Non-invasive Fetal ECG Signal Quality Assessment for Multichannel Heart Rate Estimation

2017 ◽  
Vol 64 (12) ◽  
pp. 2793-2802 ◽  
Author(s):  
Fernando Andreotti ◽  
Felix Grasser ◽  
Hagen Malberg ◽  
Sebastian Zaunseder
Keyword(s):  
Heart Rate ◽  
Quality Assessment ◽  
Signal Quality ◽  
Ecg Signal ◽  
Rate Estimation ◽  
Non Invasive ◽  
Fetal Ecg ◽  
Heart Rate Estimation

scholarly journals Fetal heart rate estimation by non-invasive single abdominal electrocardiography in real clinical conditions

2022 ◽  
Vol 71 ◽  
pp. 103187
Author(s):  
Nafissa Dia ◽  
Julie Fontecave-Jallon ◽  
Mariel Resendiz ◽  
Marie-Caroline Faisant ◽  
Veronique Equy ◽  
...  
Keyword(s):  
Heart Rate ◽  
Fetal Heart Rate ◽  
Fetal Heart ◽  
Rate Estimation ◽  
Non Invasive ◽  
Clinical Conditions ◽  
Heart Rate Estimation

scholarly journals Multi-Headed Conv-LSTM Network for Heart Rate Estimation during Daily Living Activities

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5212
Author(s):  
Michał Wilkosz ◽  
Agnieszka Szczęsna
Keyword(s):  
Heart Rate ◽  
Network Architecture ◽  
Short Term Memory ◽  
Automated Analysis ◽  
Absolute Error ◽  
Rate Estimation ◽  
Non Invasive ◽  
Major Direction ◽  
Lstm Network ◽  
Heart Rate Estimation

Non-invasive photoplethysmography (PPG) technology was developed to track heart rate during physical activity under free-living conditions. Automated analysis of PPG has made it useful in both clinical and non-clinical applications. Because of their generalization capabilities, deep learning methods can be a major direction in the search for a heart rate estimation solution based on signals from wearable devices. A novel multi-headed convolutional neural network model enriched with long short-term memory cells (MH Conv-LSTM DeepPPG) was proposed for the estimation of heart rate based on signals measured by a wrist-worn wearable device, such as PPG and acceleration signals. For the PPG-DaLiA dataset, the proposed solution improves the performance of previously proposed methods. An experimental approach was used to develop the final network architecture. The average mean absolute error (MAE) of the final solution was 6.28 bpm and Pearson’s correlation coefficient between the estimated and true heart rate values was 0.85.

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