Non-invasive Fetal ECG Signal Quality Assessment based on Unsupervised Learning Approach

Shi, Xintong; Yamamoto, Kohei; Ohtsuki, Tomoaki; Matsui, Yutaka; Owada, Kenji

doi:10.1109/embc48229.2022.9870908

Cited by 7 publications

(5 citation statements)

References 17 publications

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“…showing that the level of AI was as similar as the obstetricians and detecting the errors [20] . AI has its inherent fallacy because the proposed algorithm settled by the clinicians, leading to wrong interpretation [11] . Further, absence of a xed pattern of atypical NST pattern might be another hypothesis.…”

Section: Discussionmentioning

confidence: 99%

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A robust artificial intelligence method detects almost non-reactive Non-stress pattern: What we expect?

Zhu,

Li,

Wang

et al. 2023

Preprint

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Objective To compare interpretation of prenatal non-stress (NST) pattern between obstetricians and artificial intelligence (AI), and to determine the degree of agreement of AI system. Methods One thousand records of prenatal NST pattern with 20 to 30 minutes were interpreted using AI system, as well as visual interpretation of five obstetricians, to explore the agreement and accuracy of AI system. Weighted kappa was used to assess reliability of AI for interpretation of prenatal NST pattern. Results A total of 967 cases enroll in this study. Moderate agreement (kappa, 0.48) was found among the five obstetricians for FHR pattern during antepartum period. The AI system recognized NST pattern like obstetricians, with a moderate kappa coefficient of agreement of 0.42. When AI was used to assess the strong consistent set of inter-obstetricians, the agreement was high (kappa, 0.75). AI could identify major non-reactive NST pattern, with high sensitivity of 91.67%. A concordant identification was observed 71.76% of preterm cases and 66.05% of term cases. Conclusion Based on the visual interpretation of obstetricians, AI was excellent for antepartum FHR monitoring interpretation, regardless gestational age. Further, AI showed a competitive ability to identify non-reactive NST pattern and the potential avoidance of unnecessary clinical intervention.

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Section: Discussionmentioning

confidence: 99%

“…To overcome the present limitations of usage of NST, interpretation by AI has been implemented [10] . Signal processing and pattern recognition techniques paved the way for AI to sept over inconsistency in NST interpretation [11] . Every coin has two sides, and arti cial intelligence is no exception.…”

Section: Introductionmentioning

confidence: 99%

A robust artificial intelligence method detects almost non-reactive Non-stress pattern: What we expect?

Zhu,

Li,

Wang

et al. 2023

Preprint

View full text Add to dashboard Cite

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“…Entropy is a measure of unpredictability or randomness since it quantifies how much the probability density function of a signal varies from a uniform distribution [48]. It has been used successfully for measuring signal quality of pulse oximetry [49], ECG [50], and EEG [48] signals. Here we used the spectral entropy of the form…”

Section: Standard Deviation Of the Upper And Lower Envelopementioning

confidence: 99%

Quantifying the Suitability of Biosignals Acquired During Surgery for Multimodal Analysis

Idrobo-Ávila,

Bognár,

Krefting

et al. 2024

IEEE Open J. Eng. Med. Biol.

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Goal: Recently, large datasets of biosignals acquired during surgery became available. As they offer multiple physiological signals measured in parallel, multimodal analysiswhich involves their joint analysis -can be conducted and could provide deeper insights than unimodal analysis based on a single signal. However, it is unclear what percentage of intraoperatively acquired data is suitable for multimodal analysis. Due to the large amount of data, manual inspection and labelling into suitable and unsuitable segments are not feasible. Nevertheless, multimodal analysis is performed successfully in sleep studies since many years as their signals have proven suitable. Hence, this study evaluates the suitability to perform multimodal analysis on a surgery dataset (VitalDB) using a multi-center sleep dataset (SIESTA) as reference. Methods: We applied widely known algorithms entitled "signal quality indicators" to the common biosignals in both datasets, namely electrocardiography, electroencephalography, and respiratory signals split in segments of 10s duration. As there are no multimodal methods available, we used only unimodal signal quality indicators. In case, all three signals were determined as being adequate by the indicators, we assumed that the whole signal segment was suitable for multimodal analysis. Results: 82% of SIESTA and 72% of VitalDB are suitable for multimodal analysis. Unsuitable signal segments exhibit constant or physiologically unreasonable values. Histogram examination indicated similar signal quality distributions between the datasets, albeit with potential statistical biases due to different measurement setups. A correlation was found between entropy outliers and non-suitable electrocardiographic signals. Conclusions: The majority of data within the surgery dataset VitalDB demonstrated suitability for multimodal analysis.

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“…Furthermore, both the time-varying fetal orientation and their movements make some channels useless in the fECG extraction process. As such, SQA has been used on fECG signals after their extraction to improve fHR estimation by signal quality metrics and artificial intelligence tools (Andreotti et al, 2017;Varanini et al, 2017;Fotiadou et al, 2021;Shi et al, 2022), but also to identify useful independent components after blind source separation algorithms for optimal fECG signal recovery (Karimi Rahmati et al, 2017;Jamshidian-Tehrani and Sameni, 2018). Nonetheless, SQA applied on the extracted fECG is biased by the effectiveness of the algorithms adopted for fECG extraction or fetal QRS detection (Mertes et al, 2022;Shi et al, 2022), which the SQI identification was based on.…”

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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Non-invasive Fetal ECG Signal Quality Assessment based on Unsupervised Learning Approach

Cited by 7 publications

References 17 publications

A robust artificial intelligence method detects almost non-reactive Non-stress pattern: What we expect?

A robust artificial intelligence method detects almost non-reactive Non-stress pattern: What we expect?

Quantifying the Suitability of Biosignals Acquired During Surgery for Multimodal Analysis

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

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