LEAP LaboratoryDepartment of Electrical EngineeringIndian Institute of Science Bengaluru 560012 India.
P. D. Hinduja National Hospital and Medical Research Center Mumbai 400016 India.
IEEE J Transl Eng Health Med. 2023 Mar 8;11:199-210. doi: 10.1109/JTEHM.2023.3250700. eCollection 2023.
The COVID-19 pandemic has highlighted the need to invent alternative respiratory health diagnosis methodologies which provide improvement with respect to time, cost, physical distancing and detection performance. In this context, identifying acoustic bio-markers of respiratory diseases has received renewed interest.
In this paper, we aim to design COVID-19 diagnostics based on analyzing the acoustics and symptoms data. Towards this, the data is composed of cough, breathing, and speech signals, and health symptoms record, collected using a web-application over a period of twenty months.
We investigate the use of time-frequency features for acoustic signals and binary features for encoding different health symptoms. We experiment with use of classifiers like logistic regression, support vector machines and long-short term memory (LSTM) network models on the acoustic data, while decision tree models are proposed for the symptoms data.
We show that a multi-modal integration of inference from different acoustic signal categories and symptoms achieves an area-under-curve (AUC) of 96.3%, a statistically significant improvement when compared against any individual modality ([Formula: see text]). Experimentation with different feature representations suggests that the mel-spectrogram acoustic features performs relatively better across the three kinds of acoustic signals. Further, a score analysis with data recorded from newer SARS-CoV-2 variants highlights the generalization ability of the proposed diagnostic approach for COVID-19 detection.
The proposed method shows a promising direction for COVID-19 detection using a multi-modal dataset, while generalizing to new COVID variants.
新冠疫情凸显了发明替代呼吸健康诊断方法的必要性，这些方法在时间、成本、物理隔离和检测性能方面都有所改进。在这种情况下，识别呼吸疾病的声学生物标志物重新引起了人们的兴趣。
本文旨在基于分析声学和症状数据来设计 COVID-19 诊断方法。为此，数据由咳嗽、呼吸和语音信号以及使用网络应用程序在二十个月期间收集的健康症状记录组成。
我们研究了声学信号的时频特征和不同健康症状的二进制特征的使用。我们在声学数据上尝试使用逻辑回归、支持向量机和长短时记忆（LSTM）网络模型等分类器，同时为症状数据提出了决策树模型。
我们表明，不同声学信号类别和症状的推理的多模态集成可实现曲线下面积（AUC）为 96.3%，与任何单一模态相比，这是一个具有统计学意义的改进([Formula: see text])。不同特征表示的实验表明，梅尔频谱声学特征在三种声学信号中表现相对更好。此外，使用从新 SARS-CoV-2 变体记录的数据进行的评分分析突出了所提出的诊断方法在 COVID-19 检测中的泛化能力。
该方法使用多模态数据集显示出 COVID-19 检测的有前途的方向，同时也适用于新的 COVID 变体。
