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
翻译:COVID-19 影响全世界超过223个国家。 迫切需要的是非入侵性、低成本和高度可扩展的解决方案,以探测COVID-19-19, 特别是在没有无孔不入的PCR测试的低资源国家。 我们的目标是开发一个深度学习模型,使用普通大众通过个人设备自发提供的语音数据记录来识别COVID-19(录音和简短问卷)。 这项工作的新颖之处在于开发一个从语音录音中识别COVI-19-19病人的深层次学习模型。 方法:我们使用剑桥大学数据集,由893个音频样本组成,来自使用COVI-19声音测试的4352名参与者的人群来源。 声音特征是利用Mel-spectrogram分析来提取的。 根据语音数据,我们开发了深度学习分类模型,以检测积极的COVI-19案例。 这些模型包括长期短期内存储存储器(LSTM)和变压式诊断神经网络(CNN)的预测力和基线分类模型模型,即物流回归和支持VCentral Rest Real Stal 样。 结果:LMSM-S hal realalalalalalalalalalalalalalalalalalalalal disalalal 和89 的精确分析结果,以89 和精确度测算测算测测得的精确度测算结果,以89的精确度测算(89 和精确度测得的89 malisal-CMLMLMLMMMMLMLV) 和精确度测算结果,以89 和精确度测算法的精确度测算法的精确度测算法的精确度的精确度测算法的精确度分析结果。 。