A New Approach Based on Dynamical Model of The ECG Signal to Blood Pressure Estimation

Electrocardiogram (ECG) signal represents electrical activity of the heart. Blood pressure (BP), as the output of the heart's activity, is one of the important physiological parameters of the human body. It is demonstrated that there is a complex nonlinear relationship between the ECG signal and BP. The continuous measurement of BP can avail early detection, control and treatment of BP related diseases. To reduce difficulties and adverse effects of the traditional methods of BP measurement techniques which use a cuff, the research on cuff-less BP estimation is of significant interest in the community. This paper presents a new feature extraction algorithm based on McSharry's ECG signal dynamical modeling for estimating BP using only the ECG signal. In fact, this feature vector is formed using the morphology of the ECG signal. The proposed method, utilizing data mining techniques, achieved 1.125 mmHg for mean error and 3.125 mmHg for standard deviation of Systolic Blood Pressure (SBP).

[1]  Mohammad Bagher Shamsollahi,et al.  ECG Denoising and Compression Using a Modified Extended Kalman Filter Structure , 2008, IEEE Transactions on Biomedical Engineering.

[2]  Saso Koceski,et al.  Continuous Blood Pressure Monitoring as a Basis for Ambient Assisted Living (AAL) – Review of Methodologies and Devices , 2019, Journal of Medical Systems.

[3]  Xiufeng Yang,et al.  Noninvasive monitoring of blood pressure using optical Ballistocardiography and Photoplethysmograph approaches , 2013, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[4]  Patrick E. McSharry,et al.  A dynamical model for generating synthetic electrocardiogram signals , 2003, IEEE Transactions on Biomedical Engineering.

[5]  Cheng-Chi Tai,et al.  Accurate programmable electrocardiogram generator using a dynamical model implemented on a microcontroller , 2006 .

[6]  S. B. Jameie,et al.  A Developed Zeeman Model for HRV Signal Generation in Different Stages of Sleep , 2009 .

[7]  Norbert Noury,et al.  A review of methods for non-invasive and continuous blood pressure monitoring: Pulse transit time method is promising? , 2014 .

[8]  M. Mauloni,et al.  High blood pressure and 'ischaemic' ECG patterns in climacteric women. , 1985, Maturitas.

[9]  Yuhui Shi,et al.  Particle swarm optimization: developments, applications and resources , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[10]  Matjaz Gams,et al.  Non-Invasive Blood Pressure Estimation from ECG Using Machine Learning Techniques , 2018, Sensors.

[11]  Reza Sameni,et al.  Extraction of Fetal Cardiac Signals from an Array of Maternal Abdominal Recordings , 2008 .

[12]  Kouhyar Tavakolian,et al.  Preliminary Results for Estimating Pulse Transit Time Using Seismocardiogram , 2015 .

[13]  Mostafa Charmi,et al.  Cuff-Less Blood Pressure Estimation Using Only the ECG Signal in Frequency Domain , 2018, 2018 8th International Conference on Computer and Knowledge Engineering (ICCKE).

[14]  Mehmet Rasit Yuce,et al.  A survey on signals and systems in ambulatory blood pressure monitoring using pulse transit time , 2015, Physiological measurement.

[15]  C. Jutten,et al.  Filtering noisy ECG signals using the extended kalman filter based on a modified dynamic ECG model , 2005, Computers in Cardiology, 2005.

[16]  Luis Enrique Mendoza,et al.  Relationship of blood pressure with the electrical signal of the heart using signal processing Relación entre la presión sanguínea y la señal eléctrica del corazón usando una señal de procesamiento , 2014 .

[17]  M. Ataei,et al.  Heart diseases prediction based on ECG signals' classification using a genetic-fuzzy system and dynamical model of ECG signals , 2014, Biomed. Signal Process. Control..

[18]  Peter Scarborough,et al.  Cardiovascular disease in Europe: epidemiological update. , 2014, European heart journal.

[19]  P. Sasikala,et al.  Identification of Individuals using Electrocardiogram , 2010 .

[20]  S. A. Pyko,et al.  Blood pressure — Heart rate syncronization coefficient as a complementary indicator of baroreflex mechanism efficiency , 2015, 2015 XVIII International Conference on Soft Computing and Measurements (SCM).