Background: Digital continuous blood pressure (BP) monitoring is increasingly being used in clinical and remote settings. Although it could significantly help clinicians in vital signs monitoring, the analyzing of such amount of BP data is challenging. Objective: This study is aimed to investigate the feasibility of applying deep convolutional neural network (CNN) to the estimation of the systolic blood pressure (SBP) using electrocardiogram (ECG) and photo plethysmography (PPG) signals.
Method: A total of 62500 ECG and PPG signals, sampled at 125 Hz, with 250 corresponding SBP, sampled at 1 Hz, were selected from Medical Information Mart for Intensive Care (MIMIC-III) Waveform Database. The collected signals from 22 subjects were divided into training (80%) and testing (20%) datasets. A CNN-based model was designed with five convolutional layers, one fully connected layer, and one regression layer to predict the SBP. Two different methods of applying data to the input of the CNN model was evaluated. In the first method, the continuous wavelet transform of the data was used while in the second method, the raw ECG and PPG signals without preprocessing were used as the input dataset.
Results: The results showed a high accuracy of 87.42% for the first method and 90.31% for the second method. Moreover, the mean square errors of 5.43 mmHg and 4.82 mmHg were measured for first and second models, respectively.
Conclusions: Both methods are capable of learning how to extract relevant features from the ECG and PPG signals and estimate SBP within an acceptable error margin set by Association for the Advancement of Medical Instrumentation (AAMI).