Identifying fetal yawns based on temporal dynamics of mouth openings: A preterm neonate model using support vector machines (SVMs)
Autoři:
Damiano Menin aff001; Angela Costabile aff002; Flaviana Tenuta aff002; Harriet Oster aff003; Marco Dondi aff001
Působiště autorů:
Dipartimento di Studi Umanistici, Università degli Studi di Ferrara, Ferrara, Italy
aff001; Dipartimento di Culture, Educazione e Società, Università della Calabria, Cosenza, Italy
aff002; School of Professional Studies, New York University, New York City, New York, United States of America
aff003; Department of Psychology, Hunter College, City University of New York, New York City, New York, United States of America
aff004
Vyšlo v časopise:
PLoS ONE 14(12)
Kategorie:
Research Article
prolekare.web.journal.doi_sk:
https://doi.org/10.1371/journal.pone.0226921
Souhrn
Fetal yawning is of interest because of its clinical, developmental and theoretical implications. However, the methodological challenges of identifying yawns from ultrasonographic scans have not been systematically addressed. We report two studies that examined the temporal dynamics of yawning in preterm neonates comparable in developmental level to fetuses observed in ultrasound studies (about 31 weeks PMA). In Study 1 we tested the reliability and construct validity of the only quantitative measure for identifying fetal yawns in the literature, by comparing its scores with a more detailed behavioral coding system (The System for Coding Perinatal Behavior, SCPB) adapted from the comprehensive, anatomically based Facial Action Coding System for Infants and Young Children (Baby FACS). The previously published measure yielded good reliability but poor specificity, resulting in over-representation of yawns. In Study 2 we developed and tested a new machine learning system based on support vector machines (SVM) for identifying yawns. The system displayed excellent specificity and sensitivity, proving it to be a reliable and valid tool for identifying yawns in fetuses and neonates. This achievement represents a first step towards a fully automated system for identifying yawns in the perinatal period.
Klíčová slova:
Neonates – Behavior – Face – Mouth – Support vector machines – Research validity – Fetuses – Coding mechanisms
Zdroje
1. Almli CR, Ball RH, Wheeler ME. Human fetal and neonatal movement patterns: Gender differences and fetal-to-neonatal continuity. Dev Psychobiol, 2001; 38(4): 252–273. doi: 10.1002/dev.1019 11319731
2. Kurjak Ab, Predojević M, Stanojević M, Talić A, Honemeyer Ug, Kadić Ah. The use of 4D imaging in the behavioral assessment of high-risk fetuses. Imaging in Medicine, 2011; 3(5): 557–569.
3. Petrikovsky B, Kaplan G, Holsten N. Fetal yawning activity in normal and high-risk fetuses: a preliminary observation. Ultrasound Obstet Gynecol, 1999; 13(2): 127–130. doi: 10.1046/j.1469-0705.1999.13020127.x 10079492
4. Bertolucci L. Pandiculation: Nature's way of maintaining the functional integrity of the myofascial system?. Journal of Bodywork and Movement Therapies, 2011; 15(3): 268–280. doi: 10.1016/j.jbmt.2010.12.006 21665102
5. Gallup A, Gallup G Jr. Yawning and thermoregulation. Physiology and Behavior, 2008; 95(1–2): 10–16. doi: 10.1016/j.physbeh.2008.05.003 18550130
6. Provine RR. Contagious behavior: an alternative approach to mirror-like phenomena. The Behavioral and brain sciences, 2014; 37(): 216–217. doi: 10.1017/S0140525X13002458 24775173
7. Walusinski O. Yawning: unsuspected avenue for a better understanding of arousal and interoception. Med Hypotheses, 2006; 67(1): 6–14. doi: 10.1016/j.mehy.2006.01.020 16520004
8. Proverbio A. The urge for self and species preservation. Cognitive Neuroscience, 2011; 2(3–4): 244. doi: 10.1080/17588928.2011.618629 24168544
9. Meerloo J. Archaic behavior and the communicative act—The meaning of stretching, yawning, rocking and other fetal behavior in therapy. The Psychiatric Quarterly, 1955; 29(1–4): 60–73.
10. Reissland N, Francis B, Mason J. Development of fetal yawn compared with non-yawn mouth openings from 24–36 weeks gestation. PLoS One, 2012; 7(11): e50569. doi: 10.1371/journal.pone.0050569 23185638
11. Kurjak A, Stanojevic M, Andonotopo W, Salihagic-Kadic A, Carrera JM, Azumendi G. Behavioral pattern continuity from prenatal to postnatal life—a study by four-dimensional (4D) ultrasonography. J Perinat Med, 2004; 32(4): 346–353. doi: 10.1515/JPM.2004.065 15346822
12. van Woerden EE, van Geijn HP, Caron FJ, van der Valk AW, Swartjes JM, Arts NF. Fetal mouth movements during behavioural states 1F and 2F. Eur J Obstet Gynecol Reprod Biol, 1988; 29(2): 97–105. doi: 10.1016/0028-2243(88)90135-9 3056756
13. Kurjak A, Azumendi G, Vecek N, Kupesic S, Solak M, Varga D, Chervenak F. Fetal hand movements and facial expression in normal pregnancy studied by four-dimensional sonography. J Perinat Med, 2003; 31(6): 496–508. doi: 10.1515/JPM.2003.076 14711106
14. Yan F, Dai S-Y, Akther N, Kuno A, Yanagihara T, Hata T. Four-dimensional sonographic assessment of fetal facial expression early in the third trimester. Int J Gynaecol Obstet, 2006; 94(2): 108–113. doi: 10.1016/j.ijgo.2006.05.004 16828774
15. AboEllail MAM, Kanenishi K, Mori N, Mohamed OAK, Hata T. 4D ultrasound study of fetal facial expressions in the third trimester of pregnancy. The Journal of Maternal-Fetal & Neonatal Medicine, 2018; 31(14): 1856–1864.
16. Yigiter AB, Kavak ZN. Normal standards of fetal behavior assessed by four-dimensional sonography. The Journal of Maternal-Fetal & Neonatal Medicine, 2006; 19(11), 707–721, doi: 10.1080/14767050600924129 17127494.
17. de Vries J, Visser G, Prechtl H. The emergence of fetal behaviour. I. Qualitative aspects. Early Human Development, 1982; 7(4): 301–322. doi: 10.1016/0378-3782(82)90033-0 7169027
18. Kanenishi K, Hanaoka U, Noguchi J, Marumo G, Hata T. 4D ultrasound evaluation of fetal facial expressions during the latter stages of the second trimester. Int J Gynaecol Obstet, 2013; 121(3): 257–260. doi: 10.1016/j.ijgo.2013.01.018 23497746
19. Sato M, Kanenishi K, Hanaoka U, Noguchi J, Marumo G, Hata T. 4D ultrasound study of fetal facial expressions at 20–24 weeks of gestation. Int J Gynaecol Obstet, 2014; 126(3): 275–279. doi: 10.1016/j.ijgo.2014.03.036 24996686
20. Gonçalves LF. Three-dimensional ultrasound of the fetus: how does it help?. Pediatric radiology, 2016; 46(): 177–189. doi: 10.1007/s00247-015-3441-6 26829949
21. Walusinski O. Yawning: from birth to senescence. Psychol Neuropsychiatr Vieil, 2006; 4(1): 39–46. 16556517
22. Oster H. Baby FACS: Facial Action Coding System for infants and young children. Unpublished monograph and coding manual, 2015, Revised Edition in preparation.
23. Roodenburg PJ, Wladimiroff JW, van Es A, Prechtl HF. Classification and quantitative aspects of fetal movements during the second half of normal pregnancy. Early Hum Dev, 1991; 25(1): 19–35. doi: 10.1016/0378-3782(91)90203-f 2055173
24. Walusinski O, Kurjak A, Andonotopo W, Azumendi G. Fetal yawning assessed by 3D and 4D sonography. Ultrasound Review of Obstetrics and Gynecology, 2005; 5(3): 210–217.
25. Dondi M, Menin D, Oster H. A System for Coding Perinatal Behavior (SCPB), Supplement to Oster H. Baby FACS: Facial Action Coding System for infants and young children, Monograph and Coding manual. 2015, Revised Edition in preparation).
26. Ekman P, Friesen WV, Hager JC. Facial action coding system (FACS), 2002, available through the Paul Ekman Group, https://www.paulekman.com/facial-action-coding-system/
27. Reissland N, Francis B, Buttanshaw L In: Fetal Development, chap. The fetal observable movement system (FOMS), Springer International Publishing; 2016. 153–176.
28. Wang L Support vector machines: theory and applications. Springer Science & Business Media. 2005.
29. Anderson K, McOwan PW. A real-time automated system for the recognition of human facial expressions. IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics: a publication of the IEEE Systems, Man, and Cybernetics Society, 2006; 36(): 96–105. doi: 10.1109/tsmcb.2005.854502 16468569
30. Bartlett M, Littlewort G, Frank M, Lainscsek C, Fasel I, Movellan J. Recognizing Facial Expression: Machine Learning and Application to Spontaneous Behavior. In: Computer Society Conference on Computer Vision and Pattern Recognition; 2005. doi: 10.1109/cvpr.2005.297
31. Du J, Xu J, Song H, Liu X, Tao C. Optimization on machine learning based approaches for sentiment analysis on HPV vaccines related tweets. Journal of biomedical semantics, 2017; 8: 9. doi: 10.1186/s13326-017-0120-6 28253919
32. Michel P, Kaliouby RE. Real time facial expression recognition in video using support vector machines. In: Proceedings of the 5th international conference on Multimodal interfaces—ICMI '03; 2003, 258–264. doi: 10.1145/958432.958479
33. Valstar MF, Pantic M. Combined support vector machines and hidden markov models for modeling facial action temporal dynamics. In: International Workshop on Human-Computer Interaction; 2007, 118–127.
34. Chang C-C, Lin C-J. LIBSVM: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology (TIST), 2011; 2(3): 27.
35. Chiofalo B, Laganà AS, Vaiarelli A, La Rosa VL, Rossetti D, Palmara V, & & Triolo O (2017). Do miRNAs play a role in fetal growth restriction? A fresh look to a busy corner. BioMed research international, 2017; 2017:6073167, doi: 10.1155/2017/6073167 28466013
36. Dondi M, Gervasi MT, Valente M, Vacca T, Bogana G, De Bellis I, Melappioni S, Tran MR, Oster H. Spontaneous facial expression of distress in fetuses. In De Sousa C & Oliveira AM (Eds), In: Proceedings of the 14th European Conference on Facial Expression, 2014, 34–37, Coimbra: IPCDVS.
37. Rosenstein D & Oster H. Differential facial responses to four basic tastes in newborns. Child Development, 1988; 59, 1555–1568. 3208567
38. Oster H. Emotion in the infant’s face: Insights from the study of infants with facial anomalies. Annals of the New York Academy of Sciences, 2003;.1000, 197–204. doi: 10.1196/annals.1280.024 14766632
39. Messinger DC, Cassell TD, Acosta SI, Ambadar Z, Cohn JF. Infant smiling dynamics and perceived positive emotion. J. Nonverbal Behavior, 2008; 32(3), 133.
40. Ahola Kohut S, Pillai Riddell RR, Flora DB., & Oster H. A longitudinal analysis of the development of infant facial expressions in response to acute pain: Immediate and regulatory expressions. Pain, 2012; 153, 2458–2465. doi: 10.1016/j.pain.2012.09.005 23103435
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