End-to-End Blood Pressure Prediction via Fully Convolutional Networks

Abstract

Cardiovascular disease is the leading cause of death in the world. It is vital to prevent it by rapid diagnosis and appropriate management through periodic blood pressure (BP) measurement. Recently, many studies have been conducted on methods to measure BP without a cuff. One of the most common methods of predicting BP without a cuff is to use the correlation between pulse wave velocity (PWV) and BP. Studies that predict BP through PWV have two problems to overcome: 1) Additional efforts are required to extract PWV features manually from various biomedical signals, such as electrocardiogram (ECG) and photoplethysmogram (PPG); and 2) in predicting BP using biomedical signals from other people, individual periodic calibration is required because the correlation between PWV and BP differs from person to person. In this study, we proposed a cuffless BP prediction method based on a deep convolutional neural network (CNN) that can overcome the problems mentioned above. The proposed CNN method 1) can use raw signals for training without PWV feature extraction; and 2) automatically learns the characteristics of biomedical signals from other people to predict BP accurately without calibration. We propose two schemes: extraction through multiple dilated convolution, and concentration through strided convolution with a large kernel, to process sequential ECG and PPG signals through CNN. BP prediction performance was the best when both ECG and PPG signals were used together. To this end, we conducted extensive experiments on the different settings of the proposed method and constructed an effective learning model. The proposed method achieved excellent performance in predicting both systolic blood pressure and diastolic blood pressure over other known approaches. We also verified that the performance of our method fulfills international standard protocols, AAMI, and BHS.INDEX TERMS Biomedical signal processing, computational and artificial intelligence, health information management, machine learning, cuffless blood pressure measurement, deep learning, convolutional neural networks, end-to-end.

Blood Pressure Prediction by a Smartphone Sensor using Fully Convolutional Networks

“…A variety of combinations of PPG signal features, including time-domain, frequencydomain, and entropy-based features, among others, have been used to date as key features for BP prediction. Recently, in BP prediction research using electrocardiography (ECG) in conjunction with PPG signals, an end-to-end approach with self-generated features using deep-learning technology has been used [10], [11]. In this paper, we propose a method for predicting BP without feature extraction using only PPG signals measured by smartphone using the convolutional neural networks (CNN) model proposed in the previous study [11].…”

“…Recently, in BP prediction research using electrocardiography (ECG) in conjunction with PPG signals, an end-to-end approach with self-generated features using deep-learning technology has been used [10], [11]. In this paper, we propose a method for predicting BP without feature extraction using only PPG signals measured by smartphone using the convolutional neural networks (CNN) model proposed in the previous study [11]. Instead of using additional physiological cardiovascular signals, multiple wavelengths of PPG (infrared, red, green, blue) signals were measured using the smartphone's heart rate monitor sensor and analyzed to determine the optimal combination of PPG signals for predicting systolic BP (SBP) and diastolic BP (DBP).…”

Blood pressure prediction by exploiting informative features from ICU patients’ ECG and PPG signals under a heterogeneous ensemble learning framework

“…In recent years, there has been some progress in the utilisation of deep learning techniques for the prediction of BP [29][30][31][32][33]. Su et al [30] devised a multi-layer recurrent neural network (RNN) network named DeepRNN and multi-task training strategy is utilized to train a model to predict systolic BP(SBP), diastolic BP(DBP) and mean BP(MBP) simultaneously.…”

“…The results exhibited that LSTM-CL is better than LSTM and RNN-CL. Baek et al [32] designed a new fully convolutional network for BP prediction, where raw PPG and ECG signals were directly fed as input without additional feature extraction. Slapnicar et al [33] designed a complex convolution network with raw PPG signal and its first and second derivatives as inputs, in an attempt to train the prediction model using a large database-MIMICIII.…”

“…Slapnicar et al [33] designed a complex convolution network with raw PPG signal and its first and second derivatives as inputs, in an attempt to train the prediction model using a large database-MIMICIII. According to the signals used, above works can be grouped into three types of PPG based [10], [13], [15], [21,22], [28], [33], ECG based [14], [23] and PPG and ECG based [12], [16], [19,20], [24], [27], [30], [32]. ECG contains abundant electronic activities and can assist PPG to better predict BP.…”