It has been long established that respiration has an influence on heart rate, and this effect is called respiratory sinus arrhythmia. As a result, two inferences can be postulated: first respiration information can be derived from cardiac activity, and second this effect offers the potential of removing the respiration effect that suppresses cardiac information which is of clinical significance. As a result of research performed at NJIT, there is a significant amount of data on exercise and heart rate recovery, but not the associated respiration signal. The motivation of this research was to compare and implement an optimal ECG derived respiration program, develop an adaptive peak detector algorithm to process the complex respiration signal and produce a usable respiration rate waveform. Three methods for deriving respiration from a single lead ECG were identified and implemented in LabVIEW. The three methods were R wave amplitude modulation (RWA), R wave duration (RWD), and the multiplication of RWA and RWD signals. Data analysis was carried out by comparing actual paced breathed respiration signal with lead I ECG derived respiration of ten normal subjects. The data analysis suggests that RWA is the best method with a correlation of 0.95. Then an algorithm to derive a continuous respiration rate signal from actual respiration signal with a high level of accuracy was developed. As a result of this research a program has been developed which provides respiratory information of clinical significance from ordinary single lead ECG for situations in which ECG but respiration is not routinely monitored.