this data consolidation requires a reduction of the number of endpoint value codes available a non linear quantization function is used to represent low valued data points with higher precision and higher valued data points with lower precision this quantization function is not specifically described in the fig5 but is discussed in more detail later on with respect to fig7 and 8 the compression algorithm is suitable for implementation via embedded software dedicated hardware or a combination of the two in any of these cases the sequence of operations is the same and is as described in fig6 the differences lie in the trade offs between hardware and software resources and power consumption in fig6 the letter u2018 x u2019 has been used to identify the source of the signal e g it can be replaced with an u2018 a u2019 for atrial signal or with a u2018 v u2019 for ventricular signal as provided by the atial sensing stage or the ventricular sensing stage respectively the non linear coding referred to in the text associated with fig5 is a means of dealing with egm sample bit widths which might be greater than the available bit field width in the stored code word the idea is that endpoint values with lower magnitudes are encoded with greater precision while endpoint values with higher magnitudes are encoded with lesser precision the table in fig7 shows an example of this type of function this value to code mapping function is further illustrated in fig8 when storing two or more signals simultaneously as for example could be useful in a dual chamber pacemaker the compression method can be used in either of two ways 1 provide separate independent compression systems to operate on the individual.