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Estimation of Beat-by-Beat Blood Pressure and Heart Rate From ECG and PPG Using a Fine-Tuned Deep CNN Model
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DOI：10.1109/ACCESS.2022.3195857.png)
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Abstract
En 中文
Given that current cuffless blood pressure (BP) measurement technologies feature acceptable overall accuracy, this paper proposed a sufficiently accurate cuffless BP estimation method based on photoplethysmography (PPG) and electrocardiography (ECG) signals. This study used single-channel PPG and ECG signals to estimate heart rate (HR), diastolic BP (DBP), and systolic BP (SBP). A modified long-term recurrent convolutional network comprising a multi-scale convolution network and a long short-term memory (LSTM) network was used to develop a deep learning model for accurately estimating BP and HR. The PPG and ECG signal data of 1551 patients were obtained from the Data Sets-UCI Machine Learning Repository of the University of California, Irvine. The study dataset comprised ECG, PPG, and arterial BP (ABP) signals from the PhysioNet MIMIC II dataset. The original signals were processed by removing noise and artifacts. The aforementioned dataset contains 12,000 records in a hierarchical data format, with each record containing three signals, namely 125-Hz ECG signals from channel II (ECG lead II), 125-Hz PPG signals from the fingertip, and 125-Hz invasive ABP signals. To validate the stability and performance of the developed model, ten-fold cross-validation was conducted. The mean absolute error (MAE) (standard deviation (SD)) values of the developed model for predicting SBP, DBP, and HR were 2.24 mmHg (3.59 mmHg), 1.40 mmHg (2.56 mmHg), and 0.84 bpm (2.23 bpm), respectively. In addition, the estimated SBP and DBP values satisfied the standards of the British Hypertension Society and the Association for the Advancement of Medical Instrumentation. Compared with the methods proposed in other studies, the deep learning model developed in this study required a lower number of layers to provide accurate SBP, DBP, and HR estimations. The results of this study confirmed the effectiveness of the proposed deep learning architecture.
Keywords:
Feature extraction
Electrocardiography
Estimation
Heart rate
Deep learning
Convolutional neural networks
Convolution
Photoplethysmography (PPG)
electrocardiography (ECG)
blood pressure (BP)
heart rate (HR)
multi-scale convolution
long short-term memory (LSTM)
Journal
IF:
3.6
Papers:
9.7W
Citations:
29.4W
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