Prenatal anxiety recognition model integrating multimodal physiological signal.
Anxiety model
Emotion recognition
Feature fusion
Multimodal physiological signal
Pregnant woman
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
18 Sep 2024
18 Sep 2024
Historique:
received:
03
06
2024
accepted:
09
09
2024
medline:
19
9
2024
pubmed:
19
9
2024
entrez:
18
9
2024
Statut:
epublish
Résumé
Anxiety among pregnant women can significantly impact their overall well-being. However, the development of data-driven HCI interventions for this demographic is often hindered by data scarcity and collection challenges. In this study, we leverage the Empatica E4 wristband to gather physiological data from pregnant women in both resting and relaxed states. Additionally, we collect subjective reports on their anxiety levels. We integrate features from signals including Blood Volume Pulse (BVP), Skin Temperature (SKT), and Inter-Beat Interval (IBI). Employing a Support Vector Machine (SVM) algorithm, we construct a model capable of evaluating anxiety levels in pregnant women. Our model attains an emotion recognition accuracy of 69.3%, marking achievements in HCI technology tailored for this specific user group. Furthermore, we introduce conceptual ideas for biofeedback on maternal emotions and its interactive mechanism, shedding light on improved monitoring and timely intervention strategies to enhance the emotional health of pregnant women.
Identifiants
pubmed: 39294387
doi: 10.1038/s41598-024-72507-8
pii: 10.1038/s41598-024-72507-8
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
21767Subventions
Organisme : Ningbo Innovation Center, Zhejiang University
ID : 1140557B20220120
Informations de copyright
© 2024. The Author(s).
Références
Reid, H., Power, M. & Cheshire, K. Factors influencing antenatal depression, anxiety and stress. Br. J. Midwifery 17, 501–508 (2009).
doi: 10.12968/bjom.2009.17.8.43643
Van den Bergh PhD, B. The influence of maternal emotions during pregnancy on fetal and neonatal behavior. J. Prenat. Perinat. Psychol. Health 5, 119 (1990).
Glover, V. Maternal depression, anxiety and stress during pregnancy and child outcome; What needs to be done. Best Pract. Res. Clin. Obstet. Gynaecol. 28, 25–35 (2014).
doi: 10.1016/j.bpobgyn.2013.08.017
Milgrom, J. et al. Early intervention to prevent adverse child emotional and behavioural development following maternal depression in pregnancy: study protocol for a randomised controlled trial. BMC Psychol. 11, 1–11 (2023).
doi: 10.1186/s40359-023-01244-w
Kholghi, M. et al. The significance and limitations of monitoring sleep during pregnancy. In 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 6826–6830 (IEEE, 2021).
Jimah, T. et al. A technology-based pregnancy health and wellness intervention (two happy hearts): Case study. JMIR Formative Res. 5, e30991 (2021).
doi: 10.2196/30991
Gupta, Y., Kumar, S. & Mago, V. Pregnancy health monitoring system based on biosignal analysis. In 2019 42nd International Conference on Telecommunications and Signal Processing (TSP), 664–667 (IEEE, 2019).
Carneiro, M. B., Moreira, M. W., Pereira, S. S., Gallindo, E. L. & Rodrigues, J. J. Recommender system for postpartum depression monitoring based on sentiment analysis. In 2020 IEEE International Conference on E-health Networking, Application & Services (HEALTHCOM), 1–6 (IEEE, 2021).
Yan, M. et al. Emotion classification with multichannel physiological signals using hybrid feature and adaptive decision fusion. Biomed. Signal Process. Control 71, 103235 (2022).
doi: 10.1016/j.bspc.2021.103235
Zhang, X. et al. Emotion recognition from multimodal physiological signals using a regularized deep fusion of kernel machine. IEEE Trans. Cybern. 51, 4386–4399 (2020).
doi: 10.1109/TCYB.2020.2987575
Bailón, R., Sornmo, L. & Laguna, P. A robust method for ecg-based estimation of the respiratory frequency during stress testing. IEEE Trans. Biomed. Eng. 53, 1273–1285 (2006).
doi: 10.1109/TBME.2006.871888
Buckwalter, J. G. et al. Pregnancy, the postpartum, and steroid hormones: Effects on cognition and mood. Psychoneuroendocrinology 24, 69–84 (1999).
doi: 10.1016/S0306-4530(98)00044-4
Khan, M. & Sharma, V. Post-partum depressive episodes and bipolar disorder. Lancet 385, 771–772 (2015).
doi: 10.1016/S0140-6736(15)60433-0
Field, T. et al. Prenatal anger effects on the fetus and neonate. J. Obstet. Gynaecol. 22, 260–266 (2002).
doi: 10.1080/01443610220130526
Dunn, C., Hanieh, E., Roberts, R. & Powrie, R. Mindful pregnancy and childbirth: Effects of a mindfulness-based intervention on women’s psychological distress and well-being in the perinatal period. Arch. Womens Ment. Health 15, 139–143 (2012).
doi: 10.1007/s00737-012-0264-4
Abera, M. et al. Effects of relaxation interventions during pregnancy on maternal mental health, and pregnancy and newborn outcomes: A systematic review and meta-analysis. PLoS One 19, e0278432 (2024).
doi: 10.1371/journal.pone.0278432
Evans, K., Spiby, H. & Morrell, C. J. Developing a complex intervention to support pregnant women with mild to moderate anxiety: Application of the medical research council framework. BMC Pregnancy Childbirth 20, 1–12 (2020).
doi: 10.1186/s12884-020-03469-8
Chang, M.-Y., Chen, C.-H. & Huang, K.-F. Effects of music therapy on psychological health of women during pregnancy. J. Clin. Nurs. 17, 2580–2587 (2008).
doi: 10.1111/j.1365-2702.2007.02064.x
Mckellar, L., Steen, M. & N, L. Capture my mood: A feasibility study to develop a visual scale for women to self-monitor their mental wellbeing following birth. Evid. Based Midwifery 15, 54–59 (2017).
Zuccolo, P. F., Xavier, M. O., Matijasevich, A., Polanczyk, G. & Fatori, D. A smartphone-assisted brief online cognitive-behavioral intervention for pregnant women with depression: A study protocol of a randomized controlled trial. Trials 22, 1–19 (2021).
doi: 10.1186/s13063-021-05179-8
Vickery, M. et al. Midwives’ views towards women using mhealth and ehealth to self-monitor their pregnancy: A systematic review of the literature. Eur. J. Midwifery 4 (2020).
Hantsoo, L. et al. A mobile application for monitoring and management of depressed mood in a vulnerable pregnant population. Psychiatr. Serv. 69, 104–107 (2018).
doi: 10.1176/appi.ps.201600582
Santos, I. S. et al. Validation of the edinburgh postnatal depression scale (epds) in a sample of mothers from the 2004 pelotas birth cohort study. Cad. Saude Publica. 23, 2577–2588 (2007).
doi: 10.1590/S0102-311X2007001100005
Maruyama, J. M. et al. Maternal depression trajectories in childhood, subsequent maltreatment, and adolescent emotion regulation and self-esteem: the 2004 pelotas birth cohort. Eur. Child Adolesc. Psychiatry 1–11 (2022).
Shulman, H. B., D’Angelo, D. V., Harrison, L., Smith, R. A. & Warner, L. The pregnancy risk assessment monitoring system (prams): Overview of design and methodology. Am. J. Public Health 108, 1305–1313 (2018).
doi: 10.2105/AJPH.2018.304563
Bachiri, M., Idri, A., Fernández-Alemán, J. L. & Toval, A. Mobile personal health records for pregnancy monitoring functionalities: Analysis and potential. Comput. Methods Programs Biomed. 134, 121–135 (2016).
doi: 10.1016/j.cmpb.2016.06.008
Polsky, S. & Garcetti, R. Cgm, pregnancy, and remote monitoring. Diabetes Technol. Ther. 19, S–49 (2017).
Santur, Y., Santur, S. G. & Karaköse, M. Architecture and implementation of a smart-pregnancy monitoring system using web-based application. Expert Syst. 37 (2019).
Penders, B. J., Altini, M., Van Hoof, C. & Dy, E. Wearable sensors for healthier.
Moreira, M. W. L., Rodrigues, J. J. P. C., Oliveira, A. M. B. & Saleem, K. Smart mobile system for pregnancy care using body sensors. In 2016 International Conference on Selected Topics in Mobile & Wireless Networking (MoWNeT), 1–4. https://doi.org/10.1109/MoWNet.2016.7496609 (2016).
Yu, Q., Aris, I. M., Tan, K. H. & Li, L.-J. Application and utility of continuous glucose monitoring in pregnancy: A systematic review. Front. Endocrinol. 10, 697 (2019).
doi: 10.3389/fendo.2019.00697
Olivarez, S. A. et al. Prospective trial on obstructive sleep apnea in pregnancy and fetal heart rate monitoring. Am. J. Obstet. Gynecol. 202, 552-e1 (2010).
doi: 10.1016/j.ajog.2009.12.008
Ansari, F. A. & Peddi, P. Non-intrusive stress detection based on temporal emotion analysis in videos applying machine learning. Turk. Online J. Qual. Inquiry13 (2022).
Kurniawan, H., Maslov, A. V. & Pechenizkiy, M. Stress detection from speech and galvanic skin response signals. In Proceedings of the 26th IEEE International Symposium on Computer-Based Medical Systems, 209–214. https://doi.org/10.1109/CBMS.2013.6627790 (2013).
Jang, E.-H., Park, B.-J., Kim, S.-H., Eum, Y. & Sohn, J.-H. Identification of the optimal emotion recognition algorithm using physiological signals. In 2011 2nd International Conference on Engineering and Industries (ICEI), 1–6 (2011).
Bornoiu, I.-V. & Grigore, O. Kohonen neural network stress detection using only electrodermal activity features. Adv. Electr. Comput. Eng. 14, 71–78 (2014).
doi: 10.4316/AECE.2014.03009
Mokhayeri, F., Akbarzadeh-T, M.-R. & Toosizadeh, S. Mental stress detection using physiological signals based on soft computing techniques. In 2011 18th Iranian Conference of Biomedical Engineering (ICBME), 232–237. https://doi.org/10.1109/ICBME.2011.6168563 (2011).
Yoo, S. K. et al. Neural network based emotion estimation using heart rate variability and skin resistance. In Advances in Natural Computation (eds Wang, L. et al.) 818–824 (Springer, 2005).
Pollreisz, D. & Taherinejad, N. A simple algorithm for emotion recognition, using physiological signals of a smart watch. 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) 2353–2356 (2017).
Gupta, P., Balaji, S. A., Jain, S. & Yadav, R. Emotion recognition during social interactions using peripheral physiological signals. In Computer Networks and Inventive Communication Technologies: Proceedings of Fourth ICCNCT 2021, 99–112 (Springer, 2022).
Klinkenberg, A. V. et al. Heart rate variability changes in pregnant and non-pregnant women during standardized psychosocial stress. Acta Obstet. Gynecol. Scand. 88, 77–82 (2009).
doi: 10.1080/00016340802566762
Xue, M. et al. Affectivewall: designing collective stress-related physiological data visualization for reflection. IEEE Access 7, 131289–131303 (2019).
doi: 10.1109/ACCESS.2019.2940866
Yu, B., Feijs, L. M., Funk, M. & Hu, J. Designing auditory display of heart rate variability in biofeedback context. In ICAD, 294–298 (2015).
Laohakangvalvit, T. et al. Study on the psychological states of olfactory stimuli using electroencephalography and heart rate variability. Sensors 23, 4026 (2023).
doi: 10.3390/s23084026
Lee, J. & Finkelstein, J. Evaluation of a portable stress management device. In Driving Quality in Informatics: Fulfilling the Promise, 248–252 (IOS Press, 2015).
DUAN, H. et al. Acute stress: Induction, measurement and effect analysis. Adv. Psychol. Sci. 25, 1780 (2017).
doi: 10.3724/SP.J.1042.2017.01780
Movalled, K., Sani, A., Nikniaz, L. & Ghojazadeh, M. The impact of sound stimulations during pregnancy on fetal learning: A systematic review. BMC Pediatr. 23, 183 (2023).
doi: 10.1186/s12887-023-03990-7
Leslie Cameron, E. Measures of human olfactory perception during pregnancy. Chem. Senses 32, 775–782 (2007).
doi: 10.1093/chemse/bjm045
Nordin, S., Broman, D. A., Olofsson, J. K. & Wulff, M. A longitudinal descriptive study of self-reported abnormal smell and taste perception in pregnant women. Chem. Senses 29, 391–402 (2004).
doi: 10.1093/chemse/bjh040
Hall, K. et al. Mothers’ accounts of the impact of being in nature on postnatal wellbeing: A focus group study. BMC Womens Health 23, 32 (2023).
doi: 10.1186/s12905-023-02165-x
Al-Mutawtah, M., Campbell, E., Kubis, H.-P. & Erjavec, M. Women’s experiences of social support during pregnancy: A qualitative systematic review. BMC Pregnancy Childbirth 23, 782 (2023).
doi: 10.1186/s12884-023-06089-0
Ballantyne, A. & Rogers, W. Pregnancy, vulnerability, and the risk of exploitation in clinical research. In Clinical Research Involving Pregnant Women 139–159 (2016).
Wang, D. et al. Dernet: Driver emotion recognition using onboard camera. IEEE Intell. Transp. Syst. Mag. 16, 117–132. https://doi.org/10.1109/MITS.2023.3333882 (2024).
doi: 10.1109/MITS.2023.3333882
McCarthy, C., Pradhan, N., Redpath, C. & Adler, A. Validation of the empatica e4 wristband. In 2016 IEEE EMBS International Student Conference (ISC), 1–4, https://doi.org/10.1109/EMBSISC.2016.7508621 (2016).
Prachyabrued, M., Wattanadhirach, D., Dudrow, R. B., Krairojananan, N. & Fuengfoo, P. Toward virtual stress inoculation training of prehospital healthcare personnel: A stress-inducing environment design and investigation of an emotional connection factor. In 2019 IEEE Conference on Virtual Reality and 3D User Interfaces (VR), 671–679. https://doi.org/10.1109/VR.2019.8797705 (2019).
Rezaei, B., Lowe, J., Yee, J. R., Porges, S. & Ostadabbas, S. Non-contact automatic respiration monitoring in restrained rodents. In 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 4946–4950. https://doi.org/10.1109/EMBC.2016.7591837 (2016).
Taelman, J. et al. Stress during pregnancy: Is the autonomic nervous system influenced by anxiety? In 2010 Computing in Cardiology, 725–728 (2010).
Onan, A. & Korukoğlu, S. A feature selection model based on genetic rank aggregation for text sentiment classification. J. Inf. Sci. 43, 25–38 (2017).
doi: 10.1177/0165551515613226
Rangkuti, F. R. S., Fauzi, M. A., Sari, Y. A. & Sari, E. D. L. Sentiment analysis on movie reviews using ensemble features and pearson correlation based feature selection. In 2018 International Conference on Sustainable Information Engineering and Technology (SIET), 88–91 (IEEE, 2018).
Yuanyuan, S., Yongming, W., Lili, G., Zhongsong, M. & Shan, J. The comparison of optimizing svm by ga and grid search. In 2017 13th IEEE International Conference on Electronic Measurement & Instruments (ICEMI), 354–360. https://doi.org/10.1109/ICEMI.2017.8265815 (2017).
Semeraro, A., Vilella, S. & Ruffo, G. Pyplutchik: Visualising and comparing emotion-annotated corpora. PLoS One 16, e0256503 (2021).
doi: 10.1371/journal.pone.0256503
Budaniya, M., Mishra, A. K., Rai, A. C. & Dasgupta, M. Effects of indoor plants on occupants’ emotional-state, performance, and perceived comfort in an open-plan seating space. In Performance, and Perceived Comfort in an Open-Plan Seating Space.
McLeish, J. & Redshaw, M. Mothers’ accounts of the impact on emotional wellbeing of organised peer support in pregnancy and early parenthood: A qualitative study. BMC Pregnancy Childbirth 17, 1–14 (2017).
doi: 10.1186/s12884-017-1220-0
Kazmierczak, M., Kielbratowska, B., Pastwa-Wojciechowska, B. & Preis, K. Couvade syndrome among polish expectant fathers. Med. Sci. Monit. Int. Med. J. Exp. Clin. Res. 19, 132 (2013).
Paulson, J. F. & Bazemore, S. D. Prenatal and postpartum depression in fathers and its association with maternal depression: A meta-analysis. JAMA 303, 1961–1969 (2010).
doi: 10.1001/jama.2010.605
Cobb, S. Presidential address-1976. Social support as a moderator of life stress. Psychosom. Med. 385, 300–14 (1976).
doi: 10.1097/00006842-197609000-00003
Corrigan, L., Moran, P., McGrath, N., Eustace-Cook, J. & Daly, D. The characteristics and effectiveness of pregnancy yoga interventions: A systematic review and meta-analysis. BMC Pregnancy Childbirth 22, 250 (2022).
doi: 10.1186/s12884-022-04474-9
Dilrukshi, I. & De Zoysa, K. Twitter news classification: Theoretical and practical comparison of svm against naive bayes algorithms. In 2013 International Conference on Advances in ICT for Emerging Regions (ICTer), 278–278. https://doi.org/10.1109/ICTer.2013.6761192 (2013).
Hanczar, B., Bourgeais, V. & Zehraoui, F. Assessment of deep learning and transfer learning for cancer prediction based on gene expression data. BMC Bioinform. 23, 262 (2022).
doi: 10.1186/s12859-022-04807-7
Lafraxo, S., Ansari, M. E. & Charfi, S. Melanet: An effective deep learning framework for melanoma detection using dermoscopic images. Multimed. Tools Appl. 81, 16021–16045 (2022).
doi: 10.1007/s11042-022-12521-y
Zhai, J. & Barreto, A. B. Stress detection in computer users through non-invasive monitoring of physiological signals. Biomed. Sci. Instrum. 42, 495–500 (2006).
Sung, M. & Pentland, A. P. Pokermetrics: Stress and lie detection through non-invasive physiological sensing (2005).
Katsis, C. D., Katertsidis, N., Ganiatsas, G. & Fotiadis, D. I. Toward emotion recognition in car-racing drivers: A biosignal processing approach. IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 38, 502–512 (2008).
doi: 10.1109/TSMCA.2008.918624
Chen, Y., Jia, Z., Hirota, K. & Dai, Y. A multimodal emotion perception model based on context-aware decision-level fusion. In 2022 41st Chinese Control Conference (CCC), 7332–7337. https://doi.org/10.23919/CCC55666.2022.9902799 (2022).
Fu, L., Wang, C. & Zhang, Y. Classifier fusion for speech emotion recognition. In 2010 IEEE International Conference on Intelligent Computing and Intelligent Systems, vol. 3, 407–410 (2010).