Improvement of ECG signal quality measurement using correlation and diversity-based approaches

Martinez-Tabares, F. J.; Espinosa-Oviedo, Jairo Jose; Castellanos-Domínguez, G.

doi:10.1109/embc.2012.6346916

Cited by 15 publications

(5 citation statements)

References 3 publications

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“…To perform a more extensive and accurate comparative performance evaluation, the base performance of the proposed system is compared with the four existing algorithms, (Johannesen, 2011 ; Clifford et al, 2012 ; Martínez-Tabares et al, 2012 ; Shahriari et al, 2017 ), all of which adopted the Database D2, used single-lead ECG signal, made the comparison more persuasive. The experimental results are presented in Table 6 .…”

Section: Resultsmentioning

confidence: 99%

SQI Quality Evaluation Mechanism of Single-Lead ECG Signal Based on Simple Heuristic Fusion and Fuzzy Comprehensive Evaluation

Zhao

Zhang

2018

Front. Physiol.

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For both the acquisition of mobile electrocardiogram (ECG) devices and early warning and diagnosis of clinical work, high-quality ECG signals is particularly important. We describe an effective system which could be deployed as a stand-alone signal quality assessment algorithm for vetting the quality of ECG signals. The proposed ECG quality assessment method is based on the simple heuristic fusion and fuzzy comprehensive evaluation of the SQIs. This method includes two modules, i.e., the quantification and extraction of Signal Quality Indexes (SQIs) for different features, intelligent assessment and classification. First, simple heuristic fusion is executed to extract SQIs and determine the following SQIs: R peak detection match qSQI, QRS wave power spectrum distribution pSQI, kurtosis kSQI, and baseline relative power basSQI. Then, combined with Cauchy distribution, rectangular distribution and trapezoidal distribution, the membership function of SQIs was quantified, and the fuzzy vector was established. The bounded operator was selected for fuzzy synthesis, and the weighted membership function was used to perform the assessment and classification. The performance of the proposed method was tested on the database from Physionet ECG database, with an accuracy (Acc) of 97.67%, sensitivity (Se) of 96.33% and specificity (Sp) of 98.33% on the training set. Testing against the test datasets resulted in scores of 94.67, 90.33, and 93.00%, respectively. There's no gold standard exists for determining the quality of ECGs. However, the proposed algorithm discriminates between high- and poor-quality ECGs, which could aid in ECG acquisition for mobile ECG devices, early clinical diagnosis and early warning.

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Section: Resultsmentioning

confidence: 99%

SQI Quality Evaluation Mechanism of Single-Lead ECG Signal Based on Simple Heuristic Fusion and Fuzzy Comprehensive Evaluation

Zhao

Zhang

2018

Front. Physiol.

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“…The work [29] presented a quality ECG classifier based on residuals among noticed and filtered signals and among effective subset linear estimations without filtered data and constant term. The coefficients of prediction or estimation have been identified from the acceptable quality of ECG using the robust model.…”

Section: Related Workmentioning

confidence: 99%

Quality Assessment of Ambulatory Electrocardiogram Signals by Noise Detection using Optimal Binary Classification

Supraja

Rao²,

Giriprasad

2022

IJRITCC

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In order to improve the diagnostic capability in Ambulatory Electrocardiogram signal and to reduce the noise signal impacts, there is a need for more robust models in place. In terms of improvising to the existing solutions, this article explores a novel binary classifier that learns from the features optimized by fusion of diversity assessment measures, which performs Quality Assessment of Ambulatory Electrocardiogram Signals (QAAES) by Noise Detection. The performance of the proposed model QAAES has been scaled by comparing it with contemporary models. Concerning performance analysis, the 10-fold cross-validation has been carried on a benchmark dataset. The results obtained from experiments carried on proposed and other contemporary models for cross-validation metrics have been compared to signify the sensitivity, specificity, and noise detection accuracy.

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“…In the past, SQA has been widely exploited on adult ECGs in order to reject those signals suffering from unacceptable noise level, and as such possibly leading to incorrect clinical interpretations (Del Rio et al, 2011;Satija et al, 2018). Different signal quality indexes (SQIs) were proposed and adopted, to allow for automatic accurate estimation of R peak (Johnson et al, 2015) and robust HR estimation (Li et al, 2007;Orphanidou et al, 2015), to reduce alarms associated to false arrhythmia and HR (Allen and Murray, 1996;Wang, 2002;Li and Clifford, 2012;Behar et al, 2013;Daluwatte et al, 2016;Shahriari et al, 2018), or, more generally, to identify clinically acceptable ECGs (Behar et al, 2012;Clifford et al, 2012;Di Marco et al, 2012;Zhao and Zhang, 2018), even in real-time monitoring mobile devices (Redmond et al, 2008;Langley et al, 2011;Moody, 2011;Silva et al, 2011;Hayn et al, 2012;Martinez-Tabares et al, 2012;Liu et al, 2018), or along with their noise level quantification (Johannesen and Galeotti, 2012;.…”

Section: Introductionmentioning

confidence: 99%

Automatic signal quality assessment of raw trans-abdominal biopotential recordings for non-invasive fetal electrocardiography

Baldazzi

Sulas²,

Vullings³

et al. 2023

Front. Bioeng. Biotechnol.

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Introduction: Wearable monitoring systems for non-invasive multi-channel fetal electrocardiography (fECG) can support fetal surveillance and diagnosis during pregnancy, thus enabling prompt treatment. In these embedded systems, power saving is the key to long-term monitoring. In this regard, the computational burden of signal processing methods implemented for the fECG extraction from the multi-channel trans-abdominal recordings plays a non-negligible role. In this work, a supervised machine-learning approach for the automatic selection of the most informative raw abdominal recordings in terms of fECG content, i.e., those potentially leading to good-quality, non-invasive fECG signals from a low number of channels, is presented and evaluated.Methods: For this purpose, several signal quality indexes from the scientific literature were adopted as features to train an ensemble tree classifier, which was asked to perform a binary classification between informative and non-informative abdominal channels. To reduce the dimensionality of the classification problem, and to improve the performance, a feature selection approach was also implemented for the identification of a subset of optimal features. 10336 5-s long signal segments derived from a real dataset of multi-channel trans-abdominal recordings acquired from 55 voluntary pregnant women between the 21st and the 27th week of gestation, with healthy fetuses, were adopted to train and test the classification approach in a stratified 10-time 10-fold cross-validation scheme. Abdominal recordings were firstly pre-processed and then labeled as informative or non-informative, according to the signal-to-noise ratio exhibited by the extracted fECG, thus producing a balanced dataset of bad and good quality abdominal channels.Results and Discussion: Classification performance revealed an accuracy above 86%, and more than 88% of those channels labeled as informative were correctly identified. Furthermore, by applying the proposed method to 50 annotated 24-channel recordings from the NInFEA dataset, a significant improvement was observed in fetal QRS detection when only the channels selected by the proposed approach were considered, compared with the use of all the available channels. As such, our findings support the hypothesis that performing a channel selection by looking directly at the raw abdominal signals, regardless of the fetal presentation, can produce a reliable measurement of fetal heart rate with a lower computational burden.

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Improvement of ECG signal quality measurement using correlation and diversity-based approaches

Cited by 15 publications

References 3 publications

SQI Quality Evaluation Mechanism of Single-Lead ECG Signal Based on Simple Heuristic Fusion and Fuzzy Comprehensive Evaluation

SQI Quality Evaluation Mechanism of Single-Lead ECG Signal Based on Simple Heuristic Fusion and Fuzzy Comprehensive Evaluation

Quality Assessment of Ambulatory Electrocardiogram Signals by Noise Detection using Optimal Binary Classification

Automatic signal quality assessment of raw trans-abdominal biopotential recordings for non-invasive fetal electrocardiography

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