Abstract
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.
Highlights
Signals acquired using photoplethysmography (PPG) are referred to as PPG signals
This study evaluated the performance of two types of deep learning models, namely ResNetCNN + bidirectional long short-term memory (BILSTM) (Figure 2) and Xception + BILSTM (Figure 3), in classifying cardiovascular diseases according to the input data
This study employed different deep learning models to analyze how the classification accuracy rate of hypertensive disease test results can be enhanced through the selection of different models and parameters given limited available PPG signal data
Summary
Signals acquired using photoplethysmography (PPG) are referred to as PPG signals. By using light sensors to absorb light energy, PPG signals record signals generated by blood flow variations in blood vessels. The research Sxenturk et al.[4] proposed a novel algorithm that removes noise in highly unstable ECG and PPG signals to acquire pulse rate and measure cardiovascular parameters in a timely manner (e.g. pulse rate, total cardiac cycle, and BP). These parameters are applicable for the continuous monitoring of health conditions in patients with cardiac diseases.
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