HighlightsA novel way for ECG quality assessment is proposed, based on the posterior probability of an artefact detection classifier.A good performance was obtained when testing the classifier on two independent (re)labelled datasets, thereby showing its robustness. The performance was better, compared to a heuristic method and comparable to another machine learning algorithm.A significant correlation was observed between the proposed quality assessment and the annotators level of agreement.Significant decreases in quality level were observed for different noise levels.

Many of the existing electrocardiogram (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 (GUI) for the detection and correction of R-peaks. Within R-DECO, the R-peaks are detected by using a detection algorithm which uses an envelope-based procedure. This procedure flattens the ECG and enhances the QRS-complexes. The algorithm obtained an overall sensitivity of 99.60% and positive predictive value 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 GUI and the analysis results can be exported as Matlab or Excel files. The software is publicly available. Through its easy-to-use GUI, R-DECO allows both clinicians and researchers to use all functionalities, without previous knowledge.

We propose and evaluate a method to estimate a respiratory signal from ungated cardiac magnetic resonance (CMR) images. Ungated CMR images were acquired in 5 subjects who performed exercise at different intensity levels under different physiological conditions while breathing freely. The respiratory motion was estimated by applying principal components analysis (PCA). A sign correction procedure was developed to correctly define inspiration and expiration, based on either tracking of the diaphragmatic motion or estimation of the lung volume or a combination of both. Evaluation was done using a plethysmograph signal as reference. There was a good correspondence between the plethysmograph and the estimated respiratory signals. Respiratory motion was effectively captured by one of the PCA components in 88% of the cases. Moreover, the proposed method successfully estimated the respiratory phase in 91% of the evaluated slices. The pipeline is robust, admitting a slight decline in performance with increased exercise intensity. Respiratory motion was accurately estimated by means of PCA and the application of a sign correction procedure. Our method showed promising results even for acquisitions during exercise where excessive body motion occurs. The proposed method provides a way to extract the respiratory signal from ungated CMR images, at rest as well as during exercise, in a fully unsupervised fashion, which may reduce the clinician's workload drastically.

Impedance pneumography has been suggested as an ambulatory technique for the monitoring of respiratory diseases. However, its ambulatory nature makes the recordings more prone to noise sources. It is important that such noisy segments are identified and removed, since they could have a huge impact on the performance of data-driven decision support tools. In this study, we investigated the added value of machine learning algorithms to separate clean from noisy bio-impedance signals. We compared three approaches: a heuristic algorithm, a feature-based classification model (SVM) and a convolutional neural network (CNN). The dataset consists of 47 chronic obstructive pulmonary disease patients who performed an inspiratory threshold loading protocol. During this protocol, their respiration was recorded with a bio-impedance device and a spirometer, which served as a gold standard. Four annotators scored the signals for the presence of artefacts, based on the reference signal. We have shown that the accuracy of both machine learning approaches (SVM: 87.77 ± 2.64% and CNN: 87.20 ± 2.78%) is significantly higher, compared to the heuristic approach (84.69 ± 2.32%). Moreover, no significant differences could be observed between the two machine learning approaches. The feature-based and neural network model obtained a respective AUC of 92.77±2.95% and 92.51±1.74%. These findings show that a data-driven approach could be beneficial for the task of artefact detection in respiratory thoracic bio-impedance signals.

Transfer entropy (TE) has been used to identify and quantify interactions between physiological systems. Different methods exist to estimate TE, but there is no consensus about which one performs best in specific applications. In this study, five methods (linear, k-nearest neighbors, fixed-binning with ranking, kernel density estimation and adaptive partitioning) were compared. The comparison was made on three simulation models (linear, nonlinear and linear + nonlinear dynamics). From the simulations, it was found that the best method to quantify the different interactions was adaptive partitioning. This method was then applied on data from a polysomnography study, specifically on the ECG and the respiratory signals (nasal airflow and respiratory effort around the thorax). The hypothesis that the linear and nonlinear components of cardio-respiratory interactions during light and deep sleep change with the sleep stage, was tested. Significant differences, after performing surrogate analysis, indicate an increased TE during deep sleep. However, these differences were found to be dependent on the type of respiratory signal and sampling frequency. These results highlight the importance of selecting the appropriate signals, estimation method and surrogate analysis for the study of linear and nonlinear cardio-respiratory interactions.

Objective: Respiratory sinus arrhythmia (RSA) refers to heart rate oscillations synchronous with respiration, and it is one of the major representations of cardiorespiratory coupling. Its strength has been suggested as a biomarker to monitor different conditions and diseases. Some approaches have been proposed to quantify the RSA, but it is unclear which one performs best in specific scenarios. The main objective of this study is to compare seven state-of-the-art methods for RSA quantification using data generated with a model proposed to simulate and control the RSA. These methods are also compared and evaluated on a real-life application, for their ability to capture changes in cardiorespiratory coupling during sleep. Methods: A simulation model is used to create a dataset of heart rate variability and respiratory signals with controlled RSA, which is used to compare the RSA estimation approaches. To compare the methods objectively in real-life applications, regression models trained on the simulated data are used to map the estimates to the same measurement scale. Results and conclusion: RSA estimates based on cross entropy, time-frequency coherence and subspace projections showed the best performance on simulated data. In addition, these estimates captured the expected trends in the changes in cardiorespiratory coupling during sleep similarly. Significance: An objective comparison of methods for RSA quantification is presented to guide future analyses. Also, the proposed simulation model can be used to compare existing and newly proposed RSA estimates. It is freely accessible online.

Acquisition of capacitively-coupled ECG (ccECG) from daily life scenarios is limited by its sensitivity to motion and its variability in signal quality. 48 features, in combination with different classifiers, were evaluated for quality classification on a dataset of 10000 ccECG segments of 15 seconds. Feature subsets with potential high discriminatory power were identified and evaluated in multiple supervised models, for two classification problems with different tolerance to artefacts. This resulted in balanced accuracies of 94.02% and 92.4%, achieved using a Linear SVM and a fine KNN respectively. These models are useful tools for real-time and offline processing of ccECG signals recorded in real-life scenarios

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...