Production of reliability proven physiological data for use in automated monitoring and diagnostic systems

In recent years manufacturers of intensive care monitoring systems have introduced complex digital processing architectures that theoretically have enormous processing power. This power should allow the realization of many useful processing methodologies that up to now have only been research tools, e.g. the generation of reliable alarms, the implementation of predictive monitoring strategies and reliable diagnostic and treatment guidance to the clinical staff. However, before any of these methodologies can be successfully initiated, each must have accurate and reliable derived physiological data available to them, e.g. beat-by-beat heart rate and blood pressure. From the very nature of monitoring physiological quantities there will be much misinformation or 'noise' superimposed on the raw signal obtained from the patient. The major source of noise (as far as electrocardiogram (ECG) monitoring is concerned) is internal to the body and is electromyographic noise. This results from the contraction of skeletal muscles producing action potentials of similar magnitude and frequency to that of the ECG. Fortunately, nursing staff are very good at 'filtering out' any misinformation before recording any data (on a ward chart for instance). However, in completely automated systems, if this noise is not detected and eliminated or compensated for at an early stage in the processing chain, misinformation will result with potentially serious consequences. The recognition and elimination of such noise cannot be readily achieved using standard filtering techniques without serious degradation of information. This paper discusses the potential of modern digital system architectures developed for ECG monitoring. It analyses the noise that occurs on this physiological variable and demonstrates a novel method of eliminating such noise.