Introduction Coronavirus disease 2019 (COVID-19) primarily affects the pulmonary system and presents itself as shortness of breath, fever, and cough. However, it may affect other systems as well, including the nervous system. This study aimed to determine the frequency of neurological symptoms in COVID-19 patients and its association with the severity of the disease. Methods This cross-sectional study was conducted at a public sector tertiary care teaching hospital in Karachi, Pakistan, from April to July 2020. All patients with positive polymerase chain reaction (PCR) tests were included, except those with pre-existing neurological and psychiatric conditions. Results The most common neurological symptom was dizziness (17.5%), followed by headache (15.7%). Three (2.6%) patients had a stroke. Nine (7.8%) participants had a taste impairment, and another nine (7.8%) had a smell impairment. There was no significant difference in the frequency of neurological symptoms when severe and non-severe disease was compared. Conclusion Neurological symptoms are frequent in COVID-19. Care should be taken to identify them early. COVID-19 should be suspected in patients presenting with neurological abnormalities and should be included in the differential diagnosis to prevent further virus transmission.
The COVID-19 pandemic has affected more than 100 million people worldwide, with around 500,000 cases reported daily. This has led to the overwhelming of healthcare systems even in developed countries such as the US, UK, etc. Remote monitoring of COVID-19 patients with non-serious symptoms can help reduce the burden on healthcare facilities and make them available for high risk groups and the seriously affected. The pandemic has accelerated the demand for the remote patient monitoring (RPM) technologies, and the market is expected to reach 2.14 billion in 2027 from the value of 786.4 million in 2019. In RPM programs, there are two types of sensors that can be used: wearable and contactless. The former, which is currently more widely used, is not only more obtrusive and uncomfortable, but can also lead to cross-infection through patient contact. These two types of technologies are discussed and compared for each vital sign. In the respiratory system, the vital signs are the respiratory rate (RR) and oxygen saturation (SpO2), while for the latter, they are the heart rate/rhythm and the blood pressure (BP). Then, the discussion is broadened to policy level changes needed to expedite the use of such technologies for remote patient monitoring (RPM) in the world. Around 80% of countries' RPM programs are either informal or in a pilot phase, and thus lack policies and an established regulatory framework to implement their programs. The various policies needed to initiate, deliver, and reimburse RPM programs during emergency situations and outbreaks are discussed. Finally, technologies such as contactless systems, robotics, and Internet-of-things (IoT) that will revolutionize healthcare in the future by reducing the interaction between physicians and patients and cross-infection are discussed.
The spread of COVID-19, which has infected over 10 million people worldwide, entails the need for fast and aggressive testing never like before. As countries look to expanding testing, such test solutions must not only be technically sound, but should also be feasible and convenient for the user. The aim of this paper is to review the emerging tests and technology which can be potentially used to detect and assess the condition of those infected with the Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and the challenges in their development and use. The paper deals with 1) In vitro diagnostic tests(IVDs),i.e tests that use biological samples like blood and consist of 2 types: nucleic acid tests, which detect the RNA of the virus, and antibody tests which antibodies created by the body in response to the virus. 2) Chest X-Ray and CT scan devices, associated Deep Learning based detection methods and portable devices. 3) Wearable sensors, IoT and telemedicine for remote monitoring of COVID-19 patients to assess their condition, and also of Non-COVID-19 ones to reduce risks of cross-infection.
Human Posture Classification(HPC) is used in many fields such as as human computer interfacing, security surveillance, rehabilitation, remote monitoring, and so on. This paper compares the performance of different classifiers in the detection of 3 postures, sitting, standing, and lying down, which was recorded using Microsoft Kinect cameras. The Machine Learning classifiers used included the Support Vector Classifier, Naive Bayes, Logistic Regression, K-Nearest Neighbours, and Random Forests. The Deep Learning ones included the standard Multi-Layer Perceptron, Convolutional Neural Networks(CNN), and Long Short Term Memory Networks(LSTM). It was observed that Deep Learning methods outperformed the former and that the one-dimensional CNN performed the best with an accuracy of 93.45%.
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