Bioengineering

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23 pages, 2768 KiB

Open AccessArticle

Deep Learning for Generalized EEG Seizure Detection after Hypoxia–Ischemia—Preclinical Validation

by Hamid Abbasi, Joanne O. Davidson, Simerdeep K. Dhillon, Kelly Q. Zhou, Guido Wassink, Alistair J. Gunn and Laura Bennet

Bioengineering 2024, 11(3), 217; https://doi.org/10.3390/bioengineering11030217 - 24 Feb 2024

Cited by 1 | Viewed by 1722

Abstract

Brain maturity and many clinical treatments such as therapeutic hypothermia (TH) can significantly influence the morphology of neonatal EEG seizures after hypoxia–ischemia (HI), and so there is a need for generalized automatic seizure identification. This study validates efficacy of advanced deep-learning pattern classifiers [...] Read more.

Brain maturity and many clinical treatments such as therapeutic hypothermia (TH) can significantly influence the morphology of neonatal EEG seizures after hypoxia–ischemia (HI), and so there is a need for generalized automatic seizure identification. This study validates efficacy of advanced deep-learning pattern classifiers based on a convolutional neural network (CNN) for seizure detection after HI in fetal sheep and determines the effects of maturation and brain cooling on their accuracy. The cohorts included HI–normothermia term (n = 7), HI–hypothermia term (n = 14), sham–normothermia term (n = 5), and HI–normothermia preterm (n = 14) groups, with a total of >17,300 h of recordings. Algorithms were trained and tested using leave-one-out cross-validation and k-fold cross-validation approaches. The accuracy of the term-trained seizure detectors was consistently excellent for HI–normothermia preterm data (accuracy = 99.5%, area under curve (AUC) = 99.2%). Conversely, when the HI–normothermia preterm data were used in training, the performance on HI–normothermia term and HI–hypothermia term data fell (accuracy = 98.6%, AUC = 96.5% and accuracy = 96.9%, AUC = 89.6%, respectively). Findings suggest that HI–normothermia preterm seizures do not contain all the spectral features seen at term. Nevertheless, an average 5-fold cross-validated accuracy of 99.7% (AUC = 99.4%) was achieved from all seizure detectors. This significant advancement highlights the reliability of the proposed deep-learning algorithms in identifying clinically translatable post-HI stereotypic seizures in 256Hz recordings, regardless of maturity and with minimal impact from hypothermia. Full article

(This article belongs to the Special Issue Fetal-Maternal Monitoring during Pregnancy and Labor: Trends and Opportunities)

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15 pages, 892 KiB

Open AccessArticle

Data-Driven Insights into Labor Progression with Gaussian Processes

by Tilekbek Zhoroev, Emily F. Hamilton and Philip A. Warrick

Bioengineering 2024, 11(1), 73; https://doi.org/10.3390/bioengineering11010073 - 11 Jan 2024

Cited by 1 | Viewed by 1309

Abstract

Clinicians routinely perform pelvic examinations to assess the progress of labor. Clinical guidelines to interpret these examinations, using time-based models of cervical dilation, are not always followed and have not contributed to reducing cesarean-section rates. We present a novel Gaussian process model of [...] Read more.

Clinicians routinely perform pelvic examinations to assess the progress of labor. Clinical guidelines to interpret these examinations, using time-based models of cervical dilation, are not always followed and have not contributed to reducing cesarean-section rates. We present a novel Gaussian process model of labor progress, suitable for real-time use, that predicts cervical dilation and fetal station based on clinically relevant predictors available from the pelvic exam and cardiotocography. We show that the model is more accurate than a statistical approach using a mixed-effects model. In addition, it provides confidence estimates on the prediction, calibrated to the specific delivery. Finally, we show that predicting both dilation and station with a single Gaussian process model is more accurate than two separate models with single predictions. Full article

(This article belongs to the Special Issue Fetal-Maternal Monitoring during Pregnancy and Labor: Trends and Opportunities)

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19 pages, 1757 KiB

Open AccessArticle

Prediction of Fetal Blood Pressure during Labour with Deep Learning Techniques

by John Tolladay, Christopher A. Lear, Laura Bennet, Alistair J. Gunn and Antoniya Georgieva

Bioengineering 2023, 10(7), 775; https://doi.org/10.3390/bioengineering10070775 - 28 Jun 2023

Cited by 2 | Viewed by 1712

Abstract

Our objective is to develop a model for the prediction of minimum fetal blood pressure (FBP) during fetal heart rate (FHR) decelerations. Experimental data from umbilical occlusions in near-term fetal sheep (2698 occlusions from 57 near-term lambs) were used to train a convolutional [...] Read more.

Our objective is to develop a model for the prediction of minimum fetal blood pressure (FBP) during fetal heart rate (FHR) decelerations. Experimental data from umbilical occlusions in near-term fetal sheep (2698 occlusions from 57 near-term lambs) were used to train a convolutional neural network. This model was then used to estimate FBP for decelerations extracted from the final 90 min of 53,445 human FHR signals collected using cardiotocography. Minimum sheep FBP was predicted with a mean absolute error of

6.7

mmHg (25th, 50th, 75th percentiles of

2.3

,

5.2

,

9.7

mmHg), mean absolute percentage errors of

17.3

% (

5.5

%,

12.5

%,

23.9

%) and a coefficient of determination

R^{2} = 0.36

. While the model was unable to clearly predict severe compromise at birth in humans, there is positive evidence that such a model could predict human FBP with further development. The neural network is capable of predicting FBP for many of the sheep decelerations accurately but performed far from satisfactory at identifying FHR segments that correspond to the highest or lowest minimum FBP. These results indicate that with further work and a larger, more variable training dataset, the model could achieve higher accuracy. Full article

(This article belongs to the Special Issue Fetal-Maternal Monitoring during Pregnancy and Labor: Trends and Opportunities)

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17 pages, 2837 KiB

Open AccessArticle

Multimodal Deep Learning for Predicting Adverse Birth Outcomes Based on Early Labour Data

by Daniel Asfaw, Ivan Jordanov, Lawrence Impey, Ana Namburete, Raymond Lee and Antoniya Georgieva

Bioengineering 2023, 10(6), 730; https://doi.org/10.3390/bioengineering10060730 - 19 Jun 2023

Cited by 5 | Viewed by 2366

Abstract

Cardiotocography (CTG) is a widely used technique to monitor fetal heart rate (FHR) during labour and assess the health of the baby. However, visual interpretation of CTG signals is subjective and prone to error. Automated methods that mimic clinical guidelines have been developed, [...] Read more.

Cardiotocography (CTG) is a widely used technique to monitor fetal heart rate (FHR) during labour and assess the health of the baby. However, visual interpretation of CTG signals is subjective and prone to error. Automated methods that mimic clinical guidelines have been developed, but they failed to improve detection of abnormal traces. This study aims to classify CTGs with and without severe compromise at birth using routinely collected CTGs from 51,449 births at term from the first 20 min of FHR recordings. Three 1D-CNN and LSTM based architectures are compared. We also transform the FHR signal into 2D images using time-frequency representation with a spectrogram and scalogram analysis, and subsequently, the 2D images are analysed using a 2D-CNNs. In the proposed multi-modal architecture, the 2D-CNN and the 1D-CNN-LSTM are connected in parallel. The models are evaluated in terms of partial area under the curve (PAUC) between 0–10% false-positive rate; and sensitivity at 95% specificity. The 1D-CNN-LSTM parallel architecture outperformed the other models, achieving a PAUC of 0.20 and sensitivity of 20% at 95% specificity. Our future work will focus on improving the classification performance by employing a larger dataset, analysing longer FHR traces, and incorporating clinical risk factors. Full article

(This article belongs to the Special Issue Fetal-Maternal Monitoring during Pregnancy and Labor: Trends and Opportunities)

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Review

Jump to: Research

20 pages, 1971 KiB

Open AccessReview

Fetal Heart Rate Preprocessing Techniques: A Scoping Review

by Inês Campos, Hernâni Gonçalves, João Bernardes and Luísa Castro

Bioengineering 2024, 11(4), 368; https://doi.org/10.3390/bioengineering11040368 - 11 Apr 2024

Viewed by 1902

Abstract

Monitoring fetal heart rate (FHR) through cardiotocography is crucial for the early diagnosis of fetal distress situations, necessitating prompt obstetrical intervention. However, FHR signals are often marred by various contaminants, making preprocessing techniques essential for accurate analysis. This scoping review, following PRISMA-ScR guidelines, [...] Read more.

Monitoring fetal heart rate (FHR) through cardiotocography is crucial for the early diagnosis of fetal distress situations, necessitating prompt obstetrical intervention. However, FHR signals are often marred by various contaminants, making preprocessing techniques essential for accurate analysis. This scoping review, following PRISMA-ScR guidelines, describes the preprocessing methods in original research articles on human FHR (or beat-to-beat intervals) signal preprocessing from PubMed and Web of Science, published from their inception up to May 2021. From the 322 unique articles identified, 54 were included, from which prevalent preprocessing approaches were identified, primarily focusing on the detection and correction of poor signal quality events. Detection usually entailed analyzing deviations from neighboring samples, whereas correction often relied on interpolation techniques. It was also noted that there is a lack of consensus regarding the definition of missing samples, outliers, and artifacts. Trends indicate a surge in research interest in the decade 2011–2021. This review underscores the need for standardizing FHR signal preprocessing techniques to enhance diagnostic accuracy. Future work should focus on applying and evaluating these methods across FHR databases aiming to assess their effectiveness and propose improvements. Full article

(This article belongs to the Special Issue Fetal-Maternal Monitoring during Pregnancy and Labor: Trends and Opportunities)

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28 pages, 5375 KiB

Open AccessReview

Acquisition Devices for Fetal Phonocardiography: A Scoping Review

by Noemi Giordano, Agnese Sbrollini, Micaela Morettini, Samanta Rosati, Gabriella Balestra, Ennio Gambi, Marco Knaflitz and Laura Burattini

Bioengineering 2024, 11(4), 367; https://doi.org/10.3390/bioengineering11040367 - 11 Apr 2024

Viewed by 1962

Abstract

Timely and reliable fetal monitoring is crucial to prevent adverse events during pregnancy and delivery. Fetal phonocardiography, i.e., the recording of fetal heart sounds, is emerging as a novel possibility to monitor fetal health status. Indeed, due to its passive nature and its [...] Read more.

Timely and reliable fetal monitoring is crucial to prevent adverse events during pregnancy and delivery. Fetal phonocardiography, i.e., the recording of fetal heart sounds, is emerging as a novel possibility to monitor fetal health status. Indeed, due to its passive nature and its noninvasiveness, the technique is suitable for long-term monitoring and for telemonitoring applications. Despite the high share of literature focusing on signal processing, no previous work has reviewed the technological hardware solutions devoted to the recording of fetal heart sounds. Thus, the aim of this scoping review is to collect information regarding the acquisition devices for fetal phonocardiography (FPCG), focusing on technical specifications and clinical use. Overall, PRISMA-guidelines-based analysis selected 57 studies that described 26 research prototypes and eight commercial devices for FPCG acquisition. Results of our review study reveal that no commercial devices were designed for fetal-specific purposes, that the latest advances involve the use of multiple microphones and sensors, and that no quantitative validation was usually performed. By highlighting the past and future trends and the most relevant innovations from both a technical and clinical perspective, this review will represent a useful reference for the evaluation of different acquisition devices and for the development of new FPCG-based systems for fetal monitoring. Full article

(This article belongs to the Special Issue Fetal-Maternal Monitoring during Pregnancy and Labor: Trends and Opportunities)

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28 pages, 1174 KiB

Open AccessReview

Computerised Cardiotocography Analysis for the Automated Detection of Fetal Compromise during Labour: A Review

by Lochana Mendis, Marimuthu Palaniswami, Fiona Brownfoot and Emerson Keenan

Bioengineering 2023, 10(9), 1007; https://doi.org/10.3390/bioengineering10091007 - 25 Aug 2023

Cited by 10 | Viewed by 6600

Abstract

The measurement and analysis of fetal heart rate (FHR) and uterine contraction (UC) patterns, known as cardiotocography (CTG), is a key technology for detecting fetal compromise during labour. This technology is commonly used by clinicians to make decisions on the mode of delivery [...] Read more.

The measurement and analysis of fetal heart rate (FHR) and uterine contraction (UC) patterns, known as cardiotocography (CTG), is a key technology for detecting fetal compromise during labour. This technology is commonly used by clinicians to make decisions on the mode of delivery to minimise adverse outcomes. A range of computerised CTG analysis techniques have been proposed to overcome the limitations of manual clinician interpretation. While these automated techniques can potentially improve patient outcomes, their adoption into clinical practice remains limited. This review provides an overview of current FHR and UC monitoring technologies, public and private CTG datasets, pre-processing steps, and classification algorithms used in automated approaches for fetal compromise detection. It aims to highlight challenges inhibiting the translation of automated CTG analysis methods from research to clinical application and provide recommendations to overcome them. Full article

(This article belongs to the Special Issue Fetal-Maternal Monitoring during Pregnancy and Labor: Trends and Opportunities)

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16 pages, 1699 KiB

Open AccessReview

Heart Rate Variability Code: Does It Exist and Can We Hack It?

by Martin Gerbert Frasch

Bioengineering 2023, 10(7), 822; https://doi.org/10.3390/bioengineering10070822 - 10 Jul 2023

Cited by 8 | Viewed by 2064

Abstract

A code is generally defined as a system of signals or symbols for communication. Experimental evidence is synthesized for the presence and utility of such communication in heart rate variability (HRV) with particular attention to fetal HRV: HRV contains signatures of information flow [...] Read more.

A code is generally defined as a system of signals or symbols for communication. Experimental evidence is synthesized for the presence and utility of such communication in heart rate variability (HRV) with particular attention to fetal HRV: HRV contains signatures of information flow between the organs and of response to physiological or pathophysiological stimuli as signatures of states (or syndromes). HRV exhibits features of time structure, phase space structure, specificity with respect to (organ) target and pathophysiological syndromes, and universality with respect to species independence. Together, these features form a spatiotemporal structure, a phase space, that can be conceived of as a manifold of a yet-to-be-fully understood dynamic complexity. The objective of this article is to synthesize physiological evidence supporting the existence of HRV code: hereby, the process-specific subsets of HRV measures indirectly map the phase space traversal reflecting the specific information contained in the code required for the body to regulate the physiological responses to those processes. The following physiological examples of HRV code are reviewed, which are reflected in specific changes to HRV properties across the signal–analytical domains and across physiological states and conditions: the fetal systemic inflammatory response, organ-specific inflammatory responses (brain and gut), chronic hypoxia and intrinsic (heart) HRV (iHRV), allostatic load (physiological stress due to surgery), and vagotomy (bilateral cervical denervation). Future studies are proposed to test these observations in more depth, and the author refers the interested reader to the referenced publications for a detailed study of the HRV measures involved. While being exemplified mostly in the studies of fetal HRV, the presented framework promises more specific fetal, postnatal, and adult HRV biomarkers of health and disease, which can be obtained non-invasively and continuously. Full article

(This article belongs to the Special Issue Fetal-Maternal Monitoring during Pregnancy and Labor: Trends and Opportunities)

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