Optimized SNR-based ECAP threshold determination is comparable to the judgement of human evaluators

In cochlear implant (CI) users, measurements of electrically evoked compound action potentials (ECAPs) prove the functionality of the neuron-electrode interface. Objective measures, e.g., the ECAP threshold, may serve as a basis for the clinical adjustment of the device for the optimal benefit of the CI user. As for many neural responses, the threshold determination often is based on the subjective assessment of the clinical specialist, whose decision-making process could be aided by autonomous computational algorithms. To that end, we extended the signal-to-noise ratio (SNR) approach for ECAP threshold determination to be applicable for FineGrain (FG) ECAP responses. The new approach takes advantage of two features: the FG stimulation paradigm with its enhanced resolution of recordings, and SNR-based ECAP threshold determination, which allows defining thresholds independently of morphology and with comparably low computational power. Pearson’s correlation coefficient r between the ECAP threshold determined by five experienced evaluators and the threshold determined with the FG-SNR algorithm was in the range of r = 0.78–0.93. Between evaluators, r was in a comparable range of 0.84–0.93. A subset of the parameters of the algorithm was varied to identify the parameters with the highest potential to improve the FG-SNR formalism in the future. The two steps with the strongest influence on the agreement between the threshold estimate of the evaluators and the algorithm were the removal of undesired frequency components (denoising of the response traces) and the exact determination of the two time windows (signal and noise and noise only).”The parameters were linked to the properties of an ECAP response, indicating how to adjust the algorithm for the automatic detection of other neurophysiological responses.

Improved DEMATEL-ISM integration approach for complex systems

Purpose Decision Making Trial and Evaluation Laboratory (DEMATEL) and Interpretive Structural Modeling (ISM) are commonly used separately, but also may be combined per their common characteristics to identify causal relationships and hierarchical structure among factors in complex systems with a relatively small computational burden. The purpose of this study is to establish an improved DEMATEL-ISM integration approach to remedy the disadvantages of the traditional DEMATEL-ISM integration method. A case study was conducted to compare the proposed improved integration approach against the traditional integration method, and to validate its feasibility and effectiveness. Methods The proposed improved DEMATEL-ISM integration approach has two main parts: a threshold determination via maximum mean de-entropy (MMDE) method and an additional transitivity check process. The factors influencing China’s rural-urban floating population’s willingness to participate in social insurance was analyzed as a case study. Results The traditional and improved methods show notable differences in the hierarchical factor structure and the inner influence relationship among factors that they respectively reveal. The traditional integration approach results in some irrationality, while the improved approach does not. Originality This study confirms the importance of proper threshold determination and reachability matrix transitivity checking during DEMATEL-ISM integration. The improved approach includes a scientific threshold determination method based on the MMDE method, plus a transitivity check of the reachability matrix with necessary corrections to ensure its soundness. It can be straightforwardly operated at a relatively low computational burden while providing accurate analysis results.

Threshold-Enhanced PROMETHEE Group Decision Support under Uncertainties

As threshold determination is arduous when using PROMETHEE (Preference Ranking Organization METHod for Enrichment Evaluations) to make decisions, this study proposes a comprehensive PROMETHEE-based group decision support for covering the uncertainties of threshold determination, risk preferences, and the evaluation itself. To reduce the difficulty on threshold determination, it explicitly offers statistical aggregation of individuals’ indifference and preference thresholds so as to obtain benefits from multiple sources of knowledge and experience. Three typical combinations are characterized that reflect conservative, balanced, and aggressive group preferences. We also derive 6 properties to illustrate the effects of threshold changes on preference changes. Despite variances among the individual rankings of alternatives, the rank differences within the group decisions do converge by the illustrative example. A larger interval of a conservative group preference generates more diverse ranks versus the other two group preferences. Moreover, PROMETHEE III has a power on managing inaccurate measurement. The introduction of S-shaped functions has the benefit of fitting behavioral decision making under uncertainty. Integrating these approaches together for a decision support, our proposal is less affected by rank variance and coherent with traditional group PROMETHEE under differentiated decision power.