Adaptive Wavelet Based Identification and Extraction of PQRST Combination in Randomly Stretching ECG Sequence
This addresses the challenge of reducing fatal errors in automated ECG interpretation for medical applications, though it appears incremental as it builds on existing wavelet methods.
The paper tackled the problem of accurately identifying PQRST features in ECG signals that vary randomly in position, presenting an automated scheme using adaptive wavelets that achieved 99.99% accuracy in detecting R-peaks and tagging other peaks with similar precision.
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.