A new method for QRS complex detection in multichannel ECG: Application to self-monitoring of fetal health

Varanini, M.; Tartarisco, Gennaro; Balocchi, R.; Macerata, A.; Pioggia, Giovanni; Billeci, Lucia

doi:10.1016/j.compbiomed.2016.04.008

Cited by 33 publications

(33 citation statements)

References 22 publications

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“…We also selected channels 33 and 34, which are the mECG reference channels. It should be noticed that, compared with the PhysioNet CinC 2013 Database, in the FECGSYNDB there are reference mECG channels, so the algorithms do not include the mECG extraction step as our previous implementations [23,34]. …”

Section: Methodsmentioning

confidence: 99%

“…Maternal ECG canceling is one of the most commonly applied technique for fECG extraction and in this study, it is implemented as TS. Conversely, the third algorithm, was first introduced by our group in [34]. …”

Section: Methodsmentioning

confidence: 99%

“…In theory, the number of independent sources is undetermined and always higher than the number of acquired signals, thus ICA method works in a sub-optimal contest and it will be able to separate the independent sources of interest only if their components in the acquired mixed signals have higher power greater than the others. To face this problem, we implemented a second algorithm, the quality index optimization (QIO)-based method, which a priori takes into account specific characteristics of the fECG rather than only the unspecific independence of sources [34]. In this second method, two quality indexes (fQI and mQI) were devised, which discriminate the two components of interest (fECG and mECG, respectively) from noise sources.…”

Section: Introductionmentioning

confidence: 99%

“…In our recent paper [34] this QIO-based method was compared with the ICA-based one on the same dataset extracted from the PhysioNet Challenge 2013 Database and it outperformed the ICA-based approach for most of the records. …”

Section: Introductionmentioning

confidence: 99%

“…However, when comparing the record-by-record performance of the ICA-based and QIO-based methods we observed that, although generally the QIO-based method outperformed the ICA-based one, for some records, the opposite was true [34]. Thus, we hypothesized that a combination of the two approaches could improve the performance in fQRS detection and reduce the number of records with low performance.…”

Section: Introductionmentioning

confidence: 99%

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A Combined Independent Source Separation and Quality Index Optimization Method for Fetal ECG Extraction from Abdominal Maternal Leads

Billeci

Varanini

2017

Sensors

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The non-invasive fetal electrocardiogram (fECG) technique has recently received considerable interest in monitoring fetal health. The aim of our paper is to propose a novel fECG algorithm based on the combination of the criteria of independent source separation and of a quality index optimization (ICAQIO-based). The algorithm was compared with two methods applying the two different criteria independently—the ICA-based and the QIO-based methods—which were previously developed by our group. All three methods were tested on the recently implemented Fetal ECG Synthetic Database (FECGSYNDB). Moreover, the performance of the algorithm was tested on real data from the PhysioNet fetal ECG Challenge 2013 Database. The proposed combined method outperformed the other two algorithms on the FECGSYNDB (ICAQIO-based: 98.78%, QIO-based: 97.77%, ICA-based: 97.61%). Significant differences were obtained in particular in the conditions when uterine contractions and maternal and fetal ectopic beats occurred. On the real data, all three methods obtained very high performances, with the QIO-based method proving slightly better than the other two (ICAQIO-based: 99.38%, QIO-based: 99.76%, ICA-based: 99.37%). The findings from this study suggest that the proposed method could potentially be applied as a novel algorithm for accurate extraction of fECG, especially in critical recording conditions.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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