Key result
A deep learning network using CNN and LSTM accurately estimated physiological parameters from PPG signals, achieving a mean absolute error of 2.54 ± 3.88 mmHg for systolic blood pressure.
Why the study?
Frequent estimation of blood pressure and heart rate from PPG signals was investigated to facilitate early identification and monitoring of physiological conditions to reduce cardiovascular disease risk.
Does a deep learning network using PPG sensor signals accurately estimate continuous physiological parameters (blood pressure and heart rate)?
Does a deep learning network using PPG sensor signals accurately estimate continuous physiological parameters (blood pressure and heart rate)?
A deep learning model combining CNN and LSTM accurately estimates continuous blood pressure and heart rate from PPG signals, meeting established medical instrumentation standards.
In this paper, we propose a continuous physiological parameter estimation model based on a deep learning network for photoplethysmography (PPG) sensor signals. Signals of 8-s duration were incorporated into the proposed model in this study for frequent estimation of the systolic blood pressure (BP), diastolic BP, heart rate (HR), and mean arterial pressure of the human body; this facilitated early identification and monitoring of physiological conditions and thus reduced the risk of cardiovascular disease. The proposed model was designed using a convolutional neural network (CNN) and long short-term memory (LSTM) network. This model was trained and validated using the large-scale Multiparameter Intelligent Monitoring in Intensive Care database. The CNN was used to extract features from PPG signals automatically. This automatic extraction replaced the conventional manual feature extraction process. Features with time-series were then analyzed using the LSTM network to estimate physiological parameters. Subsequently, ten-fold cross-validation was conducted to reveal the mean absolute errors ± standard deviations of participants’ systolic BP, diastolic BP, HR, and mean arterial pressure to be 2.54 ± 3.88, 1.59 ± 2.45, 1.62 ± 2.55, and 1.59 ± 2.34 mmHg, respectively. These values meet the standards established by the Association for the Advancement of Medical Instrumentation and the British Hypertension Society. The proposed method facilitates the accurate, continuous monitoring of the BP and HR.
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Yen et al. (2021) studied this question. Deep learning network (CNN and LSTM) was evaluated on Mean absolute errors ± standard deviations of systolic BP, diastolic BP, HR, and mean arterial pressure. A deep learning network using CNN and LSTM accurately estimated physiological parameters from PPG signals, achieving a mean absolute error of 2.54 ± 3.88 mmHg for systolic blood pressure.
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