A Cuffless Blood Pressure Estimation Method Using Dimensionality Increasing and Two-Dimensional Convolution

Blood pressure (BP) monitoring is a basic way to evaluate hypertension and its related diseases. Since non-invasive measurement with cuff is not real-time and invasive measurement with vessel puncture is not practical in daily life, this paper proposes a cuffless BP estimation method using two-dimensional (2D) convolution. Dimensionality increasing algorithms including recurrence plot and Gramian angular field are firstly used to convert electrocardiography (ECG) and photoplethysmography (PPG) signals into 2D images. New fused Gramian angular field (FGAF) and combined Gramian angular field (CGAF) are proposed to reduce the input 2D images data and enhance the signals’ relevance. The converted images are used to train 2D convolutional models and estimate BP values. The 2D models effectively improved BP estimation accuracy, and the accuracy of the VGGNet 2D model using Gramian angular difference field (GADF) is improved by 38% compared with the corresponding 1D convolutional model. The proposed FGAF and CGAF can reduce input data by 50% while maintaining estimation accuracy, and the minimum mean absolute errors of the estimated BP values could reach 2. 71 and 1. 74 mmHg for systolic and diastolic blood pressures, respectively. To reduce model size, the VGGNet BP estimation model is pruned by reducing 60% of channel numbers while maintain the model performance. The pruned VGGNet model using the FGADF is then fine-tuned and validated by MIMIC-III dataset to show its generalization ability. Furthermore, a simple monitor system is built to show the feasibility of signal collection and BP estimation.

Authors

• Shouyi Cui
• Guowei Yang School of Computing and Artificial Intelligence, Southwestern University of Finance and Economics, Chengdu, Sichuan, P.R.China Kash Institute of Electronics and Information Industry, Kash, P.R.China Engineering Research Center of Intelligent Finance, Ministry of Education, Chengdu, Sichuan, P.R.China
• Jingxuan Guan
• Yuheng He
• Xuefang Zhou
• Meihua Bi
• Hanghai Shen
• Yuansheng Xu

Keywords

• Estimation
• Electrocardiography
• Accuracy
• Feature extraction
• Convolutional neural networks
• Training
• MIMICs
• Monitoring
• Hypertension
• Blood Pressure
• Two-dimensional Convolution
• Cuffless Blood Pressure
• Cuffless Blood Pressure Estimation
• Blood Pressure Estimation Method
• Model Performance
• Accurate Estimation
• Systolic Blood Pressure
• Diastolic Blood Pressure
• Sphygmomanometer
• Mean Absolute Error
• 2D Images
• Blood Pressure Values
• 2D Model
• Convolutional Model
• Invasive Measurements
• Photoplethysmography
• 1D Convolution
• Recurrence Plot
• Angular Field
• PPG Signal
• Electrocardiography Signals
• Systolic Blood Pressure Values
• 2D Convolutional Network
• Pulse Transit Time
• Diastolic Blood Pressure Values
• Continuous Blood Pressure
• Pulse Wave Velocity
• Signal Segments
• Invasive Blood Pressure
• electrocardiography (ECG)
• photoplethysmography (PPG)
• dimensionality increasing
• model pruning
• Humans
• Blood Pressure Determination
• Algorithms
• Signal Processing, Computer-Assisted
• Male
• Adult
• Female