Early recognition of a caller's emotion in out-of-hospital cardiac arrest dispatching: An artificial intelligence approach

Aim: This study aimed to develop an AI model for detecting a caller's emotional state during out-of-hospital cardiac arrest calls by processing audio recordings of dispatch communications.

Methods: Audio recordings of 337 out-of-hospital cardiac arrest calls from March-April 2011 were retrieved. The callers' emotional state was classified based on the emotional content and cooperative scores. Mel-frequency cepstral coefficients extracted essential information from the voice signals. A support vector machine was utilised for the automatic judgement, and repeated random sub-sampling cross validation (RRS-CV) was applied to evaluate robustness. The results from the artificial intelligence classifier were compared with the consensus of expert reviewers.

Results: The audio recordings were classified into five emotional content and cooperative score levels. The proposed model had an average positive predictive value of 72.97%, a negative predictive value of 93.47%, sensitivity of 38.76%, and specificity of 98.29%. If only the first 10 seconds of the recordings were considered, it had an average positive predictive value of 84.62%, a negative predictive value of 93.57%, sensitivity of 52.38%, and specificity of 98.64%. The artificial intelligence model's performance maintained preferable results for emotionally stable cases.

Conclusion: Artificial intelligence models can possibly facilitate the judgement of callers' emotional states during dispatch conversations. This model has the potential to be utilised in practice, by pre-screening emotionally stable callers, thus allowing dispatchers to focus on cases that are judged to be emotionally unstable. Further research and validation are required to improve the model's performance and make it suitable for the general population.