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@Article{Georgoulas2020_SIVP,
    author = {<NAME>. and <NAME> <NAME>. and <NAME>. and <NAME>. and <NAME>. and <NAME>. and <NAME>.},
    title = {An exploratory approach to fetal heart rate--pH-based systems},
    journal = {Signal, Image and Video Processing},
    year = {2020},
    month = {Jun},
    issn = {1863-1711},
    abstract = {This paper presents an exploratory approach of the fetal heart rate (FHR) analysis, aiming to highlight potential limitations of the current predictive modeling attempts. To do so, a set of features that are usually encountered in FHR analysis as well as features extracted using a variant of symbolic aggregate approximation were projected onto a lower-dimensional space where patterns can easily be discerned. The results show, both in a qualitative and a quantitative manner, that there is high overlap between the classes that are formed using solely the umbilical cord pH information, irrespective of the selected dimensionality reduction method. These findings suggest that there is probably a limit to the performance expectation of the current pH-based systems and that alternative approaches should be also pursued to enhance the utility of computer-based decision support technologies.},
    day = {22},
    doi = {10.1007/s11760-020-01727-y},
    url = {https://doi.org/10.1007/s11760-020-01727-y},
    bib = {Georgoulas2020_SIVP.bib},
    img = {Georgoulas2020_SIVP.png}
}
@Article{Georgieva_2019_Acta_Obstet_Gynecol_Scand,
    author = {<NAME>. and <NAME> <NAME>.},
    title = {Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK)},
    journal = {Acta Obstetricia et Gynecologica Scandinavica},
    year = {2019},
    volume = {98},
    number = {9},
    pages = {1207-1217},
    abstract = {Abstract The second Signal Processing and Monitoring in Labor workshop gathered researchers who utilize promising new research strategies and initiatives to tackle the challenges of intrapartum fetal monitoring. The workshop included a series of lectures and discussions focusing on: new algorithms and techniques for cardiotocogoraphy (CTG) and electrocardiogram acquisition and analyses; the results of a CTG evaluation challenge comparing state-of-the-art computerized methods and visual interpretation for the detection of arterial cord pH <7.05 at birth; the lack of consensus about the role of intrapartum acidemia in the etiology of fetal brain injury; the differences between methods for CTG analysis “mimicking” expert clinicians and those derived from “data-driven” analyses; a critical review of the results from two randomized controlled trials testing the former in clinical practice; and relevant insights from modern physiology-based studies. We concluded that the automated algorithms performed comparably to each other and to clinical assessment of the CTG. However, the sensitivity and specificity urgently need to be improved (both computerized and visual assessment). Data-driven CTG evaluation requires further work with large multicenter datasets based on well-defined labor outcomes. And before first tests in the clinic, there are important lessons to be learnt from clinical trials that tested automated algorithms mimicking expert CTG interpretation. In addition, transabdominal fetal electrocardiogram monitoring provides reliable CTG traces and variability estimates; and fetal electrocardiogram waveform analysis is subject to promising new research. There is a clear need for close collaboration between computing and clinical experts. We believe that progress will be possible with multidisciplinary collaborative research.},
    doi = {https://doi.org/10.1111/aogs.13639},
    eprint = {https://obgyn.onlinelibrary.wiley.com/doi/pdf/10.1111/aogs.13639},
    keywords = {artificial intelligence, cardiotocography, electronic fetal monitoring, health data, hypoxic-ischemic encephalopathy, intrapartum care, sensitivity, specificity},
    url = {https://obgyn.onlinelibrary.wiley.com/doi/abs/10.1111/aogs.13639},
    bib = {Georgieva_2019_Acta_Obstet_Gynecol_Scand.bib},
    file = {https://obgyn.onlinelibrary.wiley.com/doi/epdf/10.1111/aogs.13639},
    img = {Georgieva_2019_Acta_Obstet_Gynecol_Scand.png},
    selected = {false}
}
@Article{Abry2018_bpex,
    author = {<NAME> and <NAME> and <NAME> and <NAME> and <NAME> and <NAME>},
    title = {Sparse learning for Intrapartum fetal heart rate analysis},
    journal = {Biomedical Physics & Engineering Express},
    year = {2018},
    volume = {4},
    number = {3},
    pages = {034002},
    abstract = {Fetal Heart Rate (FHR) monitoring is used during delivery for fetal well-being assessment. Classically based on the visual evaluation of FIGO criteria, FHR characterization remains a challenging task that continuously receives intensive research efforts. Intrapartum FHR analysis is further complicated by the two different stages of labor (dilation and active pushing). Research works aimed at devising automated acidosis prediction procedures are either based on designing new advanced signal processing analyses or on efficiently combining a large number of features proposed in the literature. Such multi-feature procedures either rely on a prior feature selection step or end up with decision rules involving long lists of features. This many-feature outcome rule does not permit to easily interpret the decision and is hence not well suited for clinical practice. Machine-learning-based decision-rule assessment is often impaired by the use of different, proprietary and small databases, preventing meaningful comparisons of results reported in the literature. Here, sparse learning is promoted as a way to perform jointly feature selection and acidosis prediction, hence producing an optimal decision rule based on as few features as possible. Making use of a set of 20 features (gathering ‘FIGO-like’ features, classical spectral features and recently proposed scale-free features), applied to two large-size (respectively ~1800 and ~500 subjects), well-documented databases, collected independently in French and Czech hospitals, the benefits of sparse learning are quantified in terms of: (i) accounting for class imbalance (few acidotic subjects), (ii) producing simple and interpretable decision rules, (iii) evidences for differences between the temporal dynamics of active pushing and dilation stages, and (iv) of validity/generalizability of decision rules learned on one database and applied to the other one.},
    bib = {Abry_2018_Biomed_Phys_Eng_Express_4_034002.bib},
    file = {Abry_2018_Biomed_Phys_Eng_Express_4_034002.pdf:PDF},
    img = {Abry_2018_Biomed_Phys_Eng_Express_4_034002.png},
    selected = {true},
    supp = {http://people.ciirc.cvut.cz/\~{}spilkjir/Abry2018BPEXresults.html},
    url = {http://stacks.iop.org/2057-1976/4/i=3/a=034002},
    selected = {true}
}

@Article{Georgoulas2017_ht,
  author   = {<NAME> <NAME> <NAME>.},
  title    = {Investigating pH based evaluation of fetal heart rate (FHR0 recordings},
  journal  = {Health and Technology},
  year     = {2017},
  month    = {Jul},
  issn     = {2190-7196},
  abstract = {Cardiotocography (CTG) is a standard tool for the assessment of fetal well-being during pregnancy and delivery. However, its interpretation is associated with high inter- and intra-observer variability. Since its introduction there have been numerous attempts to develop computerized systems assisting the evaluation of the CTG recording. Nevertheless these systems are still hardly used in a delivery ward. Two main approaches to computerized evaluation are encountered in the literature; the first one emulates existing guidelines, while the second one is more of a data-driven approach using signal processing and computational methods. The latter employs preprocessing, feature extraction/selection and a classifier that discriminates between two or more classes/conditions. These classes are often formed using the umbilical cord artery pH value measured after delivery. In this work an approach to Fetal Heart Rate (FHR) classification using pH is presented that could serve as a benchmark for reporting results on the unique open-access CTU-UHB CTG database, the largest and the only freely available database of this kind. The overall results using a very small number of features and a Least Squares Support Vector Machine (LS-SVM) classifier, are in accordance to the ones encountered in the literature and outperform the results of a baseline classification scheme proving the utility of using advanced data processing methods. Therefore the achieved results can be used as a benchmark for future research involving more informative features and/or better classification algorithms.},
  bib      = {Georgoulas2017_ht.bib},
  day      = {04},
  doi      = {10.1007/s12553-017-0201-7},
  img      = {Georgoulas2017_ht.png},
  file      = {https://link.springer.com/content/pdf/10.1007/s12553-017-0201-7.pdf},
  url      = {http://dx.doi.org/10.1007/s12553-017-0201-7},
}

@Article{Spilka2017jbhi,
    author = {<NAME> and <NAME> and <NAME> and <NAME> and <NAME> and <NAME>},
    title = {Sparse Support Vector Machine for Intrapartum Fetal Heart Rate Classification},
    journal = {IEEE Journal of Biomedical and Health Informatics},
    year = {2017},
    volume = {21},
    number = {3},
    pages = {664-671},
    month = {May},
    issn = {2168-2194},
    abstract = {Fetal heart rate (FHR) monitoring is routinely used in clinical practice to help obstetricians assess fetal health status during delivery. However, early detection of fetal acidosis that allows relevant decisions for operative delivery remains a challenging task, receiving considerable attention. This contribution promotes sparse support vector machine classification that permits to select a small number of relevant features and to achieve efficient fetal acidosis detection. A comprehensive set of features is used for FHR description, including enhanced and computerized clinical features, frequency domain, and scaling and multifractal features, all computed on a large (1288 subjects) and well-documented database. The individual performance obtained for each feature independently is discussed first. Then, it is shown that the automatic selection of a sparse subset of features achieves satisfactory classification performance (sensitivity 0.73 and specificity 0.75, outperforming clinical practice). The subset of selected features (average depth of decelerations $textrm {MAD} _ {rm dtrd} $, baseline level $beta _0$ , and variability $H$) receives simple interpretation in clinical practice. Intrapartum fetal acidosis detection is improved in several respects: A comprehensive set of features combining clinical, spectral, and scale-free dynamics is used; an original multivariate classification targeting both sparse feature selection and high performance is devised; state-of-the-art performance is obtained on a much larger database than that generally studied with description of common pitfalls in supervised classification performance assessments.},
    doi = {10.1109/JBHI.2016.2546312},
    keywords = {Databases;Feature extraction;Fetal heart rate;Fractals;Informatics;Monitoring;Support vector machines;Biomedical signal processing;feature selection;fetal heart rate;sparse support vector machine (S-SVM);supervised classification},
    bib = {Spilka2017jbhi.bib},
    img = {Spilka2017jbhi.png},
    file = {Spilka2017jbhi_preprint.pdf},
    url = {https://ieeexplore.ieee.org/document/7440787},
    selected = {true}
}
@InBook{Spilka2016,
    pages = {1199--1204},
    title = {Intrapartum Fetal Heart Rate Classification: Cross-Database Evaluation},
    publisher = {Springer International Publishing},
    year = {2016},
    author = {<NAME> <NAME> <NAME> <NAME>. and <NAME>. and <NAME>.},
    editor = {Kyriacou, Efthyvoulos and <NAME> and <NAME>.},
    address = {Cham},
    isbn = {978-3-319-32703-7},
    booktitle = {XIV Mediterranean Conference on Medical and Biological Engineering and Computing 2016: MEDICON 2016, March 31st-April 2nd 2016, Paphos, Cyprus},
    doi = {10.1007/978-3-319-32703-7_232},
    url = {http://dx.doi.org/10.1007/978-3-319-32703-7_232},
    bib = {Spilka2016medicon.bib},
    img = {Spilka2016medicon.png},
    selected = {true}
}

@Article{Doret2015plosone,
    author = {<NAME> and <NAME> and <NAME>\'{a}\v{c}ek and <NAME>\c{c}alves and <NAME>},
    title = {Fractal Analysis and Hurst Parameter for intrapartum fetal heart rate variability analysis: A versatile alternative to Frequency bands and LF/HF ratio.},
    journal = {PLoS ONE},
    year = {2015},
    volume = {10},
    number = {8},
    pages = {e0136661},
    month = {08},
    abstract = {Background: The fetal heart rate (FHR) is commonly monitored during labor to detect early fetal acidosis. FHR variability is traditionally investigated using Fourier transform, often with adult predefined frequency band powers and the corresponding LF/HF ratio. However, fetal conditions differ from adults and modify spectrum repartition along frequencies. Aims: This study questions the arbitrariness definition and relevance of the frequency band splitting procedure, and thus of the calculation of the underlying LF/HF ratio, as efficient tools for characterizing intrapartum FHR variability. Study design: The last 30 minutes before delivery of the intrapartum FHR were analyzed. Subjects: Case-control study. A total of 45 singletons divided into two groups based on umbilical cord arterial pH: the Index group with pH ≤ 7.05 (n = 15) and Control group with pH > 7.05 (n = 30). Outcome measures: Frequency band-based LF/HF ratio and Hurst parameter. Results: This study shows that the intrapartum FHR is characterized by fractal temporal dynamics and promotes the Hurst parameter as a potential marker of fetal acidosis. This parameter preserves the intuition of a power frequency balance, while avoiding the frequency band splitting procedure and thus the arbitrary choice of a frequency separating bands. The study also shows that extending the frequency range covered by the adult-based bands to higher and lower frequencies permits the Hurst parameter to achieve better performance for identifying fetal acidosis. Conclusions: The Hurst parameter provides a robust and versatile tool for quantifying FHR variability, yields better acidosis detection performance compared to the LF/HF ratio, and avoids arbitrariness in spectral band splitting and definitions.},
    doi = {10.1371/journal.pone.0136661},
    publisher = {Public Library of Science},
    url = {http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0136661},
    bib = {Doret2015plosone.bib},
    file = {https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0136661&type=printable},
    img = {Doret2015plosone.png},
    selected = {true}
}

@Article{Hruban2015jep,
    author = {<NAME>. and <NAME>. and <NAME>. and Janků , P. and Huptych, M. and Burša, M. and Hudec, A. and Kacerovský , M. and Koucký , M. and Procházka, M. and Korečko, V. and Seget'a, J. and Šimetka, O. and Měchurová , A. and Lhotská , L.},
    title = {Agreement on intrapartum cardiotocogram recordings between expert obstetricians},
    journal = {Journal of Evaluation in Clinical Practice},
    abstract = {Rationale, aims and objectives: To evaluate obstetricians' inter- and intra-observer agreement on intrapartum cardiotocogram (CTG) recordings and to examine obstetricians' evaluations with respect to umbilical artery pH and base deficit. Methods: Nine experienced obstetricians annotated 634 intrapartum CTG recordings. The evaluation of each recording was divided into four steps: evaluation of two 30-minute windows in the first stage of labour, evaluation of one window in the second stage of labour and labour outcome prediction. The complete set of evaluations used for this experiment is available online. The inter- and intra-observer agreement was evaluated using proportion of agreement and kappa coefficient. Clinicians' sensitivity and specificity was computed with respect to umbilical artery pH, base deficit and to Apgar score at the fifth minute. Results: The overall proportion of agreement between clinicians reached 48% with 95% confidence intervals (CI) (CI: 47-50). Regarding the different classes, proportion of agreement ranged from 57% (CI: 54-60) for normal to 41% (CI: 36-46) for pathological class. The sensitivity of clinicians' majority vote to objective outcome was 39% (CI: 16-63) for the umbilical artery base deficit and 27% (CI: 16-42) for pH. The specificity was 89% (CI: 86-92) for both types of objective outcome. Conclusions: The reported inter-/intra-observer variability is large and this holds irrespective of clinicians' experience or work place. The results support the need of modernized guidelines for CTG evaluation and/or objectivization and repeatability by introduction of a computerized approach that could standardize the process of CTG evaluation within the delivery ward.},
    year = {2015},
    volume = {21},
    number = {4},
    pages = {694--702},
    issn = {1365-2753},
    doi = {10.1111/jep.12368},
    keywords = {cardiotocography, fetal heart rate, inter-observer variability, intra-observer variability},
    url = {http://dx.doi.org/10.1111/jep.12368},
    bib = {Hruban2015jep.bib},
    img = {Hruban2015jep.png},
    selected = {true}
}
@Article{Karvelis2015pm,
    author = {<NAME> and <NAME> and <NAME> and <NAME> and <NAME> and <NAME>},
    title = {Combining latent class analysis labeling with multiclass approach for fetal heart rate categorization},
    journal = {Physiological Measurement},
    year = {2015},
    volume = {36},
    number = {5},
    pages = {1001},
    abstract = {The most common approach to assess fetal well-being during delivery is monitoring of fetal heart rate and uterine contractionsâthe cardiotocogram (CTG). Nevertheless, 40 years since the introduction of CTG to clinical practice, its evaluation is still challenging with high inter- and intra-observer variability. Therefore the development of more objective methods has become an issue of major importance in the field. Unlike the usually proposed approaches to assign classes for classification methods that rely either on biochemical parameters (e.g. pH value) or a simple aggregation of expert judgment, this work investigates the use of an alternative labeling system using latent class analysis (LCA) along with an ordinal classification scheme. The study is performed on a well-documented open-access database, where nine expert obstetricians provided CTG annotations. The LCA is proposed here to produce more objective class labels while the ordinal classification aims to explore the natural ordering, and representation of increased severity, for obtaining the final results. The results are promising suggesting that more effort should be put into this proposed approach.},
    owner = {jirka},
    timestamp = {2015.05.27},
    url = {http://stacks.iop.org/0967-3334/36/i=5/a=1001},
    bib = {Karvelis2015pm.bib},
    img = {Karvelis2015pm.png},
    selected = {true}
}
@InCollection{Leonarduzzi2015,
    author = {<NAME> <NAME> <NAME> <NAME> <NAME> <NAME> <NAME>.},
    title = {p-Leader Based Classification of First Stage Intrapartum Fetal HRV},
    abstract = {Interpretation and analysis of intrapartum fetal heart rate, enabling early detection of fetal acidosis, remains a challenging signal processing task. Recently, a variant of the wavelet-based multifractal analysis, based on p-exponents and p-leaders, which provides a rich framework for data regularity analysis, has been proposed. The present contribution aims at studying the benefits of using the p-leader multifractal formalism for discrimination of intrapartum fetal heart rate. First, a dependence on p of the multifractal properties of data is evidenced and interpreted. Second, classification between healthy subjects and fetuses suffering from acidosis is shown to have satisfactory performance that increases when p is decreased.},
    booktitle = {VI Latin American Congress on Biomedical Engineering CLAIB 2014, Paraná, Argentina 29, 30 & 31 October 2014},
    publisher = {Springer International Publishing},
    year = {2015},
    editor = {<NAME> and <NAME>},
    volume = {49},
    series = {IFMBE Proceedings},
    pages = {504-507},
    isbn = {978-3-319-13116-0},
    keywords = {wavelets; multifractal analysis; p-leaders; intrapartum; fetal heart rate variability; acidosis detection},
    language = {English},
    owner = {jirka},
    timestamp = {2015.05.27},
    url = {https://link.springer.com/chapter/10.1007/978-3-319-13117-7_129},
    file = {https://hal.archives-ouvertes.fr/hal-01500525/document},
    bib = {Leonarduzzi2015claib.bib},
    img = {Leonarduzzi2015claib.png}
}

@InProceedings{Leonarduzzi2015embc,
    author = {<NAME> <NAME> <NAME> <NAME> <NAME> <NAME> <NAME>.},
    title = {p-leader Multifractal Analysis and Sparse {SVM} for Intrapartum Fetal Acidosis Detection},
    abstract = {Interpretation and analysis of intrapartum fetal heart rate, enabling early detection of fetal acidosis, remains a challenging signal processing task. Among the many strategies that were used to tackle this problem, scale-invariance and multifractal analysis stand out. Recently, a new and promising variant of multifractal analysis, based on p-leaders, has been proposed. In this contribution, we use sparse support vector machines applied to p-leader multifractal features with a double aim: Assessment of the features actually contributing to classification; Assessment of the contribution of non linear features (as opposed to linear ones) to classification performance. We observe and interpret that the classification rate improves when small values of the tunable parameter p are used.},
    booktitle = {Ann Int Conf IEEE Eng Med Biol Soc (EMBC)},
    year = {2015},
    pages = {1971 -- 1974},
    month = {Aug.},
    organization = {Milan, Italy},
    issn = {1557-170X},
    owner = {jirka},
    timestamp = {2015.04.28},
    url = {https://pubmed.ncbi.nlm.nih.gov/26736671/},
    file = {https://hal.archives-ouvertes.fr/hal-01511878/document},
    bib = {Leonarduzzi2015embc.bib},
    img = {Leonarduzzi2015embc.png}
}
@InProceedings{Spilka2015embc,
    author = {<NAME>. and <NAME>. and <NAME> <NAME> <NAME>. and <NAME>.},
    title = {Intrapartum Fetal Heart Rate Classification from Trajectory in Sparse SVM feature space},
    abstract = {Intrapartum fetal heart rate (FHR) constitutes a prominent source of information for the assessment of fetal reactions to stress events during delivery. Yet, early detection of fetal acidosis remains a challenging signal processing task. The originality of the present contribution are three-fold: multiscale representations and wavelet leader based multifractal analysis are used to quantify FHR variability ; Supervised classification is achieved by means of Sparse-SVM that aim jointly to achieve optimal detection performance and to select relevant features in a multivariate setting ; Trajectories in the feature space accounting for the evolution along time of features while labor progresses are involved in the construction of indices quantifying fetal health. The classification performance permitted by this combination of tools are quantified on a intrapartum FHR large database (≃ 1250 subjects) collected at a French academic public hospital.},
    booktitle = {Ann Int Conf IEEE Eng Med Biol Soc (EMBC)},
    year = {2015},
    pages = {2335--2338},
    month = {August},
    organization = {Milan, Italy},
    issn = {1557-170X},
    owner = {jirka},
    timestamp = {2015.04.28},
    url = {https://ieeexplore.ieee.org/document/7318861},
    bib = {Spilka2015embc.bib},
    img = {Spilka2015embc.png}
}

@Article{Bursa2015jal,
  author    = {<NAME>\'{a}, <NAME>\'{a}\v{c}<NAME>. and Jank\r{u}, P. and <NAME>.},
  title     = {{I}nformation retrieval from hospital information system: {I}ncreasing effectivity using swarm intelligence},
  journal   = {Journal of Applied Logic},
  year      = {2015},
  volume    = {13},
  number    = {2, Part A},
  pages     = {126 - 137},
  issn      = {1570-8683},
  abstract  = {This paper details the process of mining information from a hospital information system that has been designed approximately 15 years ago. The information is distributed within database tables in large textual attributes with a free structure. Information retrieval from these information is necessary for complementing cardiotocography signals with additional information that is to be implemented in a decision support system. The basic statistical overview (n-gram analysis) helped with the insight into data structure, however more sophisticated methods have to be used as human (and expert) processing of the whole data were out of consideration: over 620,000 text fields contained text reports in natural language with (many) typographical errors, duplicates, ambiguities, syntax errors and many (nonstandard) abbreviations. There was a strong need to efficiently determine the overall structure of the database and discover information that is important from the clinical point of view. We have used three different methods: k-means, self-organizing map and a self-organizing approach inspired by ant-colonies that performed clustering of the records. The records were visualized and revealed the most prominent information structure(s) that were consulted with medical experts and served for further mining from the database. The outcome of this task is a set of ordered or nominal attributes with a structural information that is available for rule discovery mining and automated processing for the research of asphyxia prediction during delivery. The proposed methodology has significantly reduced the processing time of loosely structured textual records for both \{IT\} and medical experts. },
  doi       = {http://dx.doi.org/10.1016/j.jal.2014.11.006},
  img       = {Bursa2015.png},
  keywords  = {Swarm intelligence},
  owner     = {jirka},
  selected  = {false},
  timestamp = {2015.05.27},
  url       = {http://www.sciencedirect.com/science/article/pii/S1570868314000809},
  bib = {Bursa2015.bib},
  img = {Bursa2015.png},
  selected = {false}
}
@Article{Spilka2014,
    author  = {<NAME> and <NAME> and <NAME> and <NAME> and <NAME> and <NAME> and <NAME> and <NAME>},
    title   = {Analysis of obstetricians’ decision making on CTG recordings},
    abstract = {Interpretation of cardiotocogram (CTG) is a difficult task since its evaluation is complicated by a great inter- and intra-individual variability. Previous studies have predominantly analyzed clinicians’ agreement on CTG evaluation based on quantitative measures (e.g. kappa coefficient) that do not offer any insight into clinical decision making. In this paper we aim to examine the agreement on evaluation in detail and provide data-driven analysis of clinical evaluation. For this study, nine obstetricians provided clinical evaluation of 634 CTG recordings (each ca. 60 min long). We studied the agreement on evaluation and its dependence on the increasing number of clinicians involved in the final decision. We showed that despite of large number of clinicians the agreement on CTG evaluations is difficult to reach. The main reason is inherent inter- and intra-observer variability of CTG evaluation. Latent class model provides better and more natural way to aggregate the CTG evaluation than the majority voting especially for larger number of clinicians. Significant improvement was reached in particular for the pathological evaluation – giving a new insight into the process of CTG evaluation. Further, the analysis of latent class model revealed that clinicians unconsciously use four classes when evaluating CTG recordings, despite the fact that the clinical evaluation was based on FIGO guidelines where three classes are defined.},
    journal = {Journal of Biomedical Informatics},
    year    = {2014},
    volume  = {51},
    number  = {0},
    pages   = {72-79},
    issn    = {1532-0464},
    doi     = {http://dx.doi.org/10.1016/j.jbi.2014.04.010},
    url     = {http://www.sciencedirect.com/science/article/pii/S1532046414000951},
    file = {Spilka2014_JBI_AAM.pdf},
    bib = {Spilka2014_JBI.bib},
    img = {Spilka2014_JBI.png},
    selected = {true}
}
@Article{Chudacek2014,
    author   = {Chud\'{a}\v{c}ek, V and Spilka, <NAME>{s}a, M and Jank\r{u}, P and Hruban, L and Huptych, M and Lhotsk\'{a}, L},
    title    = {Open access intrapartum CTG database},
    journal  = {BMC Pregnancy and Childbirth},
    year     = {2014},
    volume   = {14},
    number   = {1},
    pages    = {16},
    issn     = {1471-2393},
    abstract = {BACKGROUND:Cardiotocography (CTG) is a monitoring of fetal heart rate and uterine contractions. Since 1960 it is routinely used by obstetricians to assess fetal well-being. Many attempts to introduce methods of automatic signal processing and evaluation have appeared during the last 20 years, however still no significant progress similar to that in the domain of adult heart rate variability, where open access databases are available (e.g. MIT-BIH), is visible. Based on a thorough review of the relevant publications, presented in this paper, the shortcomings of the current state are obvious. A lack of common ground for clinicians and technicians in the field hinders clinically usable progress. Our open access database of digital intrapartum cardiotocographic recordings aims to change that.DESCRIPTION:The intrapartum CTG database consists in total of 552 intrapartum recordings, which were acquired between April 2010 and August 2012 at the obstetrics ward of the University Hospital in Brno, Czech Republic. All recordings were stored in electronic form in the OB TraceVue(R);system. The recordings were selected from 9164 intrapartum recordings with clinical as well as technical considerations in mind. All recordings are at most 90 minutes long and start a maximum of 90 minutes before delivery. The time relation of CTG to delivery is known as well as the length of the second stage of labor which does not exceed 30 minutes. The majority of recordings (all but 46 cesarean sections) is - on purpose - from vaginal deliveries. All recordings have available biochemical markers as well as some more general clinical features. Full description of the database and reasoning behind selection of the parameters is presented in the paper.CONCLUSION:A new open-access CTG database is introduced which should give the research community common ground for comparison of results on reasonably large database. We anticipate that after reading the paper, the reader will understand the context of the field from clinical and technical perspectives which will enable him/her to use the database and also understand its limitations.},
    doi      = {10.1186/1471-2393-14-16},
    pubmedid = {24418387},
    url = {http://www.biomedcentral.com/1471-2393/14/16},
    bib = {Chudacek2014_bmc.bib},
    file = {https://bmcpregnancychildbirth.biomedcentral.com/track/pdf/10.1186/1471-2393-14-16.pdf},
    img = {Chudacek2014.png},
    supp = {https://physionet.org/content/ctu-uhb-ctgdb/1.0.0/},
    selected = {true}
}
@InProceedings{Spilka2014_cinc,
    author = {<NAME>. and <NAME>. and <NAME>. and <NAME>.},
    title = {Impacts of First and Second Labour Stages on Hurst Parameter based Intrapartum Fetal Heart Rate Analysis},
    abstract = {Intrapartum fetal heart rate (FHR), routinely monitored in daily obstetrical practice, enables early detection of fetal asphyxia and thus prevention of labour adverse outcomes. FHR variability (FHRV) constitutes an essential characterization of fetal well-being; fetuses with large FHRV are unlikely to be at risk of brain injury. This study investigates the impacts of labour first and second stages on the characterization of FHR temporal dynamics as well as on the discrimination of healthy from acidotic fetuses. FHR temporal dynamics are quantified using Hurst parameter, H, practically estimated within a wavelet framework. Analyses are performed on first and second stages, over a large (3049 records) and well documented database, collected at Hôpital Femme-Mère-Enfant, in Lyon, France. It is observed that Ĥ, for healthy fetuses, remains constant along first stage and then significantly increases during second stage; while, for acidotic fetuses, Ĥ increases to larger values earlier within first stage. This may indicate that the defense mechanism classically at work during second stage as a reaction to excessive stress, is already in use during first stage for acidotic fetuses. Detection performance curves (ROC) also show that second stage Ĥ permits slightly better discrimination of healthy from acidotic fetuses, compared to first stage Ĥ.},
    booktitle = {Computing in Cardiology},
    year = {2014},
    volume = {41},
    pages = {777-780},
    owner = {jirka},
    timestamp = {2014.12.04},
    url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7043158},
    file = {http://cinc.org/archives/2014/pdf/0777.pdf},
    bib = {Spilka2014cinc.bib},
    img = {Spilka2014cinc.png}
}
@InProceedings{spilka2014nearest,
    author = {<NAME> <NAME> <NAME> <NAME> <NAME>},
    title = {Nearest-neighbor based wavelet entropy rate measures for intrapartum fetal heart rate variability},
    abstract = {The interpretation and analysis of intrapartum fetal heart rate (FHR), enabling early detection of fetal acidosis, remains a challenging signal processing task. The ability of entropy rate measures, amongst other tools, to characterize temporal dynamics of FHR variability and to discriminate non-healthy fetuses has already been massively investigated. The present contribution aims first at illustrating that a k-nearest neighbor procedure yields estimates for entropy rates that are robust and well-suited to FHR variability (compared to the more commonly used correlation-integral algorithm). Second, it investigates how entropy rates measured on multiresolution wavelet and approximation coefficients permit to improve classification performance. To that end, a supervised learning procedure is used, that selects the time scales at which entropy rates contribute to discrimination. Significant conclusions are obtained from a high quality scalp electrode database of nearly two thousands subjects collected in a French public university hospital.},
    booktitle = {Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE},
    year = {2014},
    pages = {2813--2816},
    organization = {IEEE},
    owner = {jirka},
    timestamp = {2014.12.05},
    url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6944208&searchWithin%3Dp_Authors%3A.QT.Spilka%2C+J..QT.},
    bib = {Spilka2014embc.bib},
    code = {https://github.com/jirispilka/fhr-embc14-entropy},
    img = {Spilka2014embc.png}
}
@Article{Spilka2012bspc,
    author    = {<NAME> Lhotská, <NAME> <NAME> <NAME>.},
    title     = {Using nonlinear features for fetal heart rate classification},
    abstract = {Fetal heart rate (FHR) is used to evaluate fetal well-being and enables clinicians to detect ongoing hypoxia during delivery. Routine clinical evaluation of intrapartum FHR is based on macroscopic morphological features visible to the naked eye. In this paper we evaluated conventional features and compared them to the nonlinear ones in the task of intrapartum FHR classification. The experiments were performed using a database of 217 FHR records with objective annotations, i.e. pH measurement. We have proven that the addition of nonlinear features improves accuracy of classification. The best classification results were achieved using a combination of conventional and nonlinear features with sensitivity of 73.4%, specificity of 76.3%, and F-measure of 71.9%. The best selected nonlinear features were: Lempel Ziv complexity, Sample entropy, and fractal dimension estimated by Higuchi method. Since the results of automatic signal evaluation are easily reproducible, the process of FHR evaluation can become more objective and may enable clinicians to focus on additional non-cardiotocography parameters influencing the fetus during delivery.},
    journal   = {Biomedical Signal Processing and Control},
    year      = {2012},
    volume    = {7},
    number    = {4},
    pages     = {350--357},
    owner     = {jirka},
    publisher = {Elsevier},
    timestamp = {2013.03.18},
    url = {https://www.sciencedirect.com/science/article/abs/pii/S1746809411000619},
    file = {Spilka2012_AAM.pdf},
    bib = {Spilka2012.bib},
    img = {Spilka2012.png},
    selected = {true}
}
@Article{Chudacek2011physmeas,
    author    = {<NAME>}\v{c}ek and <NAME> and <NAME>} and <NAME>} and <NAME>} and <NAME>},
    title     = {Automatic evaluation of intrapartum fetal heart rate recordings: A comprehensive analysis of useful features},
    abstract = {Cardiotocography is the monitoring of fetal heart rate (FHR) and uterine contractions (TOCO), used routinely since the 1960s by obstetricians to detect fetal hypoxia. The evaluation of the FHR in clinical settings is based on an evaluation of macroscopic morphological features and so far has managed to avoid adopting any achievements from the HRV research field. In this work, most of the features utilized for FHR characterization, including FIGO, HRV, nonlinear, wavelet, and time and frequency domain features, are investigated and assessed based on their statistical significance in the task of distinguishing the FHR into three FIGO classes. We assess the features on a large data set (552 records) and unlike in other published papers we use three-class expert evaluation of the records instead of the pH values. We conclude the paper by presenting the best uncorrelated features and their individual rank of importance according to the meta-analysis of three different ranking methods. The number of accelerations and decelerations, interval index, as well as Lempel–Ziv complexity and Higuchi's fractal dimension are among the top five features.},
    journal   = {Physiological Measurement},
    year      = {2011},
    volume    = {32},
    pages     = {1347--1360},
    owner     = {jirka},
    timestamp = {2011.06.20},
    url       = {http://iopscience.iop.org/0967-3334/32/8/022/},
    bib = {Chudacek2011.bib},
    file = {Chudacek2011_preprint.pdf:PDF},
    img = {Chudacek2011.png},
    pdf = {https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&ved=2ahUKEwjynYzSop7vAhWw-yoKHSv2C_4QFjADegQICRAD&url=http%3A%2F%2Fwww.cinc.org%2F2010%2Fprogram%2F287.pdf&usg=AOvVaw2B53wDDNzFDob-1_8FRhfS},
    selected = {true}
}
@InProceedings{Spilka2010_cinc,
    author    = {<NAME>. and Chud\'{a}\v{c}<NAME>. and Ku\v{z}\'{i}<NAME>. and Lhotsk\'{a}, L. and <NAME>.},
    title     = {Detection of Inferior Myocardial Infarction: A Comparison of Various Decision Systems and Learning Algorithms},
    abstract = {In this work we have focused on classification of inferior myocardial infarction (MI). We compared the best known scoring/coding/decision systems (the Selvester QRS score, the Novacode, and the Siemens 440/740) and several learning algorithms (Ripper, C4.5, and SVM). The decision systems were developed with different purposes (the Selvester for estimation of MI size, the Novacode for clinical and epidemiologic studies, and the Siemens for ECG device Siemens 440/740). In this work we combined these systems with additional simple rules and compared performance to: (i) decision systems alone, (ii) base classifiers (Ripper, C4.5, and SVM). Our database consisted of 2596 ECG records annotated by experienced cardiologist. Among decision systems the Selvester and the Siemens had F-measure 54% and 51%, respectively. Meaning that about 50% of MI's were correctly classified. Even lower F-measure of 39% was obtained by Novacode. Better results were achieved using rule miner Ripper with F-measure of 68%, however, due to a number of rules created, the resulting model was hard to interpret. Last, combination of decision systems with additional simple rules created by AdaBoost yielded the best performance with F-measure 71%, sensitivity (Se) 78%, and specificity (Sp) 95%.},
    booktitle = {Computers in Cardiology},
    year      = {2010},
    volume    = {37},
    owner     = {jirka},
    timestamp = {2010.02.24},
    url = {https://ieeexplore.ieee.org/document/5737962},
    file = {https://www.cinc.org/archives/2010/pdf/0273.pdf},
    bib = {Spilka2010_cinc.bib},
    img = {Spilka2010_cinc.png}
}