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

Moeyersons, Jonathan; Amoni, Matthew; Huffel, Sabine Van; Willems, Rik; Varon, Carolina

doi:10.7717/peerj-cs.226

Cited by 42 publications

(35 citation statements)

References 18 publications

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“…At first, artefacts within the ECG signal were automatically detected and removed after visual inspection [24]. Next, an initial automatic R-peak detection was performed using an algorithm described in [25] and wrong detections were manually corrected. The R-peaks were used to generate the HR, from which the HR parameters were calculated.…”

Section: Feature Extractionmentioning

confidence: 99%

Wearable Monitoring and Interpretable Machine Learning Can Objectively Track Progression in Patients during Cardiac Rehabilitation

Cannière

Corradi

Smeets

et al. 2020

Sensors

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Cardiovascular diseases (CVD) are often characterized by their multifactorial complexity. This makes remote monitoring and ambulatory cardiac rehabilitation (CR) therapy challenging. Current wearable multimodal devices enable remote monitoring. Machine learning (ML) and artificial intelligence (AI) can help in tackling multifaceted datasets. However, for clinical acceptance, easy interpretability of the AI models is crucial. The goal of the present study was to investigate whether a multi-parameter sensor could be used during a standardized activity test to interpret functional capacity in the longitudinal follow-up of CR patients. A total of 129 patients were followed for 3 months during CR using 6-min walking tests (6MWT) equipped with a wearable ECG and accelerometer device. Functional capacity was assessed based on 6MWT distance (6MWD). Linear and nonlinear interpretable models were explored to predict 6MWD. The t-distributed stochastic neighboring embedding (t-SNE) technique was exploited to embed and visualize high dimensional data. The performance of support vector machine (SVM) models, combining different features and using different kernel types, to predict functional capacity was evaluated. The SVM model, using chronotropic response and effort as input features, showed a mean absolute error of 42.8 m (±36.8 m). The 3D-maps derived using the t-SNE technique visualized the relationship between sensor-derived biomarkers and functional capacity, which enables tracking of the evolution of patients throughout the CR program. The current study showed that wearable monitoring combined with interpretable ML can objectively track clinical progression in a CR population. These results pave the road towards ambulatory CR.

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Section: Feature Extractionmentioning

confidence: 99%

Wearable Monitoring and Interpretable Machine Learning Can Objectively Track Progression in Patients during Cardiac Rehabilitation

Cannière

Corradi

Smeets

et al. 2020

Sensors

Self Cite

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“…Then, the R-waves are detected by means of a wavelet-based method [6]. Detections are manually corrected using the R-DECO application [7]. Once ECG detections are correct, the RR interval is defined by the time between two consecutive R-waves, and the normal-to-normal (NN) interval is defined after correcting ectopic beats [8].…”

Section: Hrv Estimationmentioning

confidence: 99%

“…An ad hoc algorithm is implemented to delineate the BP signal. The SAP values are essentially detected using a pulse detection algorithm based on a low pass differentiator (LPD) filter [9], and a time-varying threshold defined by an enveloping procedure [7].…”

Section: Sap Estimation and Brs Analysismentioning

confidence: 99%

Baroreflex Sensitivity Evolution before Weaning From Mechanical Ventilation

Armanac

Hernando

Lázaro

et al. 2020

2020 Computing in Cardiology Conference (CinC)

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Weaning is the process of withdrawing mechanical ventilation at the Intensive Care Units. The problem is that around 20% of weaned patients were not actually ready for discontinuation. Studies suggest that vagal dysfunction is lower in patients successfully weaned. Therefore, the Baroreflex Sensitivity (BRS) and Heart Rate Variability (HRV) are estimated to see if they can provide additional information to improve the prediction of weaning outcomes. 9 successfully weaned patients (S-group) and 6 unsuccessfully weaned (F-group) were monitored in the last hour prior to the Spontaneous Breathing Trial. The BRS is estimated through spectral analysis, to obtain the α parameter in the low and high frequency bands, and through the capacity, C, estimated by the Bivariate Phase Rectified Signal Average (BPRSA) method. The current clinic parameters of weaning readiness do not show statistical differences. However, the capacity to changes of the BRS, C, estimated via BPRSA, exhibits significant differences between the two groups. Negative values of C, and with higher absolute values, were obtained for the S-group. Temporal indices of HRV also show differences, but not significant. These results suggest that BRS should be further explored for predicting weaning outcomes.

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“…Currently, several software solutions with manual annotation are available [ 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 ]. However, to the best of the authors’ knowledge none of these tools meet all requirements listed above.…”

Section: Introductionmentioning

confidence: 99%

“…However, to the best of the authors’ knowledge none of these tools meet all requirements listed above. An often-encountered limitation is that they only work for one signal type—predominantly, ECG [ 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 ] or respiration [ 15 ]. Furthermore, the list of characteristic points is predefined: a user can annotate a single fiducial (R-peak) [ 10 , 14 ] or an extended, but fixed set of points (P, Q, R, S, and T waves) [ 12 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

Platform for Analysis and Labeling of Medical Time Series

Fedjajevs

Groenendaal

Agell

et al. 2020

Sensors

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Reliable and diverse labeled reference data are essential for the development of high-quality processing algorithms for medical signals, such as electrocardiogram (ECG) and photoplethysmogram (PPG). Here, we present the Platform for Analysis and Labeling of Medical time Series (PALMS) designed in Python. Its graphical user interface (GUI) facilitates three main types of manual annotations—(1) fiducials, e.g., R-peaks of ECG; (2) events with an adjustable duration, e.g., arrhythmic episodes; and (3) signal quality, e.g., data parts corrupted by motion artifacts. All annotations can be attributed to the same signal simultaneously in an ergonomic and user-friendly manner. Configuration for different data and annotation types is straightforward and flexible in order to use a wide range of data sources and to address many different use cases. Above all, configuration of PALMS allows plugging-in existing algorithms to display outcomes of automated processing, such as automatic R-peak detection, and to manually correct them where needed. This enables fast annotation and can be used to further improve algorithms. The GUI is currently complemented by ECG and PPG algorithms that detect characteristic points with high accuracy. The ECG algorithm reached 99% on the MIT/BIH arrhythmia database. The PPG algorithm was validated on two public databases with an F1-score above 98%. The GUI and optional algorithms result in an advanced software tool that allows the creation of diverse reference sets for existing datasets.

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

Cited by 42 publications

References 18 publications

Wearable Monitoring and Interpretable Machine Learning Can Objectively Track Progression in Patients during Cardiac Rehabilitation

Wearable Monitoring and Interpretable Machine Learning Can Objectively Track Progression in Patients during Cardiac Rehabilitation

Baroreflex Sensitivity Evolution before Weaning From Mechanical Ventilation

Platform for Analysis and Labeling of Medical Time Series

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