% to 83%, indicating significant enhancement of uncertainty quantification. Ensembles achieved higher mean accuracies than base models, with maximum accuracies over 75%, and maintained the enhanced uncertainty quantification. The implementation of similarity metric inputs and Bayesian neural networks for bioimpedance-based tissue identification offered a clear improvement in mean accuracy and uncertainty quantification over traditional models using measurements only. This marks an essential step towards enabling bioimpedance as a feasible sensing option for real-time tissue identification. Attaining dependable bioimpedance-based tissue identification will provide a foundation for new technologies due to economical and implementation advantages over competing methods while the added uncertainty-awareness makes it an excellent candidate for medical applications because it can provide additional context for outputs to users.