RR Stress Test Time Series classification using Neural networks

2018 ◽  
Author(s):  
Wilson X. Jaramillo ◽  
Fabian Astudillo-Salinas ◽  
Lizandro Solano-Quinde ◽  
Kenneth Palacio-Baus ◽  
Sara Wong
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Stress Test ◽  
Test Time ◽  
Time Series Classification

Combining contextual neural networks for time series classification

2020 ◽  
Vol 384 ◽  
pp. 57-66 ◽  
Author(s):  
Amadu Fullah Kamara ◽  
Enhong Chen ◽  
Qi Liu ◽  
Zhen Pan
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Time Series Classification

scholarly journals Characterizing artifacts in RR stress test time series

2016 ◽  
Author(s):  
Fabian Astudillo-Salinas ◽  
Kenneth Palacio-Baus ◽  
Lizandro Solano-Quinde ◽  
Ruben Medina ◽  
Sara Wong
Keyword(s):  
Time Series ◽  
Stress Test ◽  
Test Time

scholarly journals ECG Arrhythmia Time Series Classification Using 1D Convolution –LSTM Neural Networks

2021 ◽  
Vol 10 (3) ◽  
pp. 1985-1990
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Data Augmentation ◽  
Discrete Wavelet ◽  
Learning Approaches ◽  
Time Series Classification ◽  
Measuring Device ◽  
Average Accuracy ◽  
Accuracy Of Results ◽  
Ecg Arrhythmia

An electrocardiogram (ECG) can be dependablyused as a measuring device to monitor cardiovascular function. The abnormal heartbeat appears in the ECG pattern and these abnormal signals are called arrhythmias. Classification and automatic arrhythmia signals can provide a faster and more accurate result. Several machine learning approaches have been applied to enhance the accuracy of results and increase the speed and robustness of models. This paper proposes a method based on Time-series Classification using deep Convolutional -LSTM neural networks and Discrete Wavelet Transform to classify 4 different types of Arrhythmia in the MIT-BIH Database. According to the results, the suggested method gives predictions with an average accuracy of 97% without needing to do feature extraction or data augmentation.

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