Photoplethysmographic Sensors in Automatized Diagnosis of the Cardiovascular System

The implementation of photoplethysmographic sensors in the data capture and data storage to analyze the cardiovascular condition of the patient is a new direction in automatized diagnosis of the cardiovascular system. This chapter contains a description of the use of photoplethysmographic sensors in a computerized patient cardiac monitoring system. The system consists of a portable device for collection of patient’s cardiac data by applying photopletithysmographic method and software for mathematical analysis. An important diagnostic parameter that can be determined by the photoplethysmographic signal is the heart rate variability. The current application of the photoplethysmographic sensors in portable automatized system is of particular importance because the results of cardiac data analysis with these methods can provide not only detailed information about the cardiovascular status of the patients but also provide the opportunity to generate new knowledge about the diagnosis, and the prevention of pathology in cardiovascular diseases. Photoplethysmographic Sensors in Automatized Diagnosis of the Cardiovascular System: New Guidelines in ComputerBased Medical Diagnostics

[1]  M. Dick,et al.  Analyzing heart rate variability in infants using non-linear Poincaré techniques , 2009, 2009 36th Annual Computers in Cardiology Conference (CinC).

[2]  K. Nakajima,et al.  Monitoring of heart and respiratory rates by photoplethysmography using a digital filtering technique. , 1996, Medical engineering & physics.

[3]  Susannah G. Fleming Lionel Tarassenko.  A Comparison of Signal Processing Techniques for the Extraction of Breathing Rate from the Photoplethysmogram , 2007 .

[4]  R. Anderson,et al.  The optics of human skin. , 1981, The Journal of investigative dermatology.

[5]  Gilwon Yoon,et al.  Non-constrained Blood Pressure Monitoring Using ECG and PPG for Personal Healthcare , 2009, Journal of Medical Systems.

[6]  M. Malik.  Heart Rate Variability , 1996, Clinical cardiology.

[7]  T. Tamura,et al.  Photoplethysmographic measurement of heart and respiratory rates using digital filters , 1993, Proceedings of the 15th Annual International Conference of the IEEE Engineering in Medicine and Biology Societ.

[8]  Ivo Iliev,et al.  An Automated Algorithm for Fast Pulse Wave Detection , 2010 .

[9]  J. Vagedes,et al.  How accurate is pulse rate variability as an estimate of heart rate variability? A review on studies comparing photoplethysmographic technology with an electrocardiogram. , 2013, International journal of cardiology.

[10]  Toshiyo Tamura,et al.  Wearable Photoplethysmographic Sensors—Past and Present , 2014 .

[11]  Ki H. Chon,et al.  Estimation of Respiratory Rate From Photoplethysmogram Data Using Time–Frequency Spectral Estimation , 2009, IEEE Transactions on Biomedical Engineering.

[12]  Mudassir Mirza,et al.  A COMPARATIVE STUDY OF HEART RATE VARIABILITY IN DIABETIC SUBJECTS AND NORMAL SUBJECTS , 2012 .

[13]  Lorna Uden,et al.  Service Innovation Using Actor Network Theory , 2011 .

[14]  M. Tayel,et al.  Poincaré Plot for Heart Rate Variability , 2015 .

[15]  Antti Vehkaoja,et al.  Detection of beat-to-beat intervals from wrist photoplethysmography in patients with sinus rhythm and atrial fibrillation after surgery , 2018, 2018 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI).

[16]  J. N. Watson,et al.  An Algorithm for the Detection of Individual Breaths from the Pulse Oximeter Waveform , 2004, Journal of clinical monitoring and computing.

[17]  Jeunwoo Lee,et al.  Cuffless and Non-Invasive Estimation of a Continuous Blood Pressure Based on PTT , 2010, 2010 2nd International Conference on Information Technology Convergence and Services.

[18]  Boreom Lee,et al.  Detection of Stress Levels from Biosignals Measured in Virtual Reality Environments Using a Kernel-Based Extreme Learning Machine , 2017, Sensors.

[19]  B. G. Sudarshan,et al.  HEART RATE VARIABILITY ANALYSIS: A REVIEW , 2013 .

[20]  José Luis Hernández Cáceres,et al.  Heart Rate Variability Analysis Based on Photoplethysmographic Signals , 2015 .

[21]  U. RajendraAcharya.  Advances in cardiac signal processing , 2007 .

[22]  John Allen.  Photoplethysmography and its application in clinical physiological measurement , 2007, Physiological measurement.