Paper Citation Record · LEDGER

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

As of 16 August 2026, this Paper Citation Record lists 24 of 24 outbound references and 0 inbound Pith citation observations for arXiv:1908.02338.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
1908.02338 v2

Coverage vector

measured 24 of 24 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T14:52:15.072571Z

measured 24 of 24 standing notices

One-hop event checks from named stored sources.

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

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A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

24 of 24 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ff00fc78-1fb3-476e-99a7-eeb9d657a58f · outbound

This paper cites Current World Population.

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

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:52:15.751686Z

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Observation 81e6534e-d7cc-4353-8238-54fb932d7d2f · outbound

This paper cites Five Years of Cerebral Palsy Claims: A Thematic Review of NHS Resolu- tion Data.

Modelling Segmented Cardiotocography Time-Series Signals Using One-Dimensional Convolutional Neural Networks for the Early Detection of Abnormal Birth Outcomes Five Years of Cerebral Palsy Claims: A Thematic Review of NHS Resolu- tion Data

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:52:15.726659Z

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Observation 4dfa5588-57c1-464a-8bc6-c97fb06e9a81 · outbound

This paper cites MBRRACE-UK: Mothers and Babies: Reducing Risk through Audits and Conﬁdential Enquiries across the UK.

Modelling Segmented Cardiotocography Time-Series Signals Using One-Dimensional Convolutional Neural Networks for the Early Detection of Abnormal Birth Outcomes MBRRACE-UK: Mothers and Babies: Reducing Risk through Audits and Conﬁdential Enquiries across the UK

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:52:15.706248Z

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Observation c65d7a6c-af29-4255-9392-ca0854be7114 · outbound

This paper cites Olofsson, H.

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

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:52:15.668872Z

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Observation 3730eb31-55dc-47d3-bf37-fecd64945abc · outbound

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

Modelling Segmented Cardiotocography Time-Series Signals Using One-Dimensional Convolutional Neural Networks for the Early Detection of Abnormal Birth Outcomes Physiobank, physiotoolkit, and physionet: components of a new research resource for complex physiologic signals,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:52:15.637392Z

Source-reported events for the cited work

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Observation 5d550567-6ee7-4d4e-8773-d5233f9a2cdc · outbound

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

Modelling Segmented Cardiotocography Time-Series Signals Using One-Dimensional Convolutional Neural Networks for the Early Detection of Abnormal Birth Outcomes Computer analysis of antepartum fetal heart rate: 2. detection of accelerations and decelerations,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:52:15.610114Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 3db0f5f9-2e46-4908-9404-f55fd3f32242 · outbound

This paper cites Antenatal foetal heart monitoring,.

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

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:52:15.589112Z

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Observation d1e1d5fd-93a6-4309-8fcd-92488576c2e4 · outbound

This paper cites Inter- and intra-observer agreement of non- reassuring cardiotocography analysis and subsequent clinical management,.

Modelling Segmented Cardiotocography Time-Series Signals Using One-Dimensional Convolutional Neural Networks for the Early Detection of Abnormal Birth Outcomes Inter- and intra-observer agreement of non- reassuring cardiotocography analysis and subsequent clinical management,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:52:15.570080Z

Source-reported events for the cited work

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Observation 0658de19-6d7c-466f-b315-fb42289a26e6 · outbound

This paper cites Clas- siﬁcation of normal and hypoxic fetuses from systems modeling of intrapartum cardiotocography,.

Modelling Segmented Cardiotocography Time-Series Signals Using One-Dimensional Convolutional Neural Networks for the Early Detection of Abnormal Birth Outcomes Clas- siﬁcation of normal and hypoxic fetuses from systems modeling of intrapartum cardiotocography,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:52:15.546233Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 3ea83ee6-1cd7-48d7-812e-bd85a38e6985 · outbound

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

Modelling Segmented Cardiotocography Time-Series Signals Using One-Dimensional Convolutional Neural Networks for the Early Detection of Abnormal Birth Outcomes Delay in intervention in- creases neonatal morbidity in births monitored with cardiotocog- raphy and st-waveform analysis,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:52:15.522836Z

Source-reported events for the cited work

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Observation c3e9fb99-0b0d-4b1a-a958-65ba2bccf8d5 · outbound

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

Modelling Segmented Cardiotocography Time-Series Signals Using One-Dimensional Convolutional Neural Networks for the Early Detection of Abnormal Birth Outcomes Inﬂuence of feature selection on na¨ıve bayes classiﬁer for recognizing patterns in cardiotocograms,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:52:15.497997Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 40122440-54cd-433b-85db-39d8c0a9d788 · outbound

This paper cites Discriminating normal from abnormal pregnancy cases using an automated fhr evaluation method,.

Modelling Segmented Cardiotocography Time-Series Signals Using One-Dimensional Convolutional Neural Networks for the Early Detection of Abnormal Birth Outcomes Discriminating normal from abnormal pregnancy cases using an automated fhr evaluation method,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:52:15.467301Z

Source-reported events for the cited work

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Observation 39d794ac-71bd-4ca1-bfb9-e4df228b5d2a · outbound

This paper cites The value of latent class analysis in medical diagnosis,.

Modelling Segmented Cardiotocography Time-Series Signals Using One-Dimensional Convolutional Neural Networks for the Early Detection of Abnormal Birth Outcomes The value of latent class analysis in medical diagnosis,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:52:15.442364Z

Source-reported events for the cited work

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Observation 300efaeb-b995-45d1-af07-ae130af73c10 · outbound

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

Modelling Segmented Cardiotocography Time-Series Signals Using One-Dimensional Convolutional Neural Networks for the Early Detection of Abnormal Birth Outcomes Open access intrapartum ctg database: Stepping stone towards generalization of technical ﬁndings on ctg signals,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:52:15.421659Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation e545190d-8ef0-4223-89af-78c26709054f · outbound

This paper cites Using nonlinear features for fetal heart rate classiﬁcation,.

Modelling Segmented Cardiotocography Time-Series Signals Using One-Dimensional Convolutional Neural Networks for the Early Detection of Abnormal Birth Outcomes Using nonlinear features for fetal heart rate classiﬁcation,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:52:15.402468Z

Source-reported events for the cited work

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Observation 73a7bb1c-2cc6-42f5-8aad-157fb9cfeb30 · outbound

This paper cites Classiﬁcation of imbalanced data by oversampling in kernel space of support vec- tor machines,.

Modelling Segmented Cardiotocography Time-Series Signals Using One-Dimensional Convolutional Neural Networks for the Early Detection of Abnormal Birth Outcomes Classiﬁcation of imbalanced data by oversampling in kernel space of support vec- tor machines,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:52:15.382085Z

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Observation 0eadd5e7-23e6-4782-ab3c-9eb9d0f51344 · outbound

This paper cites Smote: synthetic minority over-sampling technique,.

Modelling Segmented Cardiotocography Time-Series Signals Using One-Dimensional Convolutional Neural Networks for the Early Detection of Abnormal Birth Outcomes Smote: synthetic minority over-sampling technique,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:52:15.360777Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 9b6593c8-f1fa-472c-92fe-0d75953d227f · outbound

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

Modelling Segmented Cardiotocography Time-Series Signals Using One-Dimensional Convolutional Neural Networks for the Early Detection of Abnormal Birth Outcomes Machine learning ensem- ble modelling to classify caesarean section and vaginal delivery types using cardiotocography traces,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:52:15.332573Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 05203d4c-8f9d-406c-aedd-a1a95dcd95b6 · outbound

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

Modelling Segmented Cardiotocography Time-Series Signals Using One-Dimensional Convolutional Neural Networks for the Early Detection of Abnormal Birth Outcomes Novel leakage detection by ensemble cnn-svm and graph-based localiza- tion in water distribution systems,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:52:15.304954Z

Source-reported events for the cited work

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Observation 2c2e87fe-bcb3-4dc5-8af0-3b399299fca6 · outbound

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

Modelling Segmented Cardiotocography Time-Series Signals Using One-Dimensional Convolutional Neural Networks for the Early Detection of Abnormal Birth Outcomes Classiﬁcation of caesarean section and normal vagi- nal deliveries using foetal heart rate signals and advanced ma- chine learning algorithms,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:52:15.276634Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation d1b38dd4-07ee-408c-8ca7-0a2892a00989 · outbound

This paper cites Learning to Exploit Invariances in Clinical Time-Series Data using Sequence Transformer Networks.

Modelling Segmented Cardiotocography Time-Series Signals Using One-Dimensional Convolutional Neural Networks for the Early Detection of Abnormal Birth Outcomes Learning to Exploit Invariances in Clinical Time-Series Data using Sequence Transformer Networks

Reference 21

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This paper cites Chapter 3 - deep learning of brain images and its application to multiple sclerosis,.

Modelling Segmented Cardiotocography Time-Series Signals Using One-Dimensional Convolutional Neural Networks for the Early Detection of Abnormal Birth Outcomes Chapter 3 - deep learning of brain images and its application to multiple sclerosis,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:52:15.235654Z

Source-reported events for the cited work

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Observation 085ff8e6-6a0b-4f17-a96f-5962ebb30dd2 · outbound

This paper cites TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems.

Modelling Segmented Cardiotocography Time-Series Signals Using One-Dimensional Convolutional Neural Networks for the Early Detection of Abnormal Birth Outcomes TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems

Reference 23

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Observation d608beda-042c-49fb-8868-9985b5b47d55 · outbound

This paper cites Keras: The python deep learning library,.

Modelling Segmented Cardiotocography Time-Series Signals Using One-Dimensional Convolutional Neural Networks for the Early Detection of Abnormal Birth Outcomes Keras: The python deep learning library,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:52:15.191219Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Pith citing papers

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