Acceptability of and symptom findings from an online symptom check-in tool for COVID-19 outpatient follow-up among a predominantly healthcare worker population.

INTRODUCTION: Health systems worldwide have had to prepare for a surge in volume in both the outpatient and inpatient settings since the emergence of COVID-19. Early international healthcare experiences showed approximately 80% of patients with COVID-19 had mild disease and therfore could be managed as outpatients. However, SARS-CoV-2 can cause a biphasic illness with those affected experiencing a clinical deterioration usually seen after day 4 of illness. OBJECTIVE: We created an online tool with the primary objective of allowing for virtual disease triage among the increasing number of outpatients diagnosed with COVID-19 at our hospital. Secondary aims included COVID-19 education and the promotion of official COVID-19 information among these outpatients, and analysis of reported symptomatology. METHODS: Outpatients with acute COVID-19 disease received text messages from the hospital containing a link to an online symptom check-in tool which they were invited to complete. RESULTS: 296 unique participants (72%) from 413 contacted by text completed the online check-in tool at least once, generating 831 responses from 1324 texts sent. 83% of text recipients and 91% of unique participants were healthcare workers. 7% of responses to the tool were from participants who admitted to a slight worsening of their symptoms during follow-up. Fatigue was the most commonly reported symptom overall (79%), followed by headache (72%). Fatigue, headache and myalgia were the most frequently reported symptoms in the first 3 days of illness. 8% of responses generated in the first 7 days of illness did not report any of the cardinal symptoms (fever, cough, dyspnoea, taste/smell disturbance) of COVID-19. Participants found the tool to be useful and easy to use, describing it as 'helpful' and 'reassuring' in a follow-up feedback survey (n=140). 93% said they would use such a tool in the future. 39% reported ongoing fatigue, 16% reported ongoing smell disturbance and 14% reported ongoing dyspnoea after 6 months. CONCLUSION: The online symptom check-in tool was found to be acceptable to participants and saw high levels of engagement and satisfaction. Symptomatology findings highlight the variety and persistence of symptoms experienced by those with confirmed COVID-19 disease.

Multimodal detection of head-movement artefacts in EEG.

Artefacts arising from head movements have been a considerable obstacle in the deployment of automatic event detection systems in ambulatory EEG. Recently, gyroscopes have been identified as a useful modality for providing complementary information to the head movement artefact detection task. In this work, a comprehensive data fusion analysis is conducted to investigate how EEG and gyroscope signals can be most effectively combined to provide a more accurate detection of head-movement artefacts in the EEG. To this end, several methods of combining these physiological and physical signals at the feature, decision and score fusion levels are examined. Results show that combination at the feature, score and decision levels is successful in improving classifier performance when compared to individual EEG or gyroscope classifiers, thus confirming that EEG and gyroscope signals carry complementary information regarding the detection of head-movement artefacts in the EEG. Feature fusion and the score fusion using the sum-rule provided the greatest improvement in artefact detection. By extending multimodal head-movement artefact detection to the score and decision fusion domains, it is possible to implement multimodal artefact detection in environments where gyroscope signals are intermittently available.

Automatic detection of EEG artefacts arising from head movements using EEG and gyroscope signals.

Contamination of EEG signals by artefacts arising from head movements has been a serious obstacle in the deployment of automatic neurological event detection systems in ambulatory EEG. In this paper, we present work on categorizing these head-movement artefacts as one distinct class and on using support vector machines to automatically detect their presence. The use of additional physical signals in detecting head-movement artefacts is also investigated by means of support vector machines classifiers implemented with gyroscope waveforms. Finally, the combination of features extracted from EEG and gyroscope signals is explored in order to design an algorithm which incorporates both physical and physiological signals in accurately detecting artefacts arising from head-movements.

Automatic detection of EEG artefacts arising from head movements.

The need for reliable detection of artefacts in raw and processed EEG is widely acknowledged. In this paper, we present the results of an investigation into appropriate features for artefact detection in the REACT ambulatory EEG system. The study focuses on EEG artefacts arising from head movement. The use of one generalised movement artefact class to detect movement artefacts is proposed. Temporal, frequency, and entropy-based features are evaluated using Kolmogorov-Smirnov and Wilcoxon rank-sum non-parametric tests, Mutual Information Evaluation Function and Linear Discriminant Analysis. Results indicate good separation between normal EEG and artefacts arising from head movement, providing a strong argument for treating these head movement artefacts as one generalised class rather than treating their component signals individually.