Modelling Segmented Cardiotocography Time-Series Signals Using One-Dimensional Convolutional Neural Networks for the Early Detection of Abnormal Birth Outcomes

Beth Pineles; Carl Chalmers; Casimiro Curbelo Montanez; Denis Reilly; Paul Fergus; Paulo Lisboa

REVIEW 4 major objections 6 minor 24 references

Modelling Segmented Cardiotocography Time-Series Signals Using One-Dimensional Convolutional Neural Networks for the Early Detection of Abnormal Birth Outcomes

T0 review · 4 major / 6 minor · reviewed 2026-08-14 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read Windowing fetal heart-rate traces into non-overlapping 200-point segments lets a one-dimensional convolutional network detect abnormal birth outcomes with 80% sensitivity and 79% specificity.

desk verdict Reasonable windowing + 1DCNN idea on public CTG data, but the headline AUC is undermined by label contamination, non-independent windows, and test-set model selection. read the letter →

arxiv 1908.02338 v2 pith:K2Q3O6RV submitted 2019-08-06 cs.LG stat.ML

Paul Fergus , Carl Chalmers , Casimiro Curbelo Montanez , Denis Reilly , Paulo Lisboa , Beth Pineles This is my paper · ORCID

classification cs.LGstat.ML

keywords cardiotocographyfetalheartrateone-dimensionalconvolutionalneuralnetworkclassimbalancetime-serieswindowingbirthoutcomepredictiondeeplearningCTGclassification

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper claims that the class imbalance that plagues machine learning on cardiotocography (CTG) recordings can be removed without synthetic data by segmenting traces into non-overlapping windows, keeping every window from abnormal deliveries and randomly subsampling an equal number from normal deliveries. A one-dimensional convolutional neural network trained on these raw windows learns discriminative features automatically, removing the need for manually engineered clinical features. With 200-sample windows the test performance reaches 80% sensitivity and 79% specificity with an AUC of 0.86, and this combination outperforms an MLP, SVM, random forest, FLDA, and the authors' previous SMOTE approach on the same 552-recording dataset. A sympathetic reader would take this as evidence that windowed real data plus learned features is a workable route to automated intrapartum monitoring.

What carries the argument

The load-bearing machinery is a one-dimensional convolutional neural network applied to segmented raw signals: a 1D convolution sweeps learnable kernels along the time axis to detect local patterns, a max-pooling layer downsamples them, and two dense layers classify the resulting representation. Its companion mechanism is the windowing scheme: non-overlapping windows of fixed length per trace, all windows from case records retained and an equal number randomly drawn from control records, yielding balanced training sets built from real data. The design choices include a kernel size half the window length, ReLU after the convolution, sigmoid output, Adam optimization, and binary cross-entropy loss, tuned on training and validation windows.

What would settle it

Retrain the 200-window 1DCNN using only the 18 deliveries with umbilical-artery pH $\le 7.20$ as the abnormal class and an equal number of normal windows; if the window-level AUC stays near 0.86 the separation reflects real pathology, whereas a drop toward chance or a model that labels the 24 unconfirmed caesareans as normal would show that delivery-mode labels drove the reported result.

Watch

Extended reading notes

Core claim

The central claim is that raw fetal heart-rate traces, cut into fixed-length non-overlapping windows, contain enough temporal morphology to separate normal from abnormal birth outcomes with no handcrafted feature extraction. The training set is balanced by retaining all windows from abnormal cases and randomly drawing the same number of windows from normal controls, so the model sees only real signal segments. A 1DCNN with one convolutional layer of 20 filters, a kernel half the window length, one max-pooling layer, and two dense layers is trained directly on these windows. At a window size of 200 the authors report sensitivity 80% (95% CI 75% to 85%), specificity 79% (95% CI 73% to 84%), and AUC 0.86 (95% CI 0.81 to 0.91), the best among the five window sizes tested. The same setup outperforms an MLP on raw windows and beats SVM, random forest, and FLDA classifiers under identical conditions, as well as the authors' earlier SMOTE ensemble.

Load-bearing premise

The load-bearing premise is that the 24 caesarean deliveries with no recorded pathological outcome measures were performed because of underlying fetal pathology, even though the dataset curators give no reason; those 24 cases make up more than half of the abnormal class.

Editorial extensions

If this is right

Automated CTG screening could operate directly on raw monitor signals, bypassing the time-consuming and subjective step of extracting clinical features by hand.
Windowing supplies a real-data alternative to SMOTE, so balanced training sets are produced without synthetic minority samples and their associated bias.
Because the network learns features from data rather than from fixed rules, the same pipeline can be transferred to new CTG devices or new clinical questions with little re-engineering.
Window size is a genuine tuning parameter: the best separation in this study occurs at 200 samples, with both shorter and longer windows degrading performance.
On the same dataset, the windowed 1DCNN improves on the authors' previous SMOTE ensemble and on established classifiers including FLDA, random forest, SVM, and a plain MLP.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

A testable extension follows from the label assumption: retraining the 200-window model with only the 18 pH-confirmed acidosis cases as abnormal would show whether the reported discrimination is fetal pathology or merely delivery mode.
The reported metrics are window-level, and each patient contributes many correlated windows, so a per-patient aggregation rule (for example, majority voting across windows) could change the apparent sensitivity and specificity and should be evaluated before clinical use.
The same recipe of real-data window balancing plus 1D convolution could transfer to other long, imbalanced physiological recordings, such as EEG seizure detection or continuous ECG monitoring, where handcrafted features and class skew are twin barriers.

Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Quick take: the windowing + 1DCNN idea is neat and the paper is worth a look, but I don't trust the headline numbers as an estimate of clinical performance. The main result (AUC 0.86, W=200) is built on three assumptions that the authors mostly acknowledge but don't fix: windows from the same recording treated as independent, test-set used to select the window size, and a positive class where 24 of 46 cases have no confirmed pathology.

What's actually new: combining non-overlapping windowing as a class-balancing strategy with a simple 1DCNN on raw FHR traces. That's a sensible way to use real data rather than synthetic oversampling, and it's a plausible contribution to the CTG analysis literature. The paper does some things well: public dataset, standard baselines (MLP, SVM, RF, FLDA), and a comparison with their own SMOTE work. They also tell you exactly what the label limitation is, which is more transparent than many papers in this space.

Soft spots, in order of severity. First, the label problem. The positive class is 46 caesareans, and for 24 of them there is no recorded acidosis or deterioration; the authors assume the caesarean was for pathological reasons but admit they can't validate it. Since those 24 are more than half the positives, the model may be learning "delivery mode" rather than "fetal compromise." That threatens the construct validity of the whole experiment. Second, the evaluation ignores the recording structure: multiple windows from one recording are treated as independent observations. The confidence intervals use window counts, not recording counts, so they're far too tight and the AUC is likely inflated by within-recording correlation. Third, the best window size is picked from test-set results. The text says validation pointed to W=200, but Table 5 actually shows W=500 with higher validation AUC, so the selection is at least inconsistent. A pre-specified window or a nested validation loop would fix this.

These are all fixable with a recording-level bootstrap or cross-validation, a pre-registered window size, and a cleaner outcome definition (e.g., only confirmed acidosis or a composite of known pathology). None of the problems makes the method itself nonsensical; the approach is reasonable and the data is public, so someone could rerun it properly.

My bottom line: this paper deserves a serious referee. It's a useful example of both a practical class-imbalance trick and a set of common evaluation traps. But I wouldn't cite the 0.86 AUC as evidence of a working clinical tool. If the authors tighten the evaluation, the result might hold in a weaker form; as it stands, it's a "conditional" rather than a "validated" claim.

Recommendation: send it to review with a request for major revision, focusing on the three points above.

Referee Report

4 major / 6 minor

Summary. The paper proposes a method for classifying abnormal birth outcomes from intrapartum cardiotocography (CTG) traces. The method splits each fetal heart rate (FHR) signal into non-overlapping fixed-length windows, retains all windows from the 46 abnormal deliveries, randomly subsamples an equal number of windows from the 506 normal deliveries, and trains a one-dimensional convolutional neural network (1DCNN) on the raw FHR windows. On the CTU-UHB dataset the authors report a best test performance with window size 200: sensitivity 80%, specificity 79%, and AUC 0.86, outperforming MLP, SVM, RF, FLDA, and their earlier SMOTE-based pipeline. The paper also discusses limitations, including the absence of clinical annotations in the CTG traces and the unverifiable assumption behind the positive-label definition.

Significance. If the reported result were valid, the windowing-plus-1DCNN pipeline would be a useful and simple contribution to imbalanced clinical time-series classification, removing handcrafted feature engineering and using only real data. Strengths include the use of a public dataset (CTU-UHB), head-to-head comparisons against several conventional classifiers under the same windowing protocol, and transparent acknowledgment of the unverifiable label assumption in Section 2.1 and of the lack of trace annotations in the Conclusion. However, the current evaluation does not establish the central claim because the test metrics are computed at the window level, the best window size is chosen after inspecting test results, and the positive-label definition may encode mode of delivery rather than fetal pathology.

major comments (4)

[Section 2.2 and Tables 2, 6; Eq. (7)] All performance metrics and confidence intervals are computed over test windows, with multiple non-overlapping windows from the same CTG recording treated as independent observations. For W=200, the test set contains 620 case windows and 620 control windows drawn from only 10 case recordings and 101 control recordings, so the effective sample size for estimating sensitivity, specificity, and AUC is much smaller than 1,240. Adjacent windows from one trace are highly correlated, and the model may exploit per-recording artifacts. This inflates the reported confidence intervals and can make a model look substantially better than its recording-level performance. Please report results at the recording level (for example, majority vote over windows per recording) or use clustered cross-validation that leaves entire recordings out, and adjust confidence intervals for clustering.
[Section 3.2.2 and Table 6; also Sections 3.1.2, 3.3.2, 3.3.4, 3.3.6] The window size W=200 is selected as best after inspecting test-set ROC curves and test AUC in Table 6. The same test-set selection pattern appears for the MLP, SVM, RF, and FLDA baselines. Selecting a hyperparameter such as window size on the test set and then reporting that test performance as the headline result is a form of test-set leakage; the expected best-of-five AUC is larger than the expected AUC for a fixed configuration. The reported 0.86 AUC for W=200 is therefore optimistic and is not a valid estimate of generalization performance. Please select window size and other hyperparameters using a validation split or nested cross-validation, and report the performance of the selected configuration on a truly held-out test set.
[Section 2.1] The positive class is defined as 46 caesarean deliveries, of which 18 had pH≤7.20, 4 had pH between 7.20 and 7.25 (fetal deterioration), and 24 had no recorded pathological outcome measures. The authors state that they assume the decision to deliver by caesarean was supported by underlying pathological concerns, with the explicit caveat that there is no way to validate this. Because these 24 subjects constitute 52% of the positive class, the classifier could be learning to distinguish caesarean deliveries from vaginal deliveries rather than pathological from normal fetal states, especially since caesarean delivery is associated with different clinical management and possibly different FHR patterns. This is a construct-validity threat to the central claim of detecting abnormal birth outcomes. Please provide a sensitivity analysis restricted to the 22 cases with confirmed pathological outcomes (acidosis or deterioration), and clearly interpret results for the full 46-case label definition.
[Sections 2.2 and 3] The paper describes two different split protocols. Section 3 says the data set is split randomly into training (80%), validation (10%), and testing (10%), while Section 2.2 describes only training and test sets (405/101 controls, 36/10 cases) with no validation set. It is unclear how the validation set used for early stopping and model selection was constructed, whether it was drawn from the training records before windowing, and whether any test windows came from records used in training. In addition, because control windows are randomly subsampled to balance classes, the test set is stochastic, yet no random seeds or repeated subsampling results are reported. Please specify the exact split at the recording level, including the validation set, and report results across multiple subsampling seeds or with a fixed seed.

minor comments (6)

[Section 2.4] The definitions of sensitivity and specificity appear reversed: the text says sensitivity describes true positives (normal deliveries) and specificity describes true negatives (pathological deliveries), which contradicts the usual convention and the paper's own treatment of caesarean/pathological cases as the positive class. Please correct these definitions and make the positive-class convention explicit throughout.
[Equation (7)] The confidence-interval formula uses 'i' both as an index and as the quantile, and the phrase '1 − α/2)-quantile' has a formatting error; this should be the standard normal quantile z_{1−α/2}. Please rewrite the formula and its notation.
[Table 9] The last row of Table 9 has a formatting error: the specificity confidence interval is written as '0.53,0.70")' with a stray quotation mark and parenthesis.
[Section 3.3.2] The text says 'Fig. 9 shows that the ROC curves for all SVM models...', but Figure 9 is the 1DCNN ROC plot; the SVM ROC curves appear in Figure 10. The cross-reference should be corrected.
[Discussion, SMOTE comparison] The text refers to 'Table 8' when presenting the SMOTE oversampling results, but the relevant table is Table 10. Please correct the cross-reference.
[Section 2.3] The 1DCNN architecture description is not fully reproducible: the kernel size is said to be half the windowing strategy with the example '150 for 300 data points', but no kernel size is given for W=200, and the number of filters, stride, pooling size, and dropout are not specified. Please provide a complete architecture table.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the reported performance is an empirical held-out evaluation on a public dataset, and the self-citations are comparison baselines rather than load-bearing assumptions.

full rationale

The paper's central claim is that a 1DCNN trained on non-overlapping CTG windows, with classes balanced by retaining all case windows and randomly subsampling control windows, distinguishes abnormal from normal birth outcomes. This claim is supported by an empirical evaluation on the public CTU-UHB dataset using a separate test set. No fitted parameter is renamed as a prediction, and no derived quantity is defined in terms of the quantity it is said to predict. The sensitivity, specificity, and AUC values are computed from held-out windows using standard metrics, not from the training distribution or from the balancing scheme by construction. The study's self-citations, such as the SMOTE comparison referring to the authors' earlier work [18], are external comparison baselines and do not carry the derivation of the proposed method. The acknowledged assumption that 24 caesarean deliveries without documented pathological outcome measures are abnormal is a label-validity and construct-validity concern, not a circularity: it may weaken the clinical meaning of the reported results, but it does not make any prediction equal to its input by definition. Therefore, no specific circular step is present, and the derivation chain is self-contained with respect to the claimed empirical demonstration.

Assumptions & free parameters 5 free parameters · 4 assumptions · 0 invented entities

The central evaluation rests on several untested assumptions: the outcome labels (especially the 24 unconfirmed caesareans), the representativeness of window subsampling, and the equivalence between window-level and recording-level classification. These are not derived from data and materially affect the reported performance.

free parameters (5)

Window size W = 200 (best of tested 100, 200, 300, 400, 500)
Selected by comparing test-set AUC across five window sizes (Section 3.2, Table 6); no theoretical justification, and selection is not accounted for in the confidence intervals.
Convolution kernel size = Half the window size (e.g., 100 for W=200)
Set as half the windowing strategy because the authors state this empirically produced the best results (Section 3.2.1).
Number of convolution filters = 20
Chosen empirically (Section 3.2.1); no ablation study reported.
Learning rate = 0.0001
Fixed for all 1DCNN experiments (Section 3.2.1), with no sensitivity analysis.
Number of training epochs = 500
Chosen as a fixed training duration, with convergence judged from learning curves (Section 3.2.1).

assumptions (4)

domain assumption Cubic Hermite spline interpolation adequately reconstructs missing or noisy FHR values.
Preprocessing step (Section 2.1) used without evaluation of its effect on classification performance.
ad hoc to paper The 24 caesarean deliveries without recorded pathological outcome measures represent abnormal outcomes.
Stated assumption in Section 2.1; if false, over half the positive labels are mislabeled.
domain assumption Window-level classification performance is a valid proxy for recording-level clinical diagnosis.
The model predicts on windows; clinical decisions are per-recording, and windows from the same recording are correlated.
domain assumption Random subsampling of control windows yields a representative, non-biased sample of normal deliveries.
Used to balance classes (Section 2.2); no analysis shows the sampled windows capture the full variability of normal traces.

how reviews work

0 comments

Cite this review

Pith. "Pith review of Modelling Segmented Cardiotocography Time-Series Signals Using One-Dimensional Convolutional Neural Networks for the Early Detection of Abnormal Birth Outcomes." pith.science (2026). https://pith.science/paper/K2Q3O6RV

@misc{pith2026190802338,
  author       = {Pith},
  title        = {Pith review of: Modelling Segmented Cardiotocography Time-Series Signals Using One-Dimensional Convolutional Neural Networks for the Early Detection of Abnormal Birth Outcomes},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/K2Q3O6RV}},
  note         = {Machine review of arXiv:1908.02338}
}

read the original abstract

Gynaecologists and obstetricians visually interpret cardiotocography (CTG) traces using the International Federation of Gynaecology and Obstetrics (FIGO) guidelines to assess the wellbeing of the foetus during antenatal care. This approach has raised concerns among professionals with regards to inter- and intra-variability where clinical diagnosis only has a 30\% positive predictive value when classifying pathological outcomes. Machine learning models, trained with FIGO and other user derived features extracted from CTG traces, have been shown to increase positive predictive capacity and minimise variability. This is only possible however when class distributions are equal which is rarely the case in clinical trials where case-control observations are heavily skewed in favour of normal outcomes. Classes can be balanced using either synthetic data derived from resampled case training data or by decreasing the number of control instances. However, this either introduces bias or removes valuable information. Concerns have also been raised regarding machine learning studies and their reliance on manually handcrafted features. While this has led to some interesting results, deriving an optimal set of features is considered to be an art as well as a science and is often an empirical and time consuming process. In this paper, we address both of these issues and propose a novel CTG analysis methodology that a) splits CTG time-series signals into n-size windows with equal class distributions, and b) automatically extracts features from time-series windows using a one dimensional convolutional neural network (1DCNN) and multilayer perceptron (MLP) ensemble. Collectively, the proposed approach normally distributes classes and removes the need to handcrafted features from CTG traces.

Figures

Figures reproduced from arXiv: 1908.02338 by the authors.

**Figure 2.** One Dimensional Convolutional Neural Network Architecture The network model is trained by minimizing the cost function using feedforward and backpropagation passes. The feedforward pass constructs a feature map from the previous layer to the next through the current layer until an output is obtained. The input and kernel filters of the previous layer are computed as follows: z l j = M Xl−1 l=1 1dconv(x l−1 i , kl−1 … view at source ↗

**Figure 3.** Convolution and Max Pooling Process where Yni and yni are the target labels and the predicted outputs, respectively, and i the number of classes in the nth training set. The learning process optimizes the network free parameters and minimises E. The derivatives of the free parameters are obtained and the weights and biases are updated using learning rate (η). To prompt rapid convergence, we utilise Adam as an optim… view at source ↗

Figures from the paper (7 more)

**Figure 4.** Figure 4: Baseline MLP Training and Validation Logloss plot [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]

**Figure 5.** Figure 5: Baseline MLP Training and Validation Accuracy plot [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]

**Figure 6.** Figure 6: Baseline MLP Test ROC Curves for all Window sizes. [PITH_FULL_IMAGE:figures/full_fig_p006_6.png]

**Figure 8.** Figure 8: 1D CNN Training and Validation Accuracy plot for [PITH_FULL_IMAGE:figures/full_fig_p007_8.png]

**Figure 10.** Figure 10: SVM Test ROC Curves for all Window sizes. [PITH_FULL_IMAGE:figures/full_fig_p007_10.png]

**Figure 12.** Figure 12: FLDA Test ROC Curves for all Window sizes. [PITH_FULL_IMAGE:figures/full_fig_p008_12.png]

**Figure 11.** Figure 11: RF Test ROC Curves for all Window sizes. [PITH_FULL_IMAGE:figures/full_fig_p008_11.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

24 extracted references · 22 canonical work pages

[1]

Current World Population

worldometer, “Current World Population.” http://www. worldometers.info/world-population/, 2018. [Online; accessed 28-November-2018]

work page 2018
[2]

Five Years of Cerebral Palsy Claims: A Thematic Review of NHS Resolu- tion Data

N. Resolution”, “Five Years of Cerebral Palsy Claims: A Thematic Review of NHS Resolu- tion Data.” https://resolution.nhs.uk/wp-content/ uploads/2017/09/Five-years-of-cerebral-palsy-claims A-thematic-review-of-NHS-Resolution-data.pdf, 2017. [Online; accessed 28-November-2018]

work page 2017
[3]

MBRRACE-UK: Mothers and Babies: Reducing Risk through Audits and Conﬁdential Enquiries across the UK

”Sands”, “MBRRACE-UK: Mothers and Babies: Reducing Risk through Audits and Conﬁdential Enquiries across the UK.” https://www.sands.org.uk/sites/default/ﬁles/MBRRACE-UK% 20report%20response 08.09.17.pdf, 2017. [Online; accessed 28-November-2018]

work page 2017
[4]

Olofsson, H

P . Olofsson, H. Norn, and A. Carlsson, “New ﬁgo and swedish intrapartum cardiotocography classiﬁcation systems incorporated in the fetal ecg st analysis (stan) interpretation algorithm: agree- ments and discrepancies in cardiotocography classiﬁcation and evaluation of signiﬁcant st events,” Acta Obstetricia et Gynecologica Scandinavica, vol. 97, no. 2, p...

work page 2018
[5]

Physiobank, physiotoolkit, and physionet: components of a new research resource for complex physiologic signals,

A. L. Goldberger, L. A. Amaral, L. Glass, J. M. Hausdorff, P . C. Ivanov, R. G. Mark, J. E. Mietus, G. B. Moody, C.-K. Peng, and H. E. Stanley, “Physiobank, physiotoolkit, and physionet: components of a new research resource for complex physiologic signals,” Circulation, vol. 101, no. 23, pp. e215–e220, 2000

work page 2000
[6]

Computer analysis of antepartum fetal heart rate: 2. detection of accelerations and decelerations,

R. Mantel, H. Van Geijn, F. Caron, J. Swartjes, E. Van Woerden, and H. Jongswa, “Computer analysis of antepartum fetal heart rate: 2. detection of accelerations and decelerations,” International Journal of Bio-Medical Computing, vol. 25, no. 4, pp. 273–286, 1990

work page 1990
[7]

Antenatal foetal heart monitoring,

H. Murray, “Antenatal foetal heart monitoring,” Best Practice & Research Clinical Obstetrics & Gynaecology, vol. 38, pp. 2–11, 2017

work page 2017
[8]

Inter- and intra-observer agreement of non- reassuring cardiotocography analysis and subsequent clinical management,

S. Rhse, A. M. Heinis, F. Vandenbussche, J. van Drongelen, and J. van Dillen, “Inter- and intra-observer agreement of non- reassuring cardiotocography analysis and subsequent clinical management,” Acta Obstetricia et Gynecologica Scandinavica, vol. 93, no. 6, pp. 596–602, 2014

work page 2014

Show all 24 references

[9]

Clas- siﬁcation of normal and hypoxic fetuses from systems modeling of intrapartum cardiotocography,

P . A. Warrick, E. F. Hamilton, D. Precup, and R. E. Kearney, “Clas- siﬁcation of normal and hypoxic fetuses from systems modeling of intrapartum cardiotocography,” IEEE Transactions on Biomedical Engineering, vol. 57, no. 4, pp. 771–779, 2010

2010
[10]

Delay in intervention in- creases neonatal morbidity in births monitored with cardiotocog- raphy and st-waveform analysis,

J. Kessler, D. Moster, and S. Albrechtsen, “Delay in intervention in- creases neonatal morbidity in births monitored with cardiotocog- raphy and st-waveform analysis,” Acta obstetricia et gynecologica Scandinavica, vol. 93, no. 2, pp. 175–181, 2014

2014
[11]

Inﬂuence of feature selection on na¨ıve bayes classiﬁer for recognizing patterns in cardiotocograms,

M. E. B. Menai, F. J. Mohder, and F. Al-mutairi, “Inﬂuence of feature selection on na¨ıve bayes classiﬁer for recognizing patterns in cardiotocograms,” Journal of Medical and Bioengineering , vol. 2, no. 1, 2013

2013
[12]

Discriminating normal from abnormal pregnancy cases using an automated fhr evaluation method,

J. Spilka, G. Georgoulas, P . Karvelis, V . Chudacek, C. D. Stylios, and L. Lhotska, “Discriminating normal from abnormal pregnancy cases using an automated fhr evaluation method,” in Hellenic Conference on Artiﬁcial Intelligence, pp. 521–531, 2014

2014
[13]

The value of latent class analysis in medical diagnosis,

D. Rindskopf and W. Rindskopf, “The value of latent class analysis in medical diagnosis,” Statistics in medicine, vol. 5, no. 1, pp. 21–27, 1986

1986
[14]

Open access intrapartum ctg database: Stepping stone towards generalization of technical ﬁndings on ctg signals,

V . Chudacek, J. Spilka, M. Bura, P . Janku, L. Hruban, M. Huptych, and L. Lhotska, “Open access intrapartum ctg database: Stepping stone towards generalization of technical ﬁndings on ctg signals,” PLoS ONE (manuscript submitted for publication, 2013) , 2013

2013
[15]

Using nonlinear features for fetal heart rate classiﬁcation,

J. Spilka, V . Chudavcek, M. Koucky, L. Lhotska, M. Huptych, P . Janku, G. Georgoulas, and C. Stylios, “Using nonlinear features for fetal heart rate classiﬁcation,” Biomedical Signal Processing and Control, vol. 7, no. 4, pp. 350–357, 2012

2012
[16]

Classiﬁcation of imbalanced data by oversampling in kernel space of support vec- tor machines,

J. Mathew, C. K. Pang, M. Luo, and W. H. Leong, “Classiﬁcation of imbalanced data by oversampling in kernel space of support vec- tor machines,” IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no. 9, pp. 4065–4076, 2018

2018
[17]

Smote: synthetic minority over-sampling technique,

N. V . Chawla, K. W. Bowyer, L. O. Hall, and W. P . Kegelmeyer, “Smote: synthetic minority over-sampling technique,” Journal of artiﬁcial intelligence research, vol. 16, pp. 321–357, 2002

2002
[18]

Machine learning ensem- ble modelling to classify caesarean section and vaginal delivery types using cardiotocography traces,

P . Fergus, M. Selvaraj, and C. Chalmers, “Machine learning ensem- ble modelling to classify caesarean section and vaginal delivery types using cardiotocography traces,” Computers in Biology and Medicine, vol. 93, pp. 7 – 16, 2018

2018
[19]

Novel leakage detection by ensemble cnn-svm and graph-based localiza- tion in water distribution systems,

J. Kang, Y.-J. Park, J. Lee, S.-H. Wang, and D.-S. Eom, “Novel leakage detection by ensemble cnn-svm and graph-based localiza- tion in water distribution systems,” IEEE Transactions on Industrial Electronics, vol. 65, no. 5, pp. 4279–4289, 2017

2017
[20]

Classiﬁcation of caesarean section and normal vagi- nal deliveries using foetal heart rate signals and advanced ma- chine learning algorithms,

P . Fergus, A. Hussain, D. Al-Jumeily, D.-S. Huang, and N. Bouguila, “Classiﬁcation of caesarean section and normal vagi- nal deliveries using foetal heart rate signals and advanced ma- chine learning algorithms,” BioMedical Engineering OnLine, vol. 16, no. 1, p. 89, 2017

2017
[21]

Learning to exploit invariances in clinical time-series data using sequence transformer networks,

J. Oh, J. Wang, and J. Wiens, “Learning to exploit invariances in clinical time-series data using sequence transformer networks,” CoRR, vol. abs/1808.06725, 2018

2018 arXiv
[22]

Chapter 3 - deep learning of brain images and its application to multiple sclerosis,

T. Brosch, Y. Yoo, L. Tang, and R. Tam, “Chapter 3 - deep learning of brain images and its application to multiple sclerosis,” in Machine Learning and Medical Imaging (G. Wu, D. Shen, and M. R. Sabuncu, eds.), pp. 69 – 96, Academic Press, 2016

2016
[23]

Tensorﬂow: Large-scale machine learning on heterogeneous distributed systems,

M. Abadi, A. Agarwal, P . Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. J. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. J ´ozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Man ´e, R. Monga, S. Moore, D. G. Murray, C....

2016 arXiv
[24]

Keras: The python deep learning library,

F. Chollet et al., “Keras: The python deep learning library,” Astro- physics Source Code Library, 2018. Dr Paul Fergus is a Reader (Associate Pro- fessor) in Machine Learning. He is the Head of the Data Science Research Centre. Dr Fergus’s main research interests include machi...

1993

Pith tools

Reviewed August 14, 2026 · model on record in the stance chip above.

[1] [1]

Current World Population

worldometer, “Current World Population.” http://www. worldometers.info/world-population/, 2018. [Online; accessed 28-November-2018]

work page 2018

[2] [2]

Five Years of Cerebral Palsy Claims: A Thematic Review of NHS Resolu- tion Data

N. Resolution”, “Five Years of Cerebral Palsy Claims: A Thematic Review of NHS Resolu- tion Data.” https://resolution.nhs.uk/wp-content/ uploads/2017/09/Five-years-of-cerebral-palsy-claims A-thematic-review-of-NHS-Resolution-data.pdf, 2017. [Online; accessed 28-November-2018]

work page 2017

[3] [3]

MBRRACE-UK: Mothers and Babies: Reducing Risk through Audits and Conﬁdential Enquiries across the UK

”Sands”, “MBRRACE-UK: Mothers and Babies: Reducing Risk through Audits and Conﬁdential Enquiries across the UK.” https://www.sands.org.uk/sites/default/ﬁles/MBRRACE-UK% 20report%20response 08.09.17.pdf, 2017. [Online; accessed 28-November-2018]

work page 2017

[4] [4]

Olofsson, H

P . Olofsson, H. Norn, and A. Carlsson, “New ﬁgo and swedish intrapartum cardiotocography classiﬁcation systems incorporated in the fetal ecg st analysis (stan) interpretation algorithm: agree- ments and discrepancies in cardiotocography classiﬁcation and evaluation of signiﬁcant st events,” Acta Obstetricia et Gynecologica Scandinavica, vol. 97, no. 2, p...

work page 2018

[5] [5]

Physiobank, physiotoolkit, and physionet: components of a new research resource for complex physiologic signals,

A. L. Goldberger, L. A. Amaral, L. Glass, J. M. Hausdorff, P . C. Ivanov, R. G. Mark, J. E. Mietus, G. B. Moody, C.-K. Peng, and H. E. Stanley, “Physiobank, physiotoolkit, and physionet: components of a new research resource for complex physiologic signals,” Circulation, vol. 101, no. 23, pp. e215–e220, 2000

work page 2000

[6] [6]

Computer analysis of antepartum fetal heart rate: 2. detection of accelerations and decelerations,

R. Mantel, H. Van Geijn, F. Caron, J. Swartjes, E. Van Woerden, and H. Jongswa, “Computer analysis of antepartum fetal heart rate: 2. detection of accelerations and decelerations,” International Journal of Bio-Medical Computing, vol. 25, no. 4, pp. 273–286, 1990

work page 1990

[7] [7]

Antenatal foetal heart monitoring,

H. Murray, “Antenatal foetal heart monitoring,” Best Practice & Research Clinical Obstetrics & Gynaecology, vol. 38, pp. 2–11, 2017

work page 2017

[8] [8]

Inter- and intra-observer agreement of non- reassuring cardiotocography analysis and subsequent clinical management,

S. Rhse, A. M. Heinis, F. Vandenbussche, J. van Drongelen, and J. van Dillen, “Inter- and intra-observer agreement of non- reassuring cardiotocography analysis and subsequent clinical management,” Acta Obstetricia et Gynecologica Scandinavica, vol. 93, no. 6, pp. 596–602, 2014

work page 2014