Electrocardiogram (ECG) is a noninvasive technique used as a primary diagnostic tool for cardiovascular diseases. The QRS complex detection and extraction is the primary step in the feature extraction of ECG analysis. Denoising of a signal, high accuracy, robustness and less computational time are the key features of QRS complex detection. Here we are introducing comparatively less complex algorithm with good accuracy and computational time. The proposed algorithm is based on wavelet energy-histogram, Hilbert transform & adaptive Thresholding. The QRS complex detection with proposed algorithm is evaluated with all the 48 records from MIT-BIH Arrhythmia database with average detection accuracy 98.3%, average positive predictivity of 0.974, average sensitivity of 0.974, and detection error rate as 5.17%.