Dataset Information

Fitbeat: COVID-19 estimation based on wristband heart rate using a contrastive convolutional auto-encoder.

ABSTRACT: This study proposes a contrastive convolutional auto-encoder (contrastive CAE), a combined architecture of an auto-encoder and contrastive loss, to identify individuals with suspected COVID-19 infection using heart-rate data from participants with multiple sclerosis (MS) in the ongoing RADAR-CNS mHealth research project. Heart-rate data was remotely collected using a Fitbit wristband. COVID-19 infection was either confirmed through a positive swab test, or inferred through a self-reported set of recognised symptoms of the virus. The contrastive CAE outperforms a conventional convolutional neural network (CNN), a long short-term memory (LSTM) model, and a convolutional auto-encoder without contrastive loss (CAE). On a test set of 19 participants with MS with reported symptoms of COVID-19, each one paired with a participant with MS with no COVID-19 symptoms, the contrastive CAE achieves an unweighted average recall of 95.3% , a sensitivity of 100% and a specificity of 90.6% , an area under the receiver operating characteristic curve (AUC-ROC) of 0.944, indicating a maximum successful detection of symptoms in the given heart rate measurement period, whilst at the same time keeping a low false alarm rate.

SUBMITTER: Liu S

PROVIDER: S-EPMC8547790 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature

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Fitbeat: COVID-19 estimation based on wristband heart rate using a contrastive convolutional auto-encoder.

Liu Shuo S Han Jing J Puyal Estela Laporta EL Kontaxis Spyridon S Sun Shaoxiong S Locatelli Patrick P Dineley Judith J Pokorny Florian B FB Costa Gloria Dalla GD Leocani Letizia L Guerrero Ana Isabel AI Nos Carlos C Zabalza Ana A Sørensen Per Soelberg PS Buron Mathias M Magyari Melinda M Ranjan Yatharth Y Rashid Zulqarnain Z Conde Pauline P Stewart Callum C Folarin Amos A AA Dobson Richard Jb RJ Bailón Raquel R Vairavan Srinivasan S Cummins Nicholas N Narayan Vaibhav A VA Hotopf Matthew M Comi Giancarlo G Schuller Björn B Consortium Radar-Cns RC

Pattern recognition 20211026

This study proposes a contrastive convolutional auto-encoder (contrastive CAE), a combined architecture of an auto-encoder and contrastive loss, to identify individuals with suspected COVID-19 infection using heart-rate data from participants with multiple sclerosis (MS) in the ongoing RADAR-CNS mHealth research project. Heart-rate data was remotely collected using a Fitbit wristband. COVID-19 infection was either confirmed through a positive swab test, or inferred through a self-reported set of ...[more]

PMID: 34720200

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Project description:BackgroundLong non-coding RNAs (lncRNAs) can prevent, diagnose, and treat a variety of complex human diseases, and it is crucial to establish a method to efficiently predict lncRNA-disease associations.ResultsIn this paper, we propose a prediction method for the lncRNA-disease association relationship, named LDAGM, which is based on the Graph Convolutional Autoencoder and Multilayer Perceptron model. The method first extracts the functional similarity and Gaussian interaction profile kernel similarity of lncRNAs and miRNAs, as well as the semantic similarity and Gaussian interaction profile kernel similarity of diseases. It then constructs six homogeneous networks and deeply fuses them using a deep topology feature extraction method. The fused networks facilitate feature complementation and deep mining of the original association relationships, capturing the deep connections between nodes. Next, by combining the obtained deep topological features with the similarity network of lncRNA, disease, and miRNA interactions, we construct a multi-view heterogeneous network model. The Graph Convolutional Autoencoder is employed for nonlinear feature extraction. Finally, the extracted nonlinear features are combined with the deep topological features of the multi-view heterogeneous network to obtain the final feature representation of the lncRNA-disease pair. Prediction of the lncRNA-disease association relationship is performed using the Multilayer Perceptron model. To enhance the performance and stability of the Multilayer Perceptron model, we introduce a hidden layer called the aggregation layer in the Multilayer Perceptron model. Through a gate mechanism, it controls the flow of information between each hidden layer in the Multilayer Perceptron model, aiming to achieve optimal feature extraction from each hidden layer.ConclusionsParameter analysis, ablation studies, and comparison experiments verified the effectiveness of this method, and case studies verified the accuracy of this method in predicting lncRNA-disease association relationships.

Project description:PurposeCoronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which primarily infects the lower airways and binds to angiotensin-converting enzyme 2 (ACE2) on alveolar epithelial cells. ACE2 is widely expressed not only in the lungs but also in the cardiovascular system. Therefore, SARS-CoV-2 can also damage the myocardium. This report aimed to highlight decreased heart rate variability (HRV) and cardiac injury caused by SARS-CoV-2.Materials and methodsWe evaluated three COVID-19 patients who died. Patients' data were collected from electronic medical records. We collected patient's information, including baseline information, lab results, body temperature, heart rate (HR), clinical outcome and other related data. We calculated the HRV and the difference between the expected and actual heart rate changes as the body temperature increased.ResultsAs of March 14, 2020, 3 (2.2%) of 136 patients with COVID-19 in Tianjin died in the early stage of the COVID-19 epidemic. The immediate cause of death for Case 1, Case 2, and Case 3 was cardiogenic shock, cardiac arrest and cardiac arrest, respectively. The HRV were substantially decreased in the whole course of all three cases. The actual increases in heart rate were 5 beats/min, 13 beats/min, and 4 beats/min, respectively, less than expected as their temperature increased. Troponin I and Creatine Kinase MB isoenzyme (CK-MB) were substantially increased only in Case 3, for whom the diagnosis of virus-related cardiac injury could not be made until day 7. In all three cases, decreased in HRV and HR changes occurred earlier than increases in cardiac biomarkers (e.g., troponin I and CK-MB).ConclusionsIn conclusion, COVID-19 could affect HRV and counteract tachycardia in response to increases in body temperature. The decreases of HRV and HR changes happened earlier than the increases of myocardial markers (troponin I and CK-MB). It suggested the decreases of HRV and HR changes might help predict cardiac injury earlier than myocardial markers in COVID-19, thus its early identification might help improve patient prognosis.Supplementary informationThe online version contains supplementary material available at 10.1007/s44231-022-00024-1.

Project description:IntroductionStrict lockdown rules were imposed to the French population from 17 March to 11 May 2020, which may result in limited possibilities of physical activity, modified psychological and health states. This report is focused on HRV parameters kinetics before, during and after this lockdown period.Methods95 participants were included in this study (27 women, 68 men, 37 ± 11 years, 176 ± 8 cm, 71 ± 12 kg), who underwent regular orthostatic tests (a 5-minute supine followed by a 5-minute standing recording of heart rate (HR)) on a regular basis before (BSL), during (CFN) and after (RCV) the lockdown. HR, power in low- and high-frequency bands (LF, HF, respectively) and root mean square of the successive differences (RMSSD) were computed for each orthostatic test, and for each position. Subjective well-being was assessed on a 0-10 visual analogic scale (VAS). The participants were split in two groups, those who reported an improved well-being (WB+, increase >2 in VAS score) and those who did not (WB-) during CFN.ResultsOut of the 95 participants, 19 were classified WB+ and 76 WB-. There was an increase in HR and a decrease in RMSSD when measured supine in CFN and RCV, compared to BSL in WB-, whilst opposite results were found in WB+ (i.e. decrease in HR and increase in RMSSD in CFN and RCV; increase in LF and HF in RCV). When pooling data of the three phases, there were significant correlations between VAS and HR, RMSSD, HF, respectively, in the supine position; the higher the VAS score (i.e., subjective well-being), the higher the RMSSD and HF and the lower the HR. In standing position, HRV parameters were not modified during CFN but RMSSD was correlated to VAS.ConclusionOur results suggest that the strict COVID-19 lockdown likely had opposite effects on French population as 20% of participants improved parasympathetic activation (RMSSD, HF) and rated positively this period, whilst 80% showed altered responses and deteriorated well-being. The changes in HRV parameters during and after the lockdown period were in line with subjective well-being responses. The observed recordings may reflect a large variety of responses (anxiety, anticipatory stress, change on physical activity…) beyond the scope of the present study. However, these results confirmed the usefulness of HRV as a non-invasive means for monitoring well-being and health in this population.

Dataset Information

Fitbeat: COVID-19 estimation based on wristband heart rate using a contrastive convolutional auto-encoder.

Publications

Fitbeat: COVID-19 estimation based on wristband heart rate using a contrastive convolutional auto-encoder.

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