R-DECO: An open-source Matlab based graphical user interface for the detection and correction of R-peaks

Abstract

Many of the existing ECG toolboxes focus on the derivation of heart rate variability features from RR-intervals. By doing so, they assume correct detection of the QRS-complexes. However, it is highly likely that not all detections are correct. Therefore, it is recommended to visualize the actual R-peak positions in the ECG signal and allow manual adaptations.In this paper we present R-DECO, an easy-to-use graphical user interface for the detection and correction of R-peaks. Within R-DECO, the R-peaks are detected by an adaptation of the Pan-Tompkins algorithm. Instead of using all the pre-processing steps of the latter algorithm, the proposed algorithm uses only an envelope-based procedure to flatten the ECG and enhance the QRS-complexes. The algorithm obtained an overall sensitivity of 99.60% and PPV of 99.69% on the MIT/BIH arrhythmia database.Additionally, R-DECO includes support for several input data formats for ECG signals, three basic filters, the possibility to load other R-peak locations and intuitive methods to correct ectopic, wrong, or missed heartbeats. All functionalities can be accessed via the graphical user interface and the analysis results can be exported as Matlab or Excel files. The software is publicly available.Through its easy-to-use-graphical user interface, R-DECO allows both clinicians and researchers to use all functionalities, without previous knowledge. 2 the cardiologist. It records the electrical activity of the heart, which generates the 3 myocardial contractions. A crucial step in the study of the ECG is the location of the 4 QRS-complexes. These complexes are the most prominent waveforms in the ECG, and 5 contain an enormous amount of information about the state of the heart. This is why 6 the detection of the QRS-complexes constitutes the basis for almost all automated ECG 7 analysis algorithms [1]. Once these have been identified, more elaborated analyses can 8 be performed, such as heart rate variability (HRV).9Four decades of automated QRS detection research has resulted in a variety of 10 methods using different approaches. These methods can be stratified based on 11 derivatives, digital filters, wavelet-transforms, classifiers, etc [2][3][4][5][6]. Despite the wide 12 methodological variety, most of these QRS detectors have the same algorithmic 13 structure. This can be divided in two steps: pre-processing and decision making [1]. 14 In the pre-processing step the QRS-complex is highlighted and the other signal 15 components are suppressed to facilitate the detection. The resulting signal is then used 16 to detect the occurrence of QRS-complexes in the decision making step. This is done by 17 using either fixed or adaptive thresholds. Despite high detection rates, some 18 QRS-complexes remain undetected. Reasons for this might be small amplitudes, wide 19 complexes or contamination by noise [7]. Therefore, in many algorithms an extra 20 post-processing step is added for the exact determination of the temporal location of the 21 detected QRS-complex. 22One of the most estab...

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