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.

中文翻译：

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院外心脏骤停调度中呼叫者情绪的早期识别：一种人工智能方法

目的

本研究旨在开发一种人工智能模型，通过处理调度通信的录音来检测院外心脏骤停呼叫期间呼叫者的情绪状态。

方法

检索了 2011 年 3 月至 4 月期间 337 次院外心脏骤停呼叫的录音。根据情绪内容和合作得分对呼叫者的情绪状态进行分类。梅尔频率倒谱系数从语音信号中提取基本信息。使用支持向量机进行自动判断，并应用重复随机子抽样交叉验证（RRS-CV）来评估稳健性。人工智能分类器的结果与专家评审员的共识进行了比较。

结果

录音分为五个情感内容和合作分数级别。该模型的平均阳性预测值为 72.97%，阴性预测值为 93.47%，敏感性为 38.76%，特异性为 98.29%。如果只考虑记录的前 10 秒，它的平均阳性预测值为 84.62%，阴性预测值为 93.57%，敏感性为 52.38%，特异性为 98.64%。对于情绪稳定的案例，人工智能模型的性能保持了较好的结果。

结论

人工智能模型可能有助于在调度对话期间判断呼叫者的情绪状态。通过预先筛选情绪稳定的来电者，该模型有可能在实践中得到利用，从而使调度员能够专注于被判断为情绪不稳定的案例。需要进一步的研究和验证来提高模型的性能并使其适用于一般人群。