Machine-learning based feature selection for a non-invasive breathing change detection

Figures

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  Fig. 1. Example of pressure signal recorded with TeleOxⓇ. Window of 45 seconds of nasal pressure signal from a healthy subject recording

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  Fig. 2. Full pressure signal TeleOxⓇ recordings for two healthy subjects. Dark gray areas correspond to the 3 minutes of exercising while the light gray areas correspond to breathing while drinking, coughing, speaking and oral breathing. Subjects were at rest in other areas

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  Fig. 3. Pressure signal and oxygen flow for 8-hours recording from COPD patient. Gray areas correspond to estimated exercise times

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  Fig. 4. Extracted features example from a healthy subject recording. a Raw pressure signal, b breathing rate, signal amplitude and ARIMA coefficients and c Fourier transform. In a and b, dark gray areas correspond to the 3 minutes of exercising while the light gray areas correspond to breathing while drinking, coughing, speaking and mouth breathing

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  Fig. 5. Extracted features example from a COPD patient recording. a Raw pressure signal, b breathing rate, signal amplitude and ARIMA coefficients and c. Fourier transform. In a and b, dark gray areas correspond to estimated exercise times

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  Fig. 6. ROC curves for the detection of exercise periods in the supervised context using combinations of the proposed features. a Healthy subjects and b Patients with COPD

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  Fig. 7. ROC curves for the detection of exercise periods in the one-class context using combinations of the proposed features a Healthy subjects and b Patients with COPD

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  Fig. 8. Example of Mahalanobis distances from reference points. In the one-class context, distances between each new measure x and the all reference points are given by the Mahalanobis distance, considering the reference’s mean and variance-covariance matrix