Artefact detection and quality assessment of ambulatory ECG signals

Moeyersons, Jonathan; Smets, Elena; Morales, John; Villa, Amalia; Raedt, W. De; Testelmans, Dries; Buyse, Bertien; Hoof, Chris Van; Willems, Rik; Huffel, Sabine Van; Varon, Carolina

doi:10.1016/j.cmpb.2019.105050

Cited by 52 publications

(53 citation statements)

References 22 publications

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“…In total, 1218, 4711, and 53553 min were collected for the Drivers, Fantasia, and Sleep datasets, respectively. Then, a signal quality index (SQI), denoted q(k), was computed to quantify the presence of artifacts and noise, using the algorithm proposed in 45 . The SQI ranges from 0 to 100, where higher values correspond to better signal quality.…”

mentioning

confidence: 99%

A Comparative Study of ECG-derived Respiration in Ambulatory Monitoring using the Single-lead ECG

Varon

Morales

Lázaro

et al. 2020

Sci Rep

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Cardiorespiratory monitoring is crucial for the diagnosis and management of multiple conditions such as stress and sleep disorders. Therefore, the development of ambulatory systems providing continuous, comfortable, and inexpensive means for monitoring represents an important research topic. Several techniques have been proposed in the literature to derive respiratory information from the ECG signal. Ten methods to compute single-lead ECG-derived respiration (EDR) were compared under multiple conditions, including different recording systems, baseline wander, normal and abnormal breathing patterns, changes in breathing rate, noise, and artifacts. Respiratory rates, wave morphology, and cardiorespiratory information were derived from the ECG and compared to those extracted from a reference respiratory signal. Three datasets were considered for analysis, involving a total 59 482 onemin, single-lead ECG segments recorded from 156 subjects. The results indicate that the methods based on QRS slopes outperform the other methods. This result is particularly interesting since simplicity is crucial for the development of ECG-based ambulatory systems. Continuous monitoring of respiration plays a key role in the detection and management of different conditions, such as stress 1,2 and sleep disorders 3,4. Biomarkers like respiratory rate, breathing phases, and tidal volume are relevant for the detection of mental stress 1 , anxiety 2 , and sleep apnea events 5,6. In addition, the coupling between respiration and heart rate has been used as a biomarker for the aforementioned conditions 7,8 as well as for the understanding of the interactions between the cardiac and respiratory systems 9. Despite the importance of monitoring respiration, its recording requires the use of invasive and intrusive sensors like thermistors, spirometers, and respiratory belts. Even though these sensors are regularly used, for instance during polysomnographic recordings, their use in ambulatory systems is very limited since they not only interfere with natural breathing, but are often associated with high costs and low comfort. Different studies have shown that the respiratory rate, and even the respiratory wave morphology, can be approximated by ECG-derived respiration (EDR) 5,10-20. The derived signal is defined by certain morphological properties of the ECG particularly influenced by respiration. This influence can be explained by the respiratory-induced chest movements that cause changes in the position of the electrodes relative to the cardiac vector 21. Moreover, the filling and emptying of the lungs cause changes in the electrical impedance of the chest. As a result, the morphology of the ECG is modulated by respiration.

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confidence: 99%

A Comparative Study of ECG-derived Respiration in Ambulatory Monitoring using the Single-lead ECG

Varon

Morales

Lázaro

et al. 2020

Sci Rep

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“…The ECG signal quality assessment tool based on the weighed sum of the posterior probability of a RUSBoost classifier was previously described in [6]. Briefly, it consists of three steps: pre-processing, feature extraction and quality indication.…”

Section: Ecg Quality Assessmentmentioning

confidence: 99%

“…Redmond et al stated that while discrete quality labels facilitate the creation of an expert labeled gold standard training set, it could be argued that signal quality would more naturally occupy a continuum of quality values [3]. Therefore, starting from a previously proposed binary quality indication algorithm, based on the autocorrelation function (ACF) and a RUSBoost classifier, we suggested to use the weighed sum of the posterior probability of the clean class as a continuous indication of the signal quality [6].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of a Continuous ECG Quality Indicator Based on the Autocorrelation Function

Moeyersons

Testelmans

Buyse

et al. 2018

2018 Computing in Cardiology Conference (CinC)

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Most electrocardiogram (ECG) signal quality assessment algorithms focus on a two-or multi-level classification. However, it could be argued that signal quality would more naturally occupy a continuum of quality values. Therefore, in previous work we created a continuous quality assessment algorithm based on the autocorrelation function (ACF). This paper evaluates this algorithm on a simulated dataset with five noise levels and known signal-to-noise ratios (SNR). The simulated data was created by selecting clean ECG segments of a polysomnographic dataset with an in-house quality algorithm, and adding calibrated amounts of two types of realistic ECG noise from the MIT-BIH Noise Stress Test Database (NSTDB). Both Electrode Motion (EM) and Movement Artefacts (MA) were considered. Using only three features and a binary training set, we have shown significant quality decreases per noise level for both types of added noise. Despite this finding, also significant intra-level differences were observed, indicating a change in response according to the type of noise. Adding other features might help to converge the quality scores. By presenting the users with a continuous quality score, they are given the possibility to define the preferred level of quality according to the study objective.

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“…The last feature was extracted from each ECG segment of 60 s, and it corresponds to the signal quality indicator (SQI) proposed in [10]. This indicator was used to de-PSfrag replacements cad Figure 1.…”

Section: Feature Extractionmentioning

confidence: 99%

“…The first comparison was done using the full night recordings, and the mean δ(G) for the full night was computed using only the clean segments. These clean segments corresponded to those with SQI > 50, since they were classified as clean by the algorithm proposed in [10].…”

Section: 4mentioning

confidence: 99%

The Cardiorespiratory Graph in Sleep Apnea and Associated Comorbidities

Varon

Deviaene

Hendrikx

et al. 2018

2018 Computing in Cardiology Conference (CinC)

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The severity of sleep apnea is often assessed using the apnea/hypopnea index (AHI), which is known to be inaccurate in the phenotyping of apnea patients. Hence, better approaches are needed to characterize these patients and to allow cardiovascular risk stratification. In this context, this work studies the cardiorespiratory interactions in patients suffering from both sleep apnea and apnea associated comorbidities by means of graph theory and kernel methods. Results indicate that the total connectivity of the cardiorespiratory graph is significantly (p < 0.01) reduced with higher AHI. Moreover, in patients with apnea associated comorbidities, this connectivity appears to be significantly reduced around apnea events. These results are in line with studies that report stronger oxygen desaturations in patients with apnea associated comorbidities, and more unstable control systems, which could be used for a better characterization of apnea patients.

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Artefact detection and quality assessment of ambulatory ECG signals

Cited by 52 publications

References 22 publications

A Comparative Study of ECG-derived Respiration in Ambulatory Monitoring using the Single-lead ECG

A Comparative Study of ECG-derived Respiration in Ambulatory Monitoring using the Single-lead ECG

Evaluation of a Continuous ECG Quality Indicator Based on the Autocorrelation Function

The Cardiorespiratory Graph in Sleep Apnea and Associated Comorbidities

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