Fetal Heart Rate Features of Healthy and Late IUGR Fetuses

Citation Author(s):
Nicolò
Pini
Politecnico di Milano
Maristella
Lucchini
Columbia University Irving Medical Center
Giuseppina
Esposito
Federico II University
Marta
Campanile
Federico II University
Giovanni
Magenes
University of Pavia
Maria G.
Signorini
Politecnico di Milano
Submitted by:
NICOLO PINI
Last updated:
Tue, 03/24/2020 - 21:46
DOI:
10.21227/mzc6-jt52
Data Format:
.xlsx
License:
Creative Commons Attribution
2141 Views
Categories:
Machine Learning
Health
Biomedical and Health Sciences
Signal Processing
Nonlinear signal processing
Keywords:
Late Intrauterine Growth Restriction; Perinatal Medicine; Predictive Monitoring
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Abstract 

The dataset consists of two populations of fetuses: 160 healthy and 102 Late Intra Uterine Growth Restricted (IUGR). Late IUGR is an adverse pathological condition encompassing chronic hypoxia as a consequence of placental insufficiency, resulting in an abnormal rate of fetal growth. In standard clinical practice, Late IUGR diagnosis can only be suspected in the third trimester and ultimately confirmed at birth. This data collection comprises of a set of 31 Fetal Heart Rate (FHR) indices computed at different time scales and domains accompanied by the clinical diagnosis. Antepartum CTG recordings were collected at the Azienda Ospedaliera Universitaria Federico II (Napoli, Italy), during daily routine monitoring. Traces were recorded in a controlled clinical environment, with participants lying supine and undergoing a standard non-stress test protocol using Philips Avalon FM30. The acquired traces are of duration equal to 40 minutes and sampled at 2 Hz.

Each CTG recording was subdivided in epochs of 120 samples (1-min) or 360 samples (3-min) prior to feature extraction. The choice of 1-min or 3-min subintervals is based upon to the different time scales on which FHR indices are computed. Regarding artifacts identification, we discarded segments including more than five consecutive samples or more than 5% of samples (6 FHR values out of 120 points per subinterval or 18 FHR values out of 360 points per subinterval) for which the FHR could not be detected. Lastly, isolated samples of insufficient quality were substituted by the moving average of the nearest five FHR points. The parameters obtained for all the segments of one recording are then averaged to derive a single value for each parameter for any given recording.

Instructions: 

The data for healthy and Late IUGR populations are included in a single .xlsx file.

Participants are listed by rows and features by columns. In the following we report an exhaustive list of features contained in the dataset accompanied by their units, time interval employed for the computation, and scientific literature references: 

 Fetal and Maternal Domains

  • Clinical Diagnosis [HEALTHY/LATE IUGR]: binary variable to report the clinical diagnosis of the participant
  • Gestational Age [days]: gestational age at the time of CTG examination
  • Maternal Age [years]: maternal age at the time of CTG examination
  • Sex [Male (1)/Female (2)]: fetal sex

 

Morphological and Time Domains

  • Mean FHR [bpm] – 1-min epoch: the mean of FHR excluding accelerations and decelerations 
  • Std FHR [bpm] – 1-min epoch: the standard deviation of FHR excluding accelerations and decelerations 
  • DELTA [ms] – 1-min epoch: defined in accordance with [1], [2] excluding accelerations and decelerations 
  • II [] – 1-min epoch: defined in accordance with [1], [2] excluding accelerations and decelerations 
  • STV [ms] – 1-min epoch: defined in accordance with [1], [2] excluding accelerations and decelerations 
  • LTI [ms] – 3-min epoch: defined in accordance with [1], [2] excluding accelerations and decelerations 
  • ACC_L [#] – entire recording: the count of large accelerations defined in accordance with [3], [4] 
  • ACC_S [#] – entire recording: the count of small accelerations defined in accordance with [3], [4] 
  • CONTR [#]– entire recording: the count of contractions defined in accordance with [3], [4] 

 

Frequency Domain 

  • LF [ms²/Hz] – 3-min epoch: defined in accordance with [2], LF band is defined in the range [0.03 - 0.15] Hz 
  • MF [ms²/Hz] – 3-min epoch: defined in accordance with [2], MF band is defined in the range [0.15 - 0.5] Hz 
  • HF [ms²/Hz] – 3-min epoch: defined in accordance with [2], HF band is defined in the range HF [0.5 - 1 Hz] 

 

Complexity Domain 

  • ApEn [bits] – 3-min epoch: defined in accordance with [5], m = 1, r = 0.1*standard deviation of the considered epoch 
  • SampEn [bits] – 3-min epoch: defined in accordance with [6], m = 1, r = 0.1*standard deviation of the considered epoch 
  • LCZ_BIN_0 [bits] – 3-min epoch: defined in accordance with [7], binary coding and p = 0 
  • LCZ_TER_0 [bits] – 3-min epoch: defined in accordance with [7], tertiary coding and p = 0 
  • AC/DC/DR [bpm] – entire recording: defined in accordance with [8]–[10], considering different combinations of parameters T and s, L is constant and equal 100 samples; e.g, AC_T1_s2 is defined as the acceleration capacity computed setting the parameters T = 1 and s = 2

 

References

[1]       D. Arduini, G. Rizzo, A. Piana, P. Bonalumi, P. Brambilla, and C. Romanini, “Computerized analysis of fetal heart rate—Part I: description of the sys- tem (2CTG),” J Matern Fetal Invest, vol. 3, pp. 159–164, 1993.

[2]       M. G. Signorini, G. Magenes, S. Cerutti, and D. Arduini, “Linear and nonlinear parameters for the analysis of fetal heart rate signal from cardiotocographic recordings,” IEEE Trans. Biomed. Eng., vol. 50, no. 3, pp. 365–374, 2003.

[3]       FIGO, “Guidelines for the Use of Fetal Monitoring,” Int. J. Gynecol. Obstet., vol. 25, pp. 159–167, 1986.

[4]       R. Rabinowitz, E. Persitz, and E. Sadovsky, “The relation between fetal heart rate accelerations and fetal movements.,” Obstet. Gynecol., vol. 61, no. 1, pp. 16–18, 1983.

[5]       S. M. Pincus and R. R. Viscarello, “Approximate entropy: a regularity measure for fetal heart rate analysis.,” Obstet. Gynecol., vol. 79, no. 2, pp. 249–55, 1992.

[6]       D. E. Lake, J. S. Richman, M. P. Griffin, and J. R. Moorman, “Sample entropy analysis of neonatal heart rate variability,” Am. J. Physiol. - Regul. Integr. Comp. Physiol., vol. 283, no. 3, pp. R789–R797, 2002.

[7]       A. Lempel and J. Ziv, “On the complexity of finite sequences,” IEEE Trans. Inf. Theory, vol. 22, no. 1, pp. 75–81, 1976.

[8]       A. Bauer et al., “Phase-rectified signal averaging detects quasi-periodicities in non-stationary data,” Phys. A Stat. Mech. its Appl., vol. 364, pp. 423–434, 2006.

[9]       A. Fanelli, G. Magenes, M. Campanile, and M. G. Signorini, “Quantitative assessment of fetal well-being through ctg recordings: A new parameter based on phase-rectified signal average,” IEEE J. Biomed. Heal. Informatics, vol. 17, no. 5, pp. 959–966, 2013.

[10]     M. W. Rivolta, T. Stampalija, M. G. Frasch, and R. Sassi, “Theoretical Value of Deceleration Capacity Points to Deceleration Reserve of Fetal Heart Rate,” IEEE Trans. Biomed. Eng., pp. 1–10, 2019.

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