TY - JOUR
T1 - DeepCNAP
T2 - A Deep Learning Approach for Continuous Noninvasive Arterial Blood Pressure Monitoring Using Photoplethysmography
AU - Kim, Dong Kyu
AU - Kim, Young Tak
AU - Kim, Hakseung
AU - Kim, Dong Joo
N1 - Funding Information:
This work was supported in part by the National Research Foundation of Korea (NRF) funded by the Korean Government (Ministry of Science and ICT, MSIT) under Grant 2019R1A2C1003399, and in part by the Korea Medical Device Development Fund funded by the Korea Government (the Ministry of Science and ICT, Ministry of Trade, Industry and Energy, Ministry of Health & Welfare, and Ministry of Food and Drug Safety) under Projects 1711139120 and KMDF-PR-20210528-0001.
Publisher Copyright:
© 2013 IEEE.
PY - 2022/8/1
Y1 - 2022/8/1
N2 - Arterial blood pressure (ABP) monitoring may permit the early diagnosis and management of cardiovascular disease (CVD); however, existing methods for measuring ABP outside the clinic use inconvenient cuff sphygmomanometry, or do not estimate continuous ABP waveforms. This study proposes a novel deep learning model DeepCNAP for estimating continuous BP waveform from a noninvasively measured photoplethysmography (PPG) signal in real-time. DeepCNAP was designed through the combination of deep convolutional networks and self-attention. The proposed method was constructed via 10-fold cross-validation based on the MIMIC database (the number of subjects = 942, recording time = 374.43 hours). The performance of DeepCNAP was evaluated from two perspectives: estimating ABP from PPG and classifying hemodynamically unstable events (i.e., hypertension, prehypertension, hypotension, and the normal state). The mean absolute errors of the BP estimates were 3.40 ± 4.36 mmHg for systolic BP, 1.75 ± 2.25 mmHg for diastolic BP, and 3.23 ± 2.21 mmHg for the BP waveform, indicating that DeepCNAP satisfies the standards of both the British Hypertension Society (BHS) and the Association for the Advancement of Medical Instrumentation (AAMI). From the estimated BP, hypertension, prehypertension, hypotension, and the normal state were classified with 99.44, 97.58, 92.23, and 94.64% accuracy, respectively. DeepCNAP enables feasible real-time estimation of invasively measured ABP from noninvasive PPG. With its noninvasive nature, high accuracy, and clinical relevance, the proposed model could be valuable in general wards at hospitals and for wearable devices in daily life.
AB - Arterial blood pressure (ABP) monitoring may permit the early diagnosis and management of cardiovascular disease (CVD); however, existing methods for measuring ABP outside the clinic use inconvenient cuff sphygmomanometry, or do not estimate continuous ABP waveforms. This study proposes a novel deep learning model DeepCNAP for estimating continuous BP waveform from a noninvasively measured photoplethysmography (PPG) signal in real-time. DeepCNAP was designed through the combination of deep convolutional networks and self-attention. The proposed method was constructed via 10-fold cross-validation based on the MIMIC database (the number of subjects = 942, recording time = 374.43 hours). The performance of DeepCNAP was evaluated from two perspectives: estimating ABP from PPG and classifying hemodynamically unstable events (i.e., hypertension, prehypertension, hypotension, and the normal state). The mean absolute errors of the BP estimates were 3.40 ± 4.36 mmHg for systolic BP, 1.75 ± 2.25 mmHg for diastolic BP, and 3.23 ± 2.21 mmHg for the BP waveform, indicating that DeepCNAP satisfies the standards of both the British Hypertension Society (BHS) and the Association for the Advancement of Medical Instrumentation (AAMI). From the estimated BP, hypertension, prehypertension, hypotension, and the normal state were classified with 99.44, 97.58, 92.23, and 94.64% accuracy, respectively. DeepCNAP enables feasible real-time estimation of invasively measured ABP from noninvasive PPG. With its noninvasive nature, high accuracy, and clinical relevance, the proposed model could be valuable in general wards at hospitals and for wearable devices in daily life.
KW - Deep learning
KW - arterial blood pressure
KW - hemodynamic instability
KW - photoplethysmography
KW - self-attention
UR - http://www.scopus.com/inward/record.url?scp=85132528329&partnerID=8YFLogxK
U2 - 10.1109/JBHI.2022.3172514
DO - 10.1109/JBHI.2022.3172514
M3 - Article
C2 - 35511844
AN - SCOPUS:85132528329
SN - 2168-2194
VL - 26
SP - 3697
EP - 3707
JO - IEEE Journal of Biomedical and Health Informatics
JF - IEEE Journal of Biomedical and Health Informatics
IS - 8
ER -