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.

<span lang="EN-US">The ECG signal processing methods are tested and evaluated based on many databases. The most ECG database used for many researchers is the MIT-BIH arrhythmia database. The QRS-detection algorithms are essential for ECG analyses to detect the beats for the ECG signal. There is no standard number of beats for this database that are used from numerous researches. Different beat numbers are calculated for the researchers depending on the difference in understanding the annotation file. In this paper, the beat numbers for existing methods are studied and compared to find the correct beat number that should be used. We propose a simple function to standardize the beats number for any ECG PhysioNet database to improve the waveform database toolbox (WFDB) for the MATLAB program. This function is based on the annotation's description from the databases and can be added to the Toolbox. The function is removed the non-beats annotation without any errors. The results show a high percentage of 71% from the reviewed methods used an incorrect number of beats for this database.</span>

<p class="0abstract">The electrocardiograph (ECG) signal is an essential biomedical human body signal that shows heart activity and can diagnose cardiovascular diseases. Many researchers investigate heartbeats detection and classification based on ECG to achieve a high-performance method. The main problem with improving performance is increasing the computation, such as in many existing methods. In this paper, a new artificial neural network (ANN) method named Selective-Mask Artificial Neural Network (SMANN) is proposed to improve the performance with low computational processes. Furthermore, A new mixture of features from reused the QRS-detection stage features and the others features from the RR-interval and between-RR are used to decrease the computation for features extraction. The proposed method performance evaluation is based on the MIT-BIH Arrhythmia Database using MATLAB program for software evaluation moreover a hardware implementation. The proposed method’s promising results show high accuracy of 99.9224 %, and the total classification errors for the SMANN are 80 comparing with the 583 errors for the same data with traditional ANN. The method with low error assists the clinical decision-maker in diagnosing the long-time ECG signals or the real-time monitoring. It was implemented as a prototype wearable system using Node-MCU with the internet of things (IoT). The system can operate online patient monitoring and offline for heartbeats detection and classification.</p>

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