论文标题
开发一个多变量的预测模型，以从众包呼吸道数据中检测Covid-19
Developing a multi-variate prediction model for the detection of COVID-19 from Crowd-sourced Respiratory Voice Data
论文作者
论文摘要
Covid-19在全球范围内影响了223多个国家。迫切需要非侵入性，低成本和高度可扩展的解决方案来检测COVID-19，尤其是在PCR测试不普遍的低资源国家中。我们的目的是开发一个深度学习模型，使用一般人群（语音录音和简短问卷）通过其个人设备自发提供的语音数据记录来识别Covid-19。这项工作的新颖性在于开发一种深度学习模型，以鉴定来自语音记录的199名患者。方法：我们使用了由893个音频样本组成的剑桥大学数据集，该数据集由4352名参与者的人群提供，这些参与者使用了Covid-19 Sounds应用程序。使用MEL光谱图分析提取语音特征。根据语音数据，我们开发了深度学习分类模型，以检测阳性的Covid-19情况。这些模型包括长期术语记忆（LSTM）和卷积神经网络（CNN）。我们将它们的预测能力与基线分类模型进行了比较，即逻辑回归和支持向量机。结果：LSTM基于MEL频率CEPSTRAL系数（MFCC）功能的功能最高的精度（89％），其灵敏度和特异性分别为89％和89％，与拟议模型相比，与Covid-19诊断的预测准确性相比，与ART的结果相比，该模型的预测准确性显着提高。结论：深度学习可以检测到199例患者的声音的细微变化，并有令人鼓舞的结果。作为当前测试技术的补充，该模型可以通过简单的语音分析帮助卫生专业人员快速诊断和追踪Covid-19案件
COVID-19 has affected more than 223 countries worldwide. There is a pressing need for non invasive, low costs and highly scalable solutions to detect COVID-19, especially in low-resource countries where PCR testing is not ubiquitously available. Our aim is to develop a deep learning model identifying COVID-19 using voice data recordings spontaneously provided by the general population (voice recordings and a short questionnaire) via their personal devices. The novelty of this work is in the development of a deep learning model for the identification of COVID-19 patients from voice recordings. Methods: We used the Cambridge University dataset consisting of 893 audio samples, crowd-sourced from 4352 participants that used a COVID-19 Sounds app. Voice features were extracted using a Mel-spectrogram analysis. Based on the voice data, we developed deep learning classification models to detect positive COVID-19 cases. These models included Long-Short Term Memory (LSTM) and Convolutional Neural Network (CNN). We compared their predictive power to baseline classification models, namely Logistic Regression and Support Vector Machine. Results: LSTM based on a Mel-frequency cepstral coefficients (MFCC) features achieved the highest accuracy (89%,) with a sensitivity and specificity of respectively 89% and 89%, The results achieved with the proposed model suggest a significant improvement in the prediction accuracy of COVID-19 diagnosis compared to the results obtained in the state of the art. Conclusion: Deep learning can detect subtle changes in the voice of COVID-19 patients with promising results. As an addition to the current testing techniques this model may aid health professionals in fast diagnosis and tracing of COVID-19 cases using simple voice analysis
