Application of the Artificial Neural Network for blood pressure evaluation with smartphones

The smartphone is proposed to evaluate the Blood Pressure (BP) anywhere and anytime. The tasks performed by smartphone are (i) extraction of the PhotoPlethysmoGram (PPG) signal from a frame sequence acquired by the integrated camera, and (ii) processing it by Artificial Neural Network for the evaluation of the BP. The PPG signal is evaluated by analyzing the volumetric blood variation of the fingertip on the frame sequence. Successively, parameters characterizing the pulses of the PPG signal are sent to the Fit Forward Neural Network for the simultaneously evaluation of the systolic and the diastolic BP. The validation of the results is performed by comparing them with the ones obtained by the Ambulatory Blood Pressure monitor ABP Spacelabs 90207. Preliminary experimental results show useful information to address the future research devoted to reduce the maximum error.

[1]  G. G. Stokes "J." , 1890, The New Yale Book of Quotations.

[2]  John A. Crowe,et al.  The wavelength dependence of the photoplethysmogram and its implication to pulse oximetry , 1992, 1992 14th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[3]  A. Shennan,et al.  Oscillometric blood pressure measurements in severe pre‐eclampsia: validation of the SpaceLabs 90207 , 1996, British journal of obstetrics and gynaecology.

[4]  C. Belsha,et al.  Accuracy of the SpaceLabs 90207 ambulatory blood pressure monitor in children and adolescents. , 1996, Blood pressure monitoring.

[5]  Iqbal,et al.  Validation of the SpaceLabs 90207 automatic non-invasive blood pressure monitor in elderly subjects. , 1996, Blood pressure monitoring.

[6]  A M Pirie,et al.  Oscillometric blood pressure measurements in severe pre‐eclampsia: validation of SpaceLabs 90207 , 1996, British journal of obstetrics and gynaecology.

[7]  Jeffrey M. Hausdorff,et al.  Physionet: Components of a New Research Resource for Complex Physiologic Signals". Circu-lation Vol , 2000 .

[8]  A. ADoefaa,et al.  ? ? ? ? f ? ? ? ? ? , 2003 .

[9]  Y.T. Zhang,et al.  Continuous and noninvasive estimation of arterial blood pressure using a photoplethysmographic approach , 2003, Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No.03CH37439).

[10]  Heaton T. Jeff,et al.  Introduction to Neural Networks with Java , 2005 .

[11]  Shinobu Tanaka,et al.  Accuracy Assessment of a Noninvasive Device for Monitoring Beat-by-Beat Blood Pressure in the Radial Artery Using the Volume-Compensation Method , 2007, IEEE Transactions on Biomedical Engineering.

[12]  Jing Gao,et al.  Applies of Neural Networks to Identify Gases Based on Electronic Nose , 2007, 2007 IEEE International Conference on Control and Automation.

[13]  Martin Leahy,et al.  Investigating a smartphone imaging unit for photoplethysmography , 2010, Physiological measurement.

[14]  Marko Gargenta Learning Android , 2011 .

[15]  Toshiyo Tamura,et al.  Monitoring and Evaluation of Blood Pressure Changes With a Home Healthcare System , 2011, IEEE Transactions on Information Technology in Biomedicine.

[16]  Domenico Grimaldi,et al.  Photoplethysmography detection by smartphone's videocamera , 2011, Proceedings of the 6th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems.

[17]  A. F. Mitul,et al.  Development of a noninvasive continuous blood pressure measurement and monitoring system , 2012, 2012 International Conference on Informatics, Electronics & Vision (ICIEV).

[18]  Martina Mueller,et al.  Development and Validation of a Smartphone Heart Rate Acquisition Application for Health Promotion and Wellness Telehealth Applications , 2012, International journal of telemedicine and applications.

[19]  Y. Kurylyak,et al.  PE RS ON AL C OP Y 5 Smartphone-Based Photoplethysmogram Measurement , 2012 .

[20]  Domenico Grimaldi,et al.  A Neural Network-based method for continuous blood pressure estimation from a PPG signal , 2013, 2013 IEEE International Instrumentation and Measurement Technology Conference (I2MTC).