Photoplethysmography (PPG) is an optical, non-invasive method to assess tissue blood volume/perfusion. When measured on human skin, the PPG signal includes both cardiac synchronous variations (AC) and respiratory induced intensity variations (RIIV). This makes the PPG signal appropriate for cardiorespiratory monitoring, as a single non-invasive sensor extracts both cardiac and respiratory information.
In this thesis, the origin of the RIIV signal is discussed, and invasive measurements of pressures in the circulatory system support the hypothesis of a venous origin. Important factors are intrathoracic and intra-abdominal pressure fluctuations, affecting venous return from the extrathoracic veins and the peripheral venous bed.
Previous reports have demonstrated a possibility to extract the RIIV signal for assessing respiratory rates. A more effective and reliable monitoring would be achieved if tidal volumes could be estimated from the PPG signal in addition to respiratory rates. This would provide a possibility to calculate and detect ventilatory trends. A relationship between the RIIV amplitude and the tidal volume was hypothesised, demonstrated in healthy subjects and verified in a theoretical (Windkessel) model of the circulatory system. Other factors than tidal volume influence intrathoracic and intra-abdominal pressures. Effects of thoraco-abdominal separation, posture and respiratory rate were observed, and their influence in tidal volume/ventilation monitoring was discussed.
Monitoring the cardiorespiratory function is essential in the postoperative and neonatal care environments. Studies have been performed in clinical settings including comparisons between the PPG method and more established monitoring systems. PPG was found to be suitable for monitoring heart and respiratory rates in these environments.
The arterial blood pressure contains respiratory related information, including heart rate fluctuations (respiratory sinus arrhythmia, RSA) and respiratory variations in cardiac stroke volume. These phenomena are seen in the PPG signal as frequency and amplitude modulation of the AC signal. An algorithm based on pattern recognition (neural networking) is presented, in which these respiratory components are extracted and combined with the RIIV signal. As the respiratory components are of different origins, the neural network algorithm is robust and more accurate for breath detection than algorithms utilising the components separately.
The main purposes of cardiorespiratory monitoring are to detect pathologic minute ventilation, apnoea, hypoxaemia, cardiac arrest, arrhythmia, and trends in heart rate. By using PPG, simultaneous information about heart rate, respiratory rate and tidal volume is obtained. Furthermore, as the measurement of arterial oxygen saturation by PPG is well established, a good coverage of the cardiorespiratory function can be obtained from a single non-invasive sensor.
Linköping: Linköpings universitet , 2000. , 63 p.
2000-04-28, Elsa Brändströms sal, Universitetssjukhuset, Linköping, 13:15 (Swedish)