  Cardiovascular system study using ECG signals have evolved tremendously in
the domain of electronics and signal processing. However, there are certain
floating challenges unresolved in the analysis and detection of abnormal
performances of cardiovascular system. As the medical field is moving towards
more automated and intelligent systems, wrong detection or wrong
interpretations of ECG waveform of abnormal conditions can be quite fatal.
Since the PQRST signals vary their positions randomly, the process of locating,
identifying and classifying each feature can be cumbersome and it is prone to
errors. Here we present an automated scheme using adaptive wavelet to detect
prominent R-peak with extreme accuracy and algorithmically tag and mark the
coexisting peaks P, Q, S, and T with almost same accuracy. The adaptive wavelet
approach used in this scheme is capable of detecting R-peak in ECG with 99.99%
accuracy along with the rest of the waveforms.
