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Recently, wireless medical body area network (WMBAN) plays an important role in remote cardiac patient monitoring, intelligent emergency care management system, and ubiquitous mobile healthcare applications. The wearable cardiac monitoring devices used in WMBAN system collect and transmit the vital signs of cardiac patients continuously. Generally, the use of WMBAN technology is restricted by size, power consumption, transmission capacity (bandwidth), and computational loads. Therefore, there is a great demand for low-complexity cardiac signal processing algorithms that can combat some of technical challenges related to pervasive healthcare computing with WMBAN technologies. In this paper, we present low complexity automatic QRS detection algorithm for long-term wearable cardiac monitoring device. The proposed QRS detection method first derives a smooth Shannon energy envelogram (SEE) of the first-derivative of the filtered ECG at the preprocessing stage. The major local maxima (LM) in the smooth SEE indicate the approximate locations of the R-peaks. In the second stage, the proposed HT-based peak-finding logic identifies the locations of the LM by detecting the positive zero-crossings in the HT of the SEE. Finally, the locations of the LM are used as guides to find the accurate locations of the R-peaks in the ECG signal. The proposed method is validated using the standard MIT-BIH arrhythmia database, and achieves an overall sensitivity of 99.86% and positive predictivity of 99.95%. Various experimental results show that the proposed algorithm significantly outperforms other well-known algorithms in case of noisy or pathological signals.