QRS Complex Detection Algorithm Research Articles

A simple and effective deep neural network based QRS complex detection method on ECG signal.

Introduction: The QRS complex is the most prominent waveform within the electrocardiograph (ECG) signal. The accurate detection of the QRS complex is an essential step in the ECG analysis algorithm, which can provide fundamental information for the monitoring and diagnosis of the cardiovascular diseases. Methods: Seven public ECG datasets were used in the experiments. A simple and effective QRS complex detection algorithm based on the deep neural network (DNN) was proposed. The DNN model was composed of two parts: a feature pyramid network (FPN) based backbone with dual input channels to generate the feature maps, and a location head to predict the probability of point belonging to the QRS complex. The depthwise convolution was applied to reduce the parameters of the DNN model. Furthermore, a novel training strategy was developed. The target of the DNN model was generated by using the points within 75 milliseconds and beyond 150 milliseconds from the closest annotated QRS complexes, and artificial simulated ECG segments with high heart rates were generated in the data augmentation. The number of parameters and floating point operations (FLOPs) of our model was 26976 and 9.90M, respectively. Results: The proposed method was evaluated through a cross-dataset test and compared with the sophisticated state-of-the-art methods. On the MITBIH NST, the proposed method demonstrated slightly better sensitivity (95.59% vs. 95.55%) and lower presicion (91.03% vs. 92.93%). On the CPSC 2019, the proposed method have similar sensitivity (95.15% vs.95.13%) and better precision (91.75% vs. 82.03%). Discussion: Experimental results show the proposed algorithm achieved a comparable performance with only a few parameters and FLOPs, which would be useful for the application of ECG analysis on the wearable device.

Accurate QRS complex detection in 12-lead ECG signals using multi-lead fusion

In this paper, a novel multi-lead fusion approach for accurate detection of the QRS complex from 12-lead ECG (Electrocardiogram) signals is proposed. The framework is divided into two major stages. In the first stage, the single-lead QRS complex detection algorithm detects the QRS complex location of each lead. In the second stage, the multi-lead fusion method combines the QRS complex locations obtained in each of the 12-leads, thereby improving the performance of QRS complex detection in terms of sensitivity and positive predictivity by discarding the false positives detected in each lead. The performance of the proposed method is validated using the databases of the St. Petersburg Institute of Cardiological Technics (INCART) (Goldberger et al., 2000) and the CSE (Common Standards for Electrocardiography) (Willems et al., 1987). The proposed framework achieves 99.87% sensitivity and 99.96% positive predictive accuracy for the INCART database. Sensitivity and positive predictivity for the CSE database were found to be 100% and 99.13%, respectively.

Low-Power Long-Term Ambulatory Electrocardiography Monitor of Three Leads with Beat-to-Beat Heart Rate Measurement in Real Time.

A low-power long-term ambulatory ECG monitor was developed for the acquisition, storage and processing of three simultaneous leads DI, aVF and V2 with a beat-to-beat heart rate measurement in real time. It provides long-term continuous ECG recordings until 84 h. The monitor uses a QRS complex detection algorithm based on the continuous wavelet transform with splines, which automatically selects the scale for the analysis of ECG records with different sampling frequencies. It includes a lead-off detection to continuously monitor the electrode connections and a real-time system of visual and acoustic alarms to alert users of abnormal conditions in its operation. The monitor presented is based in an ADS1294 analogue front end with four channels, 24-bit analog-to-digital converters and programmable gain amplifiers, a low-power dual-core ESP32 microcontroller, a microSD memory for data storage in a range of 4 GB to 32 GB and a 1.4 in thin-film transistor liquid crystal display (LCD) variant with a resolution of 128 × 128 pixels. It has programmable sampling rates of 250, 500 and 1000 Hz; a bandwidth of 0 Hz to 50% of the selected sampling rate; a CMRR of -105 dB; an input margin of ±2.4 V; a resolution of 286 nV; and a current consumption of 50 mA for an average battery life of 84 h. The ambulatory ECG monitor was evaluated with the commercial data-acquisition system BIOPAC MP36 and its module for ECG LABEL SS2LB, simultaneously comparing the morphologies of two ECG records and obtaining a correlation of 91.78%. For the QRS detection in real time, the implemented algorithm had an error less than 5%. The developed ambulatory ECG monitor can be used for the analysis of the dynamics of the heart rate variability in long-term ECG records and for the development of one's own databases of ECG recordings of normal subjects and patients with cardiovascular and noncardiovascular diseases.

Arrhythmia Classification Based on Adaptive Refined Composite Multiscale Fluctuation Dispersion Entropy

To improve the accuracy of electrocardiography (ECG) signal classification and identify abnormal heart rhythms, an arrhythmia classification algorithm based on adaptive refined composite multiscale fluctuation dispersion entropy (ARCMFDE) is proposed. First, an improved QRS complex detection algorithm named the improved Pan-Tompkins algorithm (IPTA) is used. The QRS wave is detected, and the waveform is further processed; then, the signal is decomposed into multiple modal components using variational mode decomposition with the optimized number of decomposition layers (K). Subsequently, the RCMFDE is extracted from the different modal components as a classification feature. Finally, differential evolution (DE) and grey wolf optimization (GWO) are combined to form the hybrid differential evolution-grey wolf pack optimization (DE-GWO) algorithm to optimize the penalty factor c and the kernel function parameter g of the support vector machine for performing pattern recognition. Experimental results show that compared with other methods such as variational mode decomposition (VMD), fluctuation dispersion entropy (FDE), genetic algorithms (GA), and support vector machine (SVM). The proposed classification model has superior performance, with an average accuracy of 96.1%, a sensitivity of 95.9%, and a specificity of 98.7% for four types of heart rhythm recognition. Thus, accurate classification of ECG signals can be achieved using the proposed ARCMFDE-based DE-GWO method.

Clinical Fusion for Real-Time Complex QRS Pattern Detection in Wearable ECG Using the Pan-Tompkins Algorithm

This scientific paper presents a novel approach of real-time signal analysis in electrocardiogram (ECG) monitoring systems, focusing on the integration of device design,algorithm implementation for accurate measurement and interpretation of heart activity. The proposed system leverages a low-cost framework, employing a microcontroller and Arduino programming language for raw ECG data acquisition, while utilizing the AD8232 sensor and ESP8266 Node MCU for continuous patient monitoring. The acquired data is processed, stored, and analyzed using the Pan-Tompkins algorithm, which effectively filters and analyzes heart signals, including noise reduction and QRS complex detection. Two case studies involving a healthy individual and a patient with Myocarditis were conducted to demonstrate the effectiveness of the system. The integration of device design and algorithm development in ECG analysis is emphasized, highlighting the affordability, wearability, and potential for continuous monitoring and early detection of heart conditions. By successfully mitigating noise-related challenges, the implementation of the Pan algorithm enables accurate signal analysis. This interdisciplinary research contributes to the advancement of ECG interpretation and underscores the significance of clinical fusion between designed systems and applied algorithms on real cases. The performance of two Pan-Tompkins based QRS complex detection algorithms was systematically analyzed, offering valuable insights for their reasonable utilization.

A High-Performance Low Complex Design and Implementation of QRS Detector Using Modified MaMeMi Filter Optimized with Mayfly Optimization Algorithm

Electrocardiogram (ECG) is considered as the important diagnostic tests in medical field for detecting the cardiac anomalies. But, the ECG signals are polluted with numerous noise from power line intrusion, muscle noise, baseline wander, motion artifacts, low frequency noise signals, high frequency noise signals and T-wave, which automatically affects the QRS profile. The existing method provides the result in lesser accuracy with higher rate of error detection. To overcome these issues, QRS detector using modified maximum mean minimum (MoMaMeMi) filter optimized with mayfly optimization algorithm (QRS-MoMaMeMi-MOA) is proposed in this paper for less computational cost along with resource requirements. The proposed filter design consists of two phases for detecting QRS detector, such as filtering process associated to the enhancement and detection phase. Initially, the ECG data are taken from MIT/BIH arrhythmia dataset (MIT-AD). For eradicating the baseline wander in ECG data, MaMeMi filter is used. For expanding the performance of the modified MaMeMi filter, filter parameters, such as [Formula: see text] and [Formula: see text] are optimized by MOA to accomplish the best values and measure the performance of the whole QRS detector. For high frequency noise suppression in ECG data, the range function, noise subtractors, modified triangular detector are used. Then, heart beat detection can be done with the help of adaptive thresholding technique. The proposed filter design is carried out in MATLAB and implemented on field programmable gate arrays (FPGAs). The proposed QRS-MoMaMeMi-MOA filter design had 0.93%, 0.12% and 0.19% higher accuracy and 89.32%, 50% and 62% low detection error rate, compared to the existing filters, like Kalman filtering based adaptive threshold algorithm for QRS complex detection (QRS-KF-ATA), QRS detection of ECG signal utilizing hybrid derivative with MaMeMi filter by efficiently removing the baseline wander (QRS-HD-MaMeMi), and knowledge-based QRS detection operated by cascade of moving average filters (QRS-CAF). Then, the device utilization of the proposed FPGA implementation of the QRS-MoMaMeMi-MOA filter provides 95.556% and 71.428% lower power usage compared with the existing algorithms, like Kalman filtering based adaptive threshold algorithm for QRS complex detection in FPGA (FPGA-QRS-KF-ATA), and efficient architecture for QRS detection in FPGA utilizing integer Haar wavelet transform (FPGA-QRS-IHWT).

Robust Identification of the QRS-Complexes in Electrocardiogram Signals Using Ramanujan Filter Bank-Based Periodicity Estimation Technique

Plausibly, the first computerized and automated electrocardiogram (ECG) signal processing algorithm was published in the literature in 1961, and since then, the number of algorithms that have been developed to-date for the detection of the QRS-complexes in ECG signals is countless. Both the digital signal processing and artificial intelligence-based techniques have been tested rigorously in many applications to achieve a high accuracy of the detection of the QRS-complexes in ECG signals. However, since the ECG signals are quasi-periodic in nature, a periodicity analysis-based technique would be an apt approach for the detection its QRS-complexes. Ramanujan filter bank (RFB)-based periodicity estimation technique is used in this research for the identification of the QRS-complexes in ECG signals. An added advantage of the proposed algorithm is that, at the instant of detection of a QRS-complex the algorithm can efficiently indicate whether it is a normal or a premature ventricular contraction or an atrial premature contraction QRS-complex. First, the ECG signal is preprocessed using Butterworth low and highpass filters followed by amplitude normalization. The normalized signal is then passed through a set of Ramanujan filters. Filtered signals from all the filters in the bank are then summed up to obtain a holistic time-domain representation of the ECG signal. Next, a Gaussian-weighted moving average filter is used to smooth the time-period-estimation data. Finally, the QRS-complexes are detected from the smoothed data using a peak-detection-based technique, and the abnormal ones are identified using a period thresholding-based technique. Performance of the proposed algorithm is tested on nine ECG databases (totaling a duration of 48.91 days) and is found to be highly competent compared to that of the state-of-the-art algorithms. To the best of our knowledge, such an RFB-based QRS-complex detection algorithm is reported here for the first time. The proposed algorithm can be adapted for the detection of other ECG waves, and also for the processing of other biomedical signals which exhibit periodic or quasi-periodic nature.

Embedded QRS complex detection based on ECG signal strength and trend

Cardiovascular diseases (CVD) are among the most fatal diseases and its mitigation is a huge challenge in healthcare. Electrocardiography (ECG) remains the most commonly used method and device for the visualization and analysis cardiac rhythm. QRS complex detection has been extensively used for this purpose. Recently, several PC based QRS detection algorithms were proposed wherein the QRS complex is detected from previously stored ECG signals. However, with the expansion of personalized medicine backed by advances in the field of wearable medical devices, it is imperative to have an algorithm that is robust, accurate, real-time, and computationally efficient for low resource microcontroller units (MCU). In this paper, a novel embedded QRS complex detection algorithm based on the ECG signal strength and its trend is presented. The algorithm is based on a simulated capacitor threshold that captures the signal strength two other thresholds based on the signal trend to find the position of the R-peak. Subsequently, the location of the end of the R-peak and the tangent to the ECG curve are used to find the onset of the QRS complex. The algorithm obtained average F1 scores of 99.75%, 99.84%, 97.39%, and 86.63% on the MIT-BIH Arrhythmia, MIT-BIH Normal Sinus, the MIT-BIH Noise Stress Test, and the 2014 PhysioNet/CinC Challenge databases respectively. Average run-times for the processing of a single data point were 3.38μs, and 383μs when the algorithm was running respectively on an ESP32-WROOM-32 MCU @240MHz and an Arduino Nano MCU @16MHz. This algorithm has a great potential in the field of wearable medical devices and remote monitoring systems.

A ROBUST QRS COMPLEX DETECTION METHOD BASED ON SHANNON ENERGY ENVELOPE AND HILBERT TRANSFORM

QRS complex detection plays an important role in electrocardiogram (ECG) automatic analysis. The accuracy and robustness of the detection algorithm greatly affect its practicability. However, the existing detection algorithms are greatly affected by ECG signal quality, and some detection algorithms cannot even work properly due to the poor signal quality. In this paper, a robust QRS complex detection algorithm is proposed based on Shannon energy envelope and Hilbert transform. The detection algorithm extracts the Shannon energy envelope of the preprocessed ECG signal, performs Hilbert transform on the envelope signal, then detects the suspected [Formula: see text]-peaks on the envelope by detecting the position of zero pass and screens the real [Formula: see text]-peaks by using a combination of ECG refractory period and backtracking mechanism. The proposed detection algorithm is validated using MIT-BIH Arrhythmia Database, and achieves the average detection accuracy of 99.69%, sensitivity of 99.81% and positive predictivity of 99.88%. Experimental results show that the proposed detection algorithm can still detect QRS complex correctly under complex interference, and the performance of the algorithm is hardly affected.

A new approach to adaptive threshold based method for QRS detection with fuzzy clustering

The most crucial requirements for a QRS complex detection algorithm are accuracy, precision and repeatability. Most methods of detecting QRS complexes use the approach based on exceeding a certain amplitude threshold. However, the presence of noise in the electrocardiographic signal can inhibit the accuracy and precision of detection especially for low amplitude QRS-complexes. The proposed algorithm uses a new approach for the amplitude threshold determination and in the decision stage. The fuzzy c-median clustering method is used to determine the amplitude threshold values for each sliding window across the composed detection function waveform. It allows us to adjust threshold value to noise variations in the ECG signal. When a specified amplitude threshold is exceeded by the detection function and finding the peaks in its waveform, potential QRS complexes can be identified. Then the identified peaks are evaluated on the basis of the speed of rising and falling slopes of detection function peak. It enables identification of only those peaks of the detection function whose location corresponds to QRS complexes. ECG recordings taken from the standard-available eight databases are used to evaluate the performance quality of the proposed method. The proposed QRS detector achieved sensitivity of 99.82%, positive predictivity of 99.88% over the validation MIT-BIH Arrhythmia Database. The overall sensitivity and positive predictivity are respectively 99.81% and 99.67%. Advantages of the proposed method are the robustness against noise, the accuracy and the simplicity of the algorithm that evaluates the candidate peaks of the detection function which indicate the QRS complexes.

A hybrid EMD-DWT based algorithm for detection of QRS complex in electrocardiogram signal

Accurate QRS detection is an important first step for almost every electrocardiogram (ECG) signal analysis. However, detecting QRS is difficult, not only because of the large variety, but also as a result of interference caused by various types of noise. This paper employs a hybrid feature extraction technique of ECG signal for the detection of cardiac abnormalities. Noise removal and accurate QRS detection play a major role in the analysis of ECG signals. In this paper various types of noises such as additive white Gaussian noise, baseline wander and power line interference is eliminated to enhance the signal quality. This study proposes an improved QRS complex detection algorithm based on the combination of empirical mode decomposition-discrete wavelet transform (EMD-DWT) with threshold and compared with ordinary discrete wavelet transform. The system efficacy and performance have been evaluated using accuracy, sensitivity (Se), positive predictive value (PPV) and detection error rate (DER). The results show the high accuracy of the proposed EMD-DWT algorithm, which attains a detection error rate of 1.1233%, a sensitivity of 99.28%, and a positive predictive value of 99.99%, evaluated using the MIT-BIH arrhythmia database. The proposed algorithm improves the accuracy of QRS detection compared to state-of-art methods.

A 12-Bit Dynamic Tracking Algorithm-Based SAR ADC With Real-Time QRS Detection

A 12-bit successive approximation register (SAR) ADC based on dynamic tracking algorithm and a real-time QRS-detection algorithm are proposed. The dynamic tracking algorithm features two tracking windows which are adjacent to prediction interval. This algorithm is able to track down the input signal's variation range and automatically adjust the subrange interval and update prediction code. QRS-complex detection algorithm integrates synchronous time sequential ADC and realtime QRS-detector. The chip is fabricated in a standard 0.13 μm CMOS process with a 0.6 V supply. Measurement results show that proposed ADC exhibits 10.72 effective number of bit (ENOB) and 79.63 dB spur-free dynamic range (SFDR) at 10k Hz sample rate given 41.5 Hz sinusoid input. The DNL and INL are bounded at -0.6/0.62 LSB and -0.67/1.43 LSBs. The ADC achieves FoM of 48 fJ/conversion-step at the best case. Also, the prototype is experimented with ECG signal input and extracts the heart beat signal successfully.

A Graph-constrained Changepoint Detection Approach for ECG Segmentation.

Electrocardiogram (ECG) signal is the most commonly used non-invasive tool in the assessment of cardiovascular diseases. Segmentation of the ECG signal to locate its constitutive waves, in particular the R-peaks, is a key step in ECG processing and analysis. Over the years, several segmentation and QRS complex detection algorithms have been proposed with different features; however, their performance highly depends on applying preprocessing steps which makes them unreliable in realtime data analysis of ambulatory care settings and remote monitoring systems, where the collected data is highly noisy. Moreover, some issues still remain with the current algorithms in regard to the diverse morphological categories for the ECG signal and their high computation cost. In this paper, we introduce a novel graph-based optimal changepoint detection (GCCD) method for reliable detection of Rpeak positions without employing any preprocessing step. The proposed model guarantees to compute the globally optimal changepoint detection solution. It is also generic in nature and can be applied to other time-series biomedical signals. Based on the MIT-BIH arrhythmia (MIT-BIH-AR) database, the proposed method achieves overall sensitivity Sen = 99.76, positive predictivity PPR = 99.68, and detection error rate DER = 0.55 which are comparable to other state-of-the-art approaches.1 2.

A Kalman filtering based adaptive threshold algorithm for QRS complex detection

This work presents an adaptive threshold algorithm in electrocardiogram signal feature extraction by introducing Kalman filtering theory. Low computational cost, low storage requirement and fast response feature are achieved by applying two sets of adaptive threshold systems in different conditions. Also, double-threshold peak detection design dramatically decreases the false detection conditions resulting from noise. As a proof of concept, the proposed algorithm is verified in Matlab and implemented on field programmable gate arrays (FPGA) using MIT/BIH database. The experimental results demonstrate proposed algorithm consumes low resource of FPGA and exhibits 99.30 % detection sensitivity and 99.31 % positive prediction in average, respectively. With the self-adjusting system, proposed algorithm can rapidly adapt different individuals in satisfied detection accuracy.

Detecting Noisy ECG QRS Complexes Using WaveletCNN Autoencoder and ConvLSTM

In this paper, we propose a novel machine learning pipeline to detect QRS complexes in very noisy wearable electrocardiogram (ECG) devices. The machine learning pipeline consists of a Butterworth filter, two wavelet convolutional neural networks (WaveletCNNs) autoencoders, an optional QRS complex inverter, a Monte Carlo k-nearest neighbours (k-NN), and a convolutional long short-term memory (ConvLSTM). WaveletCNN autoencoders filter out electrode contact noise, instrumentation noise, and motion artifact noise by using the advantages of wavelet filters and convolutional neural networks. The QRS complex inverter flips inverted QRS complexes. Monte Carlo k-NN performs automatic gain control on the ECG signals in order to normalize it. The ConvLSTM executes the final QRS complex detection by using the power of a convolutional neural network and a long short-term memory. The MIT-BIH, the European ST-T, and the Long Term ST database Noise Stress Test databases provide the training and testing ECG recordings. The proposed machine learning pipeline performs 3 standard deviations better than the state of the art QRS complex detection algorithms in terms of F 1 score for very noisy environments.

Electrocardiogram and Phonocardiogram Monitoring System for Cardiac Auscultation

Heart-sound auscultation is a rapid and fundamental technique used for examining the cardiovascular system. The main components of heart sounds are the first and second heart sounds. Discriminating these heart sounds under the presence of additional heart sounds and murmurs will be difficult. To recognize these signals efficiently, this study proposes a monitoring system with phonocardiogram and electrocardiogram. This system has two key points. The first is chip implementation, including capacitor coupled amplifier, transimpedance amplifier, high-pass sigma-delta modulator, and digital signal processing block. The chip in the system is fabricated in 0.18 μm standard complementary metal-oxide-semiconductor process. The second is a software application on smartphones for heart-related physiological signal recording, display, and identification. A wavelet-based QRS complex detection algorithm verified by MIT/BIH Arrhythmia Database is also proposed. The overall measured positive prediction, sensitivity, and error rate of the proposed algorithm are 99.90%, 99.82%, and 0.28%, respectively. During auscultation, doctors may refer to these physiological signals displayed on the smartphone and simultaneously listen to the heart sounds to diagnose the potential heart disease. By taking advantage of signal visualization and keeping the original diagnosis procedure, the uncertainty existing in heart sounds can be eliminated, and the training period to acquire auscultation skills can be reduced.

A Novel Wavelet-Based Algorithm for Detection of QRS Complex

Accurate QRS detection is an important first step for almost all automatic electrocardiogram (ECG) analyzing systems. However, QRS detection is difficult, not only because of the wide variety of ECG waveforms but also because of the interferences caused by various types of noise. This study proposes an improved QRS complex detection algorithm based on a four-level biorthogonal spline wavelet transform. A noise evaluation method is proposed to quantify the noise amount and to select a lower-noise wavelet detail signal instead of removing high-frequency components in the preprocessing stage. The QRS peaks can be detected by the extremum pairs in the selected wavelet detail signal and the proposed decision rules. The results show the high accuracy of the proposed algorithm, which achieves a 0.25% detection error rate, 99.84% sensitivity, and 99.92% positive prediction value, evaluated using the MIT-BIT arrhythmia database. The proposed algorithm improves the accuracy of QRS detection in comparison with several wavelet-based and non-wavelet-based approaches.

Efficient QRS complex detection algorithm based on Fast Fourier Transform.

An ECG signal, generally filled with noise, when de-noised, enables a physician to effectively determine and predict the condition and health of the heart. This paper aims to address the issue of denoising a noisy ECG signal using the Fast Fourier Transform based bandpass filter. Multi-stage adaptive peak detection is then applied to identify the R-peak in the QRS complex of the ECG signal. The result of test simulations using the MIT/BIH Arrhythmia database shows high sensitivity and positive predictivity (PP) of 99.98 and 99.96% respectively, confirming the accuracy and reliability of proposed algorithm for detecting R-peaks in the ECG signal.

QRS Complex Detection and Measurement Algorithms for Multichannel ECGs in Cardiac Resynchronization Therapy Patients.

We developed an automated approach for QRS complex detection and QRS duration (QRSd) measurement that can effectively analyze multichannel electrocardiograms (MECGs) acquired during abnormal conduction and pacing in heart failure and cardiac resynchronization therapy (CRT) patients to enable the use of MECGs to characterize cardiac activation in such patients. The algorithms use MECGs acquired with a custom 53-electrode investigational body surface mapping system and were validated using previously collected data from 58 CRT patients. An expert cohort analyzed the same data to determine algorithm accuracy and error. The algorithms: 1) detect QRS complexes; 2) identify complexes of the most prevalent morphology and morphologic outliers; and 3) determine the array-specific (i.e., anterior and posterior) and global QRS complex onsets, offsets, and durations for the detected complexes. The QRS complex detection algorithm had a positive predictivity and sensitivity of ≥96% for complex detection and classification. The absolute QRSd error was 17 ± 14 ms, or 12%, for array-specific QRSd and 12 ± 10 ms, or 8%, for global QRSd. The absolute global QRSd error (12 ms) was less than the interobserver variation in that measurement (15 ± 10 ms). The sensitivity, positive predictivity, and error of the algorithms were similar to the values reported for current state-of-the-art algorithms designed for and limited to simpler data sets and conduction patterns and within the variation found in clinical 12-lead ECG QRSd measurement techniques. These new algorithms permit accurate, real-time analysis of QRS complex features in MECGs in patients with conduction disorders and/or pacing.

Algorithm for detection the QRS complexes based on support vector machine

The efficiency of computer ECG analysis depends on the accurate detection of QRS-complexes. This paper presents an algorithm for QRS complex detection based of support vector machine (SVM). The proposed algorithm is evaluated on annotated standard databases such as MIT-BIH Arrhythmia database. The QRS detector obtained a sensitivity Se = 98.32% and specificity Sp = 95.46% for MIT-BIH Arrhythmia database. This algorithm can be used as the basis for the software to diagnose electrical activity of the heart.