{"id":29384,"date":"2024-01-09T12:53:20","date_gmt":"2024-01-09T11:53:20","guid":{"rendered":"https:\/\/www.cosinuss.com\/?p=29384"},"modified":"2024-01-11T10:16:10","modified_gmt":"2024-01-11T09:16:10","slug":"advancing-covid-19-patient-monitoring-reliable-hrv-analysis-from-ppg-data","status":"publish","type":"post","link":"https:\/\/www.cosinuss.com\/en\/2024\/01\/09\/advancing-covid-19-patient-monitoring-reliable-hrv-analysis-from-ppg-data\/","title":{"rendered":"Advancing COVID-19 Patient Monitoring: Reliable HRV Analysis from PPG Data"},"content":{"rendered":"

Master\u2019s thesis by Sophie Wagner: Advancing Clinical Patient Monitoring: Robust RR Interval Extraction and Heart Rate Variability Analysis from Continuous Photoplethysmography Data in COVID-19 Patients
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This thesis showcases the viability of obtaining dependable long-term HRV data using a cosinuss\u00b0 in-ear PPG sensor. A thorough examination of the DiAssCo<\/a> data set was carried out, which includes 145 patients who were hospitalized with COVID-19 at the Gro\u00dfhadern Clinic in Munich in the first half of 2021. All patients were monitored continuously with the cosinuss\u00b0 Two, covering a wide range of disease severities and outcomes.<\/p>\n

As HRV integration in long-term recordings is not trivial, the research contributes to understanding HRV’s potential in healthcare and emphasizes the importance of diligent data cleaning methodologies. To gain insights into COVID-19 disease progression, a machine learning algorithm was successfully employed to predict health outcomes, independent of the subjects\u2019 age.<\/p>\n

As a scientific consensus regarding the most descriptive HRV parameters in the context of COVID-19 has yet to be established, this thesis presents feature rankings to indicate parameters that are associated with adverse outcomes.<\/p>\n<\/div><\/div><\/div><\/div><\/div>\n

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