A Wavelet Compression based Multi-resolution Bidirectional LSTM Network for Electrocardiogram Biometric Classification in constructing Biometric Ontology

Author(s):  
Htet Myet Lynn ◽  
Taekeun Hong ◽  
Hyoungju Kim ◽  
Sung Hwan Kim ◽  
Pankoo Kim
2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kilin Shi ◽  
Tobias Steigleder ◽  
Sven Schellenberger ◽  
Fabian Michler ◽  
Anke Malessa ◽  
...  

AbstractContactless measurement of heart rate variability (HRV), which reflects changes of the autonomic nervous system (ANS) and provides crucial information on the health status of a person, would provide great benefits for both patients and doctors during prevention and aftercare. However, gold standard devices to record the HRV, such as the electrocardiograph, have the common disadvantage that they need permanent skin contact with the patient. Being connected to a monitoring device by cable reduces the mobility, comfort, and compliance by patients. Here, we present a contactless approach using a 24 GHz Six-Port-based radar system and an LSTM network for radar heart sound segmentation. The best scores are obtained using a two-layer bidirectional LSTM architecture. To verify the performance of the proposed system not only in a static measurement scenario but also during a dynamic change of HRV parameters, a stimulation of the ANS through a cold pressor test is integrated in the study design. A total of 638 minutes of data is gathered from 25 test subjects and is analysed extensively. High F-scores of over 95% are achieved for heartbeat detection. HRV indices such as HF norm are extracted with relative errors around 5%. Our proposed approach is capable to perform contactless and convenient HRV monitoring and is therefore suitable for long-term recordings in clinical environments and home-care scenarios.


Author(s):  
Rafiul Kadir ◽  
Ritu Saha ◽  
Md. Abdul Awal ◽  
Mohammad Ismat Kadir

Author(s):  
Ahmed Ben Said ◽  
Abdelkarim Erradi ◽  
Hussein Ahmed Aly ◽  
Abdelmonem Mohamed

AbstractTo assist policymakers in making adequate decisions to stop the spread of the COVID-19 pandemic, accurate forecasting of the disease propagation is of paramount importance. This paper presents a deep learning approach to forecast the cumulative number of COVID-19 cases using bidirectional Long Short-Term Memory (Bi-LSTM) network applied to multivariate time series. Unlike other forecasting techniques, our proposed approach first groups the countries having similar demographic and socioeconomic aspects and health sector indicators using K-means clustering algorithm. The cumulative case data of the clustered countries enriched with data related to the lockdown measures are fed to the bidirectional LSTM to train the forecasting model. We validate the effectiveness of the proposed approach by studying the disease outbreak in Qatar and the proposed model prediction from December 1st until December 31st, 2020. The quantitative evaluation shows that the proposed technique outperforms state-of-art forecasting approaches.


2020 ◽  
Author(s):  
Xiaoqi Li ◽  
Yaxing Li ◽  
Yuanjie Dong ◽  
Shan Xu ◽  
Zhihui Zhang ◽  
...  

2021 ◽  
pp. 31-44
Author(s):  
Manmohan Dogra ◽  
Jayashree Domala ◽  
Jenny Dcruz ◽  
Safa Hamdare

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