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researchsquare; 2022.


Representation of one-dimensional (1D) signals as surfaces and higher dimensional manifolds reveals geometric structures that can enhance assessment of signal similarity and classification of large sets of signals. We therefore represent 1D signals as surface objects embedded in higher dimensional Euclidean or other spaces. Specifically, since we are concerned with audio signals, the spectrogram is utilized for the representation of the 1D signals by surfaces, but a handful of other representations in combined spaces such as wavelets can be utilized for this purpose. A novel class of geometric features is then extracted by parameterizing the surfaces, and by utilizing distortion measures that are defined with reference to them. This yields a set of highly descriptive features that are instrumental in our approach to feature engineering, analysis and classification of audio or other 1D signals. Two examples of audio signals were selected to illustrate applications and the capabilities of the new approach: Lung sounds were chosen in view of the interest nowadays in respiratory pathologies caused by the Corona virus and environmental problems; Accent detection was selected as an example of a challenging speech problem. The proposed approach outperformed baseline models under all measured metrics. Our novel approach to 1D signal representation and processing can be further extended considering higher dimensional distortion measures.

medrxiv; 2021.
Preprint in English | medRxiv | ID: ppzbmed-10.1101.2021.09.26.21264135


BackgroundNon-invasive oxygen saturation (SpO2) measurement is a central vital sign that supports the management of COVID-19 patients. However, reports on SpO2 characteristics (patterns and dynamics) are scarce and none, to our knowledge, has analysed high resolution continuous SpO2 in COVID-19. MethodsSpO2 signal sampled at 1Hz and clinical data were collected from COVID-19 departments at the Rambam Health Care Campus (Haifa, Israel) between May 1st, 2020 and February 1st, 2021. Data from a total of 367 COVID-19 patients, totalling 27K hours of continuous SpO2 recording, could be retrieved, including 205 non-critical and 162 critical cases. Desaturations based on different SpO2 threshold definitions and oximetry derived digital biomarkers (OBMs) were extracted and compared across severity and support levels. FindingsAn absolute SpO2 threshold at 93% was the most efficient in discriminating between critical and non-critical patients without support or under oxygen support. Under no support, the non-critical group depicted a fold change (FC) of 1 {middle dot}8 times more frequent desaturations compared to the critical group. However, the hypoxic burden was 1 {middle dot}6 times more important in critical versus non-critical patients. Other OBMs depicted significant differences, notably the percentage of time below 93% SpO2 (CT93) was the most discriminating OBM. Mechanical ventilation depicted a strong effect on SpO2 by significantly reducing the frequency (1 {middle dot}85 FC) and depth (1 {middle dot}21 FC) of desaturations. OBMs related to periodicity and hypoxic burden were markedly affected up to several hours before the initiation of the mechanical ventilation. InterpretationThis is the first report investigating continuous SpO2 measurements in hospitalized patients affected with COVID-19. SpO2 characteristics differ between critical and non-critical patients and are impacted by the level of support. OBMs from high resolution SpO2 signal may enable to anticipate clinically relevant events, monitoring of treatment response and may be indicative of future deterioration. FundingThe Milner Foundation, The Placide Nicod fundation and the Technion Machine Learning and Intelligent Systems center (MLIS).

COVID-19 , Hypoxia