CNN-LSTM Prediction Method for Blood Pressure Based on Pulse Wave

Cardiovascular disease (CVD), which seriously threatens human health, can be prevented by blood pressure (BP) measurement. However, convenient and accurate BP measurement is a vital problem. Although the easily-collected pulse wave (PW)-based methods make it possible to monitor BP at all times and places, the current methods still require professional knowledge to process the medical data. In this paper, we combine the advantages of Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks, to propose a CNN-LSTM BP prediction method based on PW data. In detailed, CNN first extract features from PW data, and then the features are input into LSTM for further training. The numerical results based on real-life data sets show that the proposed method can achieve high predicted accuracy of BP while saving training time. As a result, CNN-LSTM can achieve convenient BP monitoring in daily health.

[1]  Hamada R. H. Al-Absi,et al.  Risk Factors and Comorbidities Associated to Cardiovascular Disease in Qatar: A Machine Learning Based Case-Control Study , 2021, IEEE Access.

[2]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[3]  Zhu Han,et al.  Caching for Mobile Social Networks with Deep Learning: Twitter Analysis for 2016 U.S. Election , 2020, IEEE Transactions on Network Science and Engineering.

[4]  Yuhong Liu,et al.  LSTM for Mobility Based Content Popularity Prediction in Wireless Caching Networks , 2019, 2019 IEEE Globecom Workshops (GC Wkshps).

[5]  Sung-Bae Cho,et al.  Predicting residential energy consumption using CNN-LSTM neural networks , 2019, Energy.

[6]  Navdeep Jaitly,et al.  Hybrid speech recognition with Deep Bidirectional LSTM , 2013, 2013 IEEE Workshop on Automatic Speech Recognition and Understanding.

[7]  Xi Deng,et al.  A novel short-term blood pressure prediction model based on LSTM , 2019 .

[8]  L. Gallo.  Cardiovascular Disease , 1995, GWUMC Department of Biochemistry Annual Spring Symposia.

[9]  Yue Zhang,et al.  A hybrid model for blood pressure prediction from a PPG signal based on MIV and GA-BP neural network , 2017, 2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD).

[10]  Salwan N. Mahmood,et al.  Development of Blood Pressure Monitor by Using Capacitive Pressure Sensor and Microcontroller , 2018, 2018 International Conference on Engineering Technology and their Applications (IICETA).

[11]  Bo Wen,et al.  Continuous blood pressure measurement from one-channel electrocardiogram signal using deep-learning techniques , 2020, Artif. Intell. Medicine.

[12]  Zixue Cheng,et al.  CNN for situations understanding based on sentiment analysis of twitter data , 2017 .

[13]  David J. C. MacKay,et al.  A Practical Bayesian Framework for Backpropagation Networks , 1992, Neural Computation.

[14]  H. Kanai,et al.  [Indirect blood pressure measurement by the pulse wave velocity method]. , 1984, Iyo denshi to seitai kogaku. Japanese journal of medical electronics and biological engineering.

[15]  Mark Butlin,et al.  Arterial blood pressure measurement and pulse wave analysis—their role in enhancing cardiovascular assessment , 2010, Physiological measurement.

[16]  Ş. Yiğit,et al.  Comparison between oscillometric and invasive blood pressure measurements in critically ill premature infants , 2012, Acta paediatrica.

[17]  S. Parasuraman,et al.  Measurement of invasive blood pressure in rats , 2012, Journal of pharmacology & pharmacotherapeutics.

[18]  Cheolsoo Park,et al.  End-To-End Deep Learning Architecture for Continuous Blood Pressure Estimation Using Attention Mechanism , 2020, Sensors.

[19]  Haruki Kawanaka,et al.  Cuffless Blood Pressure Estimation from only the Waveform of Photoplethysmography using CNN , 2019, 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[20]  Shinobu Tanaka,et al.  Standard algorithm of blood-pressure measurement by the oscillometric method , 1992, Medical and Biological Engineering and Computing.

[21]  T. Wibmer,et al.  Pulse transit time and blood pressure during cardiopulmonary exercise tests. , 2011, Physiological research.

[22]  A novel art of continuous noninvasive blood pressure measurement , 2021, Nature communications.

[23]  Peter E.D. Love,et al.  Convolutional neural networks: Computer vision-based workforce activity assessment in construction , 2018, Automation in Construction.

[24]  Xiaoxiao Sun,et al.  Using CNN and HHT to Predict Blood Pressure Level Based on Photoplethysmography and Its Derivatives , 2021, Biosensors.

[25]  Cheng Jian,et al.  Continuous blood pressure measurement based on photoplethysmography , 2019, 2019 14th IEEE International Conference on Electronic Measurement & Instruments (ICEMI).

[26]  Md. Kamrul Hasan,et al.  Cuffless blood pressure estimation from electrocardiogram and photoplethysmogram using waveform based ANN-LSTM network , 2018, Biomed. Signal Process. Control..

[27]  Mikko Peltokangas,et al.  Age Dependence of Arterial Pulse Wave Parameters Extracted From Dynamic Blood Pressure and Blood Volume Pulse Waves , 2017, IEEE Journal of Biomedical and Health Informatics.

[28]  Yan Chen,et al.  Continuous and Noninvasive Blood Pressure Measurement: A Novel Modeling Methodology of the Relationship Between Blood Pressure and Pulse Wave Velocity , 2009, Annals of Biomedical Engineering.

[29]  Brian W. Anthony,et al.  Real-Time Blood Pressure Estimation From Force-Measured Ultrasound , 2018, IEEE Transactions on Biomedical Engineering.

[30]  Hua Han,et al.  Wind Power Short-Term Prediction Based on LSTM and Discrete Wavelet Transform , 2019, Applied Sciences.

[31]  Jiyu Cheng,et al.  Continuous systolic and diastolic blood pressure estimation utilizing long short-term memory network , 2017, 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[32]  S-H. Liu,et al.  The Cuffless Blood Pressure Measurement with Multi-dimension Regression Model based on Characteristics of Pulse Waveform , 2019, 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[33]  Peter C. Y. Chen,et al.  LSTM network: a deep learning approach for short-term traffic forecast , 2017 .

[34]  Hermann Ney,et al.  A comprehensive study of deep bidirectional LSTM RNNS for acoustic modeling in speech recognition , 2016, 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[35]  Kozo Hirata,et al.  Pulse wave analysis and pulse wave velocity: a review of blood pressure interpretation 100 years after Korotkov. , 2006, Circulation journal : official journal of the Japanese Circulation Society.

[36]  G. Moody,et al.  A database to support development and evaluation of intelligent intensive care monitoring , 1996, Computers in Cardiology 1996.

[37]  K. Robert Lai,et al.  Dimensional Sentiment Analysis Using a Regional CNN-LSTM Model , 2016, ACL.

[38]  Roberto Sega,et al.  Blood-pressure control in the hypertensive population , 1997, The Lancet.

[39]  Prasad Sistla,et al.  Cuff-less Blood Pressure Measurement Using Supplementary ECG and PPG Features Extracted Through Wavelet Transformation , 2019, 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[40]  Mohammed Bennamoun,et al.  A Guide to Convolutional Neural Networks for Computer Vision , 2018, A Guide to Convolutional Neural Networks for Computer Vision.

[41]  Ting Liu,et al.  Recent advances in convolutional neural networks , 2015, Pattern Recognit..

[42]  Tara N. Sainath,et al.  Deep convolutional neural networks for LVCSR , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[43]  Bin Sun,et al.  Machine Learning Method for Continuous Noninvasive Blood Pressure Detection Based on Random Forest , 2021, IEEE Access.

[44]  Emil Raschman,et al.  New Digital Architecture of CNN for Pattern Recognition , 2009, 2009 MIXDES-16th International Conference Mixed Design of Integrated Circuits & Systems.

[45]  Daniel J. Sebald,et al.  Narrowband auscultatory blood pressure measurement , 2002, IEEE Transactions on Biomedical Engineering.