Early recognition of a caller’s emotion in out-of-hospital cardiac arrest dispatching: An artificial intelligence approach
| Autor: | Tzu-Chun Hsieh, Matthew Huei-Ming Ma, Hao-Yang Lin, Jen-Tung Sun, Kuan-Chen Chin, Wen-Chu Chiang, Albert Y. Chen, Yu-Chun Chien, Ming-Ju Hsieh, Chi-Wei Yang |
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| Rok vydání: | 2021 |
| Předmět: |
education.field_of_study
Mel scale business.industry Emergency Medical Service Communication Systems Emotions Judgement Population Emergency Nursing Cardiopulmonary Resuscitation Cross-validation Support vector machine Artificial Intelligence Classifier (linguistics) Emergency Medicine Emergency medical dispatch Humans Medicine Mel-frequency cepstrum Artificial intelligence Cardiology and Cardiovascular Medicine business education Out-of-Hospital Cardiac Arrest |
| Zdroj: | Resuscitation. 167:144-150 |
| ISSN: | 0300-9572 |
| DOI: | 10.1016/j.resuscitation.2021.08.032 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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