ABSTRACT
Long-COVID or post-COVID is a phenomenon where people who have recovered from the COVID-19, suffer persisting symptoms for more than 4 weeks after the confirmed case of COVID-19 and they can last for months. Approximately 20% of the people affected by this Coronavirus disease (COVID-19) are suffering from mid and long term effects known as the Long COVID and it can affect multiple organs in the body and this can lead to death. To date, different studies and researches have been undertaken to understand about the Long COVID and make robust estimates on the predicting factors, symptoms and also to assess the various long term effects on the patients affected by it. Based on the available research articles and the papers published in mainstream journals on Long COVID, this survey paper aims at analyzing various methods and Machine learning models used to detect and predict Long COVID, to help clinicians and researchers working on early diagnosis of Long COVID. © 2022 IEEE.
ABSTRACT
The COVID-19 pandemic has brought about an unprecedented shift towards Telehealth since physicians are overwhelmed by the huge patient load in hospitals. This has forced policy makers to advise home quarantine for mild and moderate COVID patients. Additionally, even non-COVID patients with diabetes and cardiovascular or pulmonary diseases who do not need hospitalization are currently being monitored at home for any changes in their severity that may require a home to hospital transfer. Our research team has developed an Internet of Medical Things wearable Heart Lung Health monitor for patients with cardiovascular and pulmonary risk factors so as to enable hospitals to remotely track patient health status. Our system consists of a credit-card sized wearable 3-lead ECG device interfaced with smartphone that analyzes ECG and extracts heart and respiratory parameters, and transmits these to a dashboard for remote monitoring. We present the architecture, device, respiratory rate extraction algorithm, and its validation on 50 patients. Encouraged by these results we are readying deployment of our system for home monitoring of at-risk patients.
ABSTRACT
COVID-19 pandemic has challenged the capabilities of hospital healthcare delivery systems worldwide. Among patients admitted in hospitals, sudden severity deterioration leading to out-of-ICU ward crashes are observed in many care areas. During the current pandemic, the major gap in the timely identification of COVID patient deterioration is due to the isolation precautions precluding continuous patient monitoring in wards. To address this challenge, we developed and deployed a wearable IoT integrated system called Remote Early Warning of Out-of-ICU Crashes (REWOC in short), which consists of wearable devices at the patient end and early warning score integrated dashboards for physicians and nurses to monitor patients remotely. We describe the architecture and design of REWOC as well as our deployment experience of REWOC on COVID patients in a large hospital in India. To our knowledge, this is one of the first reports of a real-world deployment using wearable devices for monitoring out-of-ICU ward crashes among COVID patients. © 2021 IEEE.