Key result
Deep learning models using photoplethysmography signals demonstrated an optimal classification accuracy of 76% for categorizing hypertension stages.
Why the study?
Determining optimal deep learning parameters such as kernel, kernel size, and layers for hypertension classification remains challenging when limited photoplethysmography training data are available.
Can deep learning models accurately classify hypertension stages using limited photoplethysmography signals?
Can deep learning models accurately classify hypertension stages using limited photoplethysmography signals?
Deep learning models using photoplethysmography signals can classify hypertension stages with 76% accuracy even with limited training data.
Should not yet change hypertension staging practice; hypothesis-generating for photoplethysmography-based deep learning.
This study used photoplethysmography signals to classify hypertensive into no hypertension, prehypertension, stage I hypertension, and stage II hypertension. There are four deep learning models are compared in the study. The difficulties in the study are how to find the optimal parameters such as kernel, kernel size, and layers in less photoplethysmographyt (PPG) training data condition. PPG signals were used to train deep residual network convolutional neural network (ResNetCNN) and bidirectional long short-term memory (BILSTM) to determine the optimal operating parameters when each dataset consisted of 2100 data points. During the experiment, the proportion of training and testing datasets was 8:2. The model demonstrated an optimal classification accuracy of 76% when the testing dataset was used.
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Yen et al. (2021) studied Hypertension. Deep learning models (ResNetCNN and BILSTM) was evaluated on Classification accuracy. Deep learning models using photoplethysmography signals demonstrated an optimal classification accuracy of 76% for categorizing hypertension stages.
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