The extracorporeal counterpulsation ( ECP ) device requires compression and release of the lower extremity with a high recognition rate and real-time ECG signature wave detection method. Based on traditional medical signal processing methods, the article proposes an algorithm for ECG feature signal detection by the improved differential thresholding and distribution calculation. The algorithm utilizes the low-pass filtering and moving average filtering methods to smooth the ECG signal. Then, the locations of R-wave, P-wave and T-wave are identified and determined by using the adaptive differential double thresholding method and distribution calculation method. Simulation analysis and experimental verification results base on the MIT-BIH database and ECG sampling module show that the algorithm has an accuracy of 99. 9% for the comprehensive recognition of R-wave of ECG signal and 99. 87% for the recognition of P-wave and T-wave. In addition, the average time consumed by the algorithm is only 0. 65 s. The algorithm can identify the characteristic waves of common ECG signals, which can satisfactorily meet the requirements of devices like ECP to quickly identification of ECG characteristic waves.