Objective dyspnea evaluation on COVID-19 patients learning from exertion-induced dyspnea scores
Objective: Dyspnea is one of the most common symptoms for many pulmonary diseases including COVID-19. Clinical assessment of dyspnea is mainly performed by subjective self-report, which has limited accuracy and is challenging for continuous monitoring. The objective of this research study is to determine if dyspnea progression in COVID patients can be assessed using a non-invasive wearable sensor and if the findings are comparable to a learning model of physiologically induced dyspnea on healthy subjects. Methods: Non-invasive wearable respiratory sensors were employed to retrieve continuous respiratory characteristics with user comfort and convenience. Overnight (~16h) respiratory waveforms were collected on 12 COVID-19 patients, and a benchmark on 13 healthy subjects with exertion-induced dyspnea were also performed for blind comparison. The learning model was built from the respiratory features with self report on 32 healthy subjects under exertion and airway blockage. Results: High similarity between dyspnea on COVID patients and physiologically induced dyspnea on healthy subjects was established. COVID patients have consistently high objective dyspnea scores in comparison with normal breathing of healthy subjects. We also exhibited continuous dyspnea scoring capability for 12-16 hours on patients. Conclusion: This paper validates the viability to use our objective dyspnea scoring for clinical dyspnea assessment on COVID patients. Significance: The proposed system can help the identification of dyspneic exacerbation in conditions such as COVID, leading to early intervention and possibly improving their outcome. This approach can be potentially applied to other pulmonary disorders such as asthma, emphysema, and pneumonia.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Predicting Outcomes in Long COVID Patients with Spatiotemporal Attention
Long COVID is a general term of post-acute sequelae of COVID-19. Patients with long COVID can endure long-lasting symptoms including fatigue, headache, dyspnea and anosmia, etc. Identifying the cohorts with severe long-t…
Feature ImportanceExtracting Angina Symptoms from Clinical Notes Using Pre-Trained Transformer Architectures
Anginal symptoms can connote increased cardiac risk and a need for change in cardiovascular management. This study evaluated the potential to extract these symptoms from physician notes using the Bidirectional Encoder fr…
Language ModelingLanguage ModellingManagementSpecificityPredicting Risk of Pulmonary Fibrosis Formation in PASC Patients
While the acute phase of the COVID-19 pandemic has subsided, its long-term effects persist through Post-Acute Sequelae of COVID-19 (PASC), commonly known as Long COVID. There remains substantial uncertainty regarding bot…
Prediction Model for Mortality Analysis of Pregnant Women Affected With COVID-19
COVID-19 pandemic is an ongoing global pandemic which has caused unprecedented disruptions in the public health sector and global economy. The virus, SARS-CoV-2 is responsible for the rapid transmission of coronavirus di…
Policy Learning for Social Robot-Led Physiotherapy
Social robots offer a promising solution for autonomously guiding patients through physiotherapy exercise sessions, but effective deployment requires advanced decision-making to adapt to patient needs. A key challenge is…
Reinforcement Learning