The QRS detection algorithm is substantial for healthcare monitoring and diagnostic applications. A low error detection without adding more computation is a big challenge for researchers. The proposed QRS detection algorithm is a simple, real-time, and high-performance hybrid technique based on decision tree and artificial neural networks (ANN). In this study, the five stages algorithm is designed, implemented, and evaluated for wearable healthcare applications. The first stage is filtering the original ECG signal to reduce the noise and baseline wandering. After that, a maximum or minimum moving-window for positive or negative peaks respectively is searching R-peaks for any expected value and finding the Q and S corresponding to this R-peak. Only these values from all ECG samples are passed to the next stage for feature extraction to reduce the algorithm computation. Stage four is excluded any unlikely points using the mean of the slope and level based on a simple decision tree. Finally, artificial neural networks are designed to classify the rest point for QRS detection using ANNs for each peak polarity to improve the network’s performance by separating the data as a positive or negative peak. The algorithm is evaluated based on MATLAB using the MIT-BIH Arrhythmia Database, and the results show a low error rate detection of 0.25%, high sensitivity of 99.86%, and high predictivity of 99.89%. We develop a new approach for real-time QRS detection with low resources and high efficiency compared with other approaches.
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