[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-85049-en":3,"doc-seo-85049-105":29,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},85049,1099514067415,"Rowan","https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502",8,"Research & Report","Cross-Modal Generative Framework for Signal Translation from Fetal–Maternal Electrocardiograms to Fetal Doppler Waveforms","Fetal electrocardiogram (fECG) and Doppler ultrasound offer complementary views of fetal cardiovascular function, with fECG reflecting electrical activity and Doppler capturing mechanical hemodynamics shaped by placental resistance and vascular compliance. The work models recoverable versus unrecoverable Doppler components reconstructed from fECG, clarifying the respective electrical and mechanical contributions to fetal circulation and informing clinical decisions. A crossmodal generative framework uses dilated convolutions and cross-modal attention to integrate maternal ECG. Trained on 885 synchronized fetal/maternal ECG and Doppler segments from 39 pregnancies, it synthesizes Doppler envelopes with PSD MSE and heart-rate error improvements over a dual-channel baseline, enabling quantification of purely mechanical Doppler components.","Cross-Modal Generative Framework for Signal Translation from Fetal–Maternal Electrocardiograms  \nto Fetal Doppler Waveforms  \nTongli Su 1 , Alireza Rafiei 1 , Marly van Assen 1 , Reza Sameni 1 ,2 ,  \nGari D. Clifford 1 ,2 , Faezeh Marzbanrad3 , Nasim Katebi 1  \n1Emory University, Atlanta, GA, USA, 2 Georgia Institute of Technology, Atlanta, GA, USA  \n3Monash University, Clayton, VIC, Australia  \narXiv :2607 .08073v 1 [ cs .LG] 9 Jul 2026  \nAbstract—Fetal electrocardiogram (fECG) and Doppler ultrasound provide complementary views of fetal cardiovascular function: fECG captures electrical activity while Doppler reflects mechanical hemodynamics shaped by factors such as placental resistance and vascular compliance. Understanding the recoverable and unrecoverable Doppler components through reconstruction from fECG offers insight into the relative contributions of electrical versus mechanical factors in fetal circulation, thereby informing clinical decisions. In addition, clinical evidence of maternal–fetal cardiac coupling suggests that maternal cardiovascular dynamics may also inform fetal hemodynamics. To computationally model these relationships, we propose a crossmodal generative framework combining dilated convolutions with cross-modal attention to selectively incorporate maternal ECG and self-attention to capture long-range temporal dependencies. Trained on 885 synchronized fetal/maternal ECG and Doppler envelope segments from 39 pregnancies, our model synthesizes Doppler envelopes with power spectral density mean squared error (PSD MSE) of 49 .9±15 .8dB2 (51% lower than two-channel baseline) and heart-rate error of 4.71 ± 0.77bpm (1.5% better than baseline; negligible relative to the 110–160 bpm physiological range). Cross-modal attention yields 39% PSD MSE reduction over na¨ıve dual-channel concatenation, quantifying the contribution of maternal–fetal coupling. Our proposed framework advances computational modeling of the maternal–fetal cardiovascular system by enabling the synthesis of Doppler envelopes from dual-lead ECG. By analysis of both recoverable and residual Doppler components, this approach enables quantification of the purely mechanical contributions to Doppler waveforms—those not recoverable from electrical recordings—ultimately facilitating a more comprehensive fetal assessment.  \nIndex Terms—Attention mechanism, cross-modal synthesis, deep generative learning, Doppler ultrasound, fetal health monitoring, maternal–fetal coupling  \nI. INTRODUCTION  \nFetal electrocardiogram (fECG) and Doppler ultrasound provide complementary views of fetal cardiovascular function. The fECG captures the electrical activity of the heart, primarily heart rate and rhythm, while Doppler reflects mechanical hemodynamics including blood flow velocity patterns [1] . Clinical indices derived from Doppler waveforms characterize diastolic function and overall cardiac performance [2], [3] .  \nThis work was supported in part by the NIH (R01HD110480) and Google.org AI for the Global Goals [Impact Challenge. Email:](Impact Challenge. Email: {ken.su}@emory.edu)[ {](Impact Challenge. Email: {ken.su}@emory.edu)[ken.su](Impact Challenge. Email: {ken.su}@emory.edu)[}](Impact Challenge. Email: {ken.su}@emory.edu)[@emory.edu](Impact Challenge. Email: {ken.su}@emory.edu)  \nHowever, Doppler acquisition requires expensive equipment and skilled sonographers, limiting accessibility in resourceconstrained settings [1] . In contrast, fECG can be collected widely and inexpensively but lacks direct measurements of mechanical function. Rather than treating these modalities as interchangeable, we recognize that fECG and Doppler capture fundamentally different aspects of fetal cardiac physiology. Excitation-contraction coupling links electrical depolarization to mechanical contraction: each R-peak marks systolic onset after an electromechanical delay of roughly 60–80 ms [4], [5] . Correlations between ECG features and contraction vigor suggest that fECG encodes","cbCaihTNVNBKdduz","https://ap.wps.com/l/cbCaihTNVNBKdduz","pdf",2335495,1,7,"English","en",105,"# Introduction\n## Computational Modeling of Maternal–Fetal Cardiac Relationships","[{\"question\":\"What problem does the framework address in fetal cardiovascular monitoring?\",\"answer\":\"It aims to reconstruct fetal Doppler waveforms from fetal electrocardiogram information, distinguishing Doppler components that are recoverable from electrical recordings versus those that remain residual and purely mechanical.\"},{\"question\":\"How does the proposed model incorporate maternal information?\",\"answer\":\"It combines dilated convolutions with cross-modal attention to selectively integrate maternal ECG, motivated by maternal–fetal cardiac coupling that can inform fetal hemodynamics.\"},{\"question\":\"What performance improvements are reported compared with a baseline?\",\"answer\":\"The approach reduces Doppler envelope synthesis error, reporting a 51% lower PSD MSE than a two-channel baseline, and improves heart-rate error by 1.5% while remaining within the physiological range.\"}]",1784200631,18,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":27},"cross-modal-generative-framework-for-signal-translation-from-fetalmaternal-electrocardiograms-to-fetal-doppler-waveforms","",{"@graph":35,"@context":85},[36,53,68],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/cross-modal-generative-framework-for-signal-translation-from-fetalmaternal-electrocardiograms-to-fetal-doppler-waveforms/85049/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-17","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the framework address in fetal cardiovascular monitoring?","Question",{"text":75,"@type":76},"It aims to reconstruct fetal Doppler waveforms from fetal electrocardiogram information, distinguishing Doppler components that are recoverable from electrical recordings versus those that remain residual and purely mechanical.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed model incorporate maternal information?",{"text":80,"@type":76},"It combines dilated convolutions with cross-modal attention to selectively integrate maternal ECG, motivated by maternal–fetal cardiac coupling that can inform fetal hemodynamics.",{"name":82,"@type":73,"acceptedAnswer":83},"What performance improvements are reported compared with a baseline?",{"text":84,"@type":76},"The approach reduces Doppler envelope synthesis error, reporting a 51% lower PSD MSE than a two-channel baseline, and improves heart-rate error by 1.5% while remaining within the physiological 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