Assessing ECG signal quality indices to discriminate ECGs with artefacts from pathologically different arrhythmic ECGs

Daluwatte, Chathuri; Johannesen, Lars; Galeotti, Loriano; Vicente, José; Strauss, David G.; Scully, Christopher G.

doi:10.1088/0967-3334/37/8/1370

Cited by 28 publications

(13 citation statements)

References 21 publications

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“…Due to the low level of agreement between the annotators of the CinC dataset and the lack of labelling in the Stress dataset we performed our own (re)labelling procedure. Relabelling (parts) of a freely available dataset was already done by other authors [21], [27], [28].…”

Section: Discussionmentioning

confidence: 99%

Artefact detection and quality assessment of ambulatory ECG signals

Moeyersons

Smets

Morales

et al. 2019

Computer Methods and Programs in Biomedicine

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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.

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Section: Discussionmentioning

confidence: 99%

Artefact detection and quality assessment of ambulatory ECG signals

Moeyersons

Smets

Morales

et al. 2019

Computer Methods and Programs in Biomedicine

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“…After aligning by the detected QRS peaks, average template matching correlation coefficient [10] with the threshold of 0.5 was used as SQI to identify noisy data. This measure had the highest area under the receiver operating characteristic (ROC) curve for discriminating between artefacts and arrhythmic ECG [11].…”

Section: Qrs Detection and Signal Quality Assessmentmentioning

confidence: 99%

Densely Connected Convolutional Networks and Signal Quality Analysis to Detect Atrial Fibrillation Using Short Single-Lead ECG Recordings

Rubin

Parvaneh

Rahman

et al. 2017

Computing in Cardiology Conference (CinC)

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The development of new technology such as wearables that record high-quality single channel ECG, provides an opportunity for ECG screening in a larger population, especially for atrial fibrillation screening. The main goal of this study is to develop an automatic classification algorithm for normal sinus rhythm (NSR), atrial fibrillation (AF), other rhythms (O), and noise from a single channel short ECG segment (9-60 seconds). For this purpose, signal quality index (SQI) along with dense convolutional neural networks was used. Two convolutional neural network (CNN) models (main model that accepts 15 seconds ECG and secondary model that processes 9 seconds shorter ECG) were trained using the training data set. If the recording is determined to be of low quality by SQI, it is immediately classified as noisy. Otherwise, it is transformed to a time-frequency representation and classified with the CNN as NSR, AF, O, or noise. At the final step, a feature-based post-processing algorithm classifies the rhythm as either NSR or O in case the CNN model's discrimination between the two is indeterminate. The best result achieved at the official phase of the PhysioNet/CinC challenge on the blind test set was 0.80 (F1 for NSR, AF, and O were 0.90, 0.80, and 0.70, respectively).

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“…Finally, the last two articles in this review (Tsimenidis and Murray (2016); Daluwatte et al (2016)) did not attempt to reduce the number of false alarms, but rather provide some useful insights into the relationship between signal quality metrics and false alarm rates.…”

Section: Review Of Articles In the Special Issuementioning

confidence: 99%

False alarm reduction in critical care

et al. 2016

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High false alarm rates in the ICU decrease quality of care by slowing staff response times while increasing patient delirium through noise pollution. The 2015 Physio-Net/Computing in Cardiology Challenge provides a set of 1,250 multi-parameter ICU data segments associated with critical arrhythmia alarms, and challenges the general research community to address the issue of false alarm suppression using all available signals. Each data segment was 5 minutes long (for real time analysis), ending at the time of the alarm. For retrospective analysis, we provided a further 30 seconds of data after the alarm was triggered. A total of 750 data segments were made available for training and 500 were held back for testing. Each alarm was reviewed by expert annotators, at least two of whom agreed that the alarm was either true or false. Challenge participants were invited to submit a complete, working algorithm to distinguish true from false alarms, and received a score based on their program's performance on the hidden test set. This score was based on the percentage of alarms correct, but with a penalty that weights the suppression of true alarms five times more heavily than acceptance of false alarms. We provided three example entries based on well-known, open source signal processing algorithms, to serve as a basis for comparison and as a starting point for participants to develop their own code. A total of 38 teams submitted a total of 215 entries in this year's Challenge. This editorial reviews the background issues for this Challenge, the design of the Challenge itself, the key achievements, and the follow-up research generated as a result of the Challenge, published in the concurrent special issue of Physiological Measurement. Additionally we make some recommendations for future changes in the field of patient monitoring as a result of the Challenge.

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Assessing ECG signal quality indices to discriminate ECGs with artefacts from pathologically different arrhythmic ECGs

Cited by 28 publications

References 21 publications

Artefact detection and quality assessment of ambulatory ECG signals

Artefact detection and quality assessment of ambulatory ECG signals

Densely Connected Convolutional Networks and Signal Quality Analysis to Detect Atrial Fibrillation Using Short Single-Lead ECG Recordings

False alarm reduction in critical care

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