Table 4. Summary table of the current state-of-art works in COVID-19 detection using machine learning and breathing/coughing recordings.

Study	Year	Recordings Source	Respiratory Sound	Number of Subjects	Number of Recordings	Pre-processing Steps	Trained Model	Performance
Bagad et al [70]	2020	Smartphone app	Cough	2001 COVID-19 1620 healthy	3621	Short-term magnitude spectrogram	Convolutional neural network (ResNet-18)	Accuracy: Not applicable AUC: 0.72
Laguarta et al. [39]	2020	Web-based	Cough	2660 COVID-19 2660 healthy	5320	Mel-frequency cepstral coefficients (MFCC)	Convolutional neural network (ResNet-50)	Accuracy: 97.10%
Mohammed et al. [71]	2021	Web-based	Cough	114 COVID-19 1388 healthy	1502	Spectrogram Mel spectrum, power spectrum Tonal spectrum, chroma spectrum Raw signals Mel-frequency cepstral coefficients (MFCC)	Ensemble convolutional neural network	Accuracy: 77.00%
Lella et al. [69]	2021	Web-based smartphone app	Cough voice breathing	5000-6000 Subjects 300 COVID-19	6000	De-noising auto encoder (DAE) Gamm-atone frequency cepstral coefficients (GFCC) Improved Mel-frequency cepstral coefficients (IMFCC)	Convolutional neural network	Accuracy: 95.45%
Sait et al. [68]	2021	Electronic stethoscope	Breathing	5 COVID-19 5 healthy	10	Two-dimensional (2D) Fourier transformation	Convolutional neural network (Inception-v3)	Accuracy: 80.00%
Manshouri et al. [72]	2021	Web-based	Cough	7 COVID-19 9 Healthy	16	Mel-frequency cepstral coefficients (MFCC) Single-time Fourier transformation	Support vector machine (SVM)	Accuracy: 94.21%
This study	2021	Smartphone app	Shallow/deep breathing	120 COVID-19 120 healthy	Total: 480 Shallow: 240 Deep: 240	Raw signals Mel-frequency cepstral coefficients (MFCC)	Convolutional neural network Bi-directional long short-term memory (CNN-BiLSTM) + Hand-crafted features	Accuracy: Shallow = 94.58% Accuracy: Deep = 92.08%