SE-Diff: Simulator and Experience Enhanced Diffusion Model for Comprehensive ECG Generation
Authors: Xiaoda Wang
Kaiqiao Han
Yuhao Xu
Xiao Luo
Yizhou Sun
Wei Wang
Carl Yang
Venue: ICLR 2026 Poster (2026)
Keywords: Diffusion Model; ECG; Simulator; LLM
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)

Abstract: Cardiovascular disease (CVD) is a leading cause of mortality worldwide. Electrocardiograms (ECGs) are the most widely used non-invasive tool for cardiac assessment, yet large, well-annotated ECG corpora are scarce due to cost, privacy, and workflow constraints. Generating ECGs can aid mechanistic understanding of cardiac electrical activity, enable the construction of large, heterogeneous, and unbiased datasets, and facilitate privacy-preserving data sharing. Generating realistic ECG signals from clinical context is important yet underexplored. Recent work has leveraged diffusion models for text-to-ECG generation, but two challenges remain: (i) existing methods often overlook physiological simulator knowledge of cardiac activity; and (ii) they ignore broader, experience-based clinical knowledge grounded in real-world practice. To address these gaps, we propose **SE-Diff**, a physiological simulator- and experience-enhanced diffusion model for comprehensive ECG generation. SE-Diff integrates a lightweight ordinary differential equation (ODE)–based ECG simulator into the diffusion process via a beat decoder and simulator-consistent constraints, injecting mechanistic priors that promote physiologically plausible waveforms. In parallel, we design an LLM-powered, experience retrieval–augmented strategy to inject clinical knowledge, providing stronger guidance for ECG generation. Extensive experiments on real-world ECG datasets demonstrate that SE-Diff improves both signal fidelity and text–ECG semantic alignment over baselines. We further show that simulator-based and experience-based knowledge benefit downstream ECG classification.

Published as a conference paper at ICLR 2026
SE-DIFF: SIMULATOR AND EXPERIENCE ENHANCED DIFFUSION MODEL FOR COMPREHENSIVE ECG GEN- ERATION
Xiaoda Wang", Kaiqiao Han', Yuhao Xu", Xiao Luo', Yizhou Sun', Wei Wang', Carl Yang" "Emory University !University of California, Los Angeles (xiaoda.woa wang, y j. @ @emory edu (kahan, yzaun, weiwang)eee ecs ucla.
#University Wisconsin-Madison xiao. luo wi sc. edu
carlyang)
ABSTRACT
Cardiovascular disease (CVD) is a leading cause mortality worldwide. Elec- trocardiograms (ECGs) are the most widely used non-invasive tool for cardiac assessment, yet large, well-annotated ECG corpora are scarce due to cost, privacy, and workflow constraints. Generating ECGs can be beneficial for the mechanistic understanding of cardiac electrical activity, enable the construction of large, het- erogeneous, and unbiased datasets, and facilitate Generating realistic ECG signals from clinical context is important yet underex- plored. Recent work has leveraged diffuusion models for text-to-ECG generation, but two challenges remain: (i) existing methods often overlook the physiological simulator knowledge of cardiac activity; and (ii) they ignore broader, experience- based clinical knowledge grounded in real-world practice. To address these gaps, we propose SE--iff, a novel physiological simulator and experience enhanced dif- fusion model for comprehensive ECG generation. SE-Diff integrates a lightweight ordinary differential equation (ODE))based ECG simulator into the diffusion pro- via beat decoder and simulator-consiitent constraints, injecting mechanistic priors that promote physiologically plausible waveforms. In parallel, we design an LLM- L.M--powredd experience rerrieval-augmented strategy inject clinical knowl- edge, providing more guidance for ECG generation. Extensive experiments on real-world ECG datasets demonstrate that SE-Diff improves both signal fidelity and text-ECG semantic alignment over baselines, proving its superiority for text- to-ECG generation. We further show that the simulator- saaueator-bbsed and experience- based knowledge also benefit downstream ECG classification.
data sharing.
INTRODUCTION
Cardiovascular disease (CVD) remains leading cause of global mortality and morbidity (Roth al., 2020). In clinical workflows, the 12-lead electrocrdioogrmm (ECG), the standard setup using 10 electrodes derive 12 voltage traces, is ubiquitous, non-invasive, and low-cost for screening, triage, and longitudinal monitoring (Kligfield et al., 2007; Xu et al., 2025a:b). While machine learning (ML) has advanced ECG interpretation, progress is constrained by limited access to large, well- annotated corpora, stringent privacy considerations around sharing protected health information, and the expense of expert labeling (Johnson et al., 2023; Goldberger et al., 2000; Wang et al., 2026; 2025b). ECG generation provides principled way mitigate these barriers by expanding training data, enabling controlled curation, and decoupling model development from directly identifiable records (Zanchi et al., 2025). Concurrently, denoising diffusion probabilistic models (DDPMs) and score-based methods have established strong fidelity and coverage across modalities (Ho et al., 2020; Song et al., 2021), motivating their adaptation time series and, specifically, text-conditioned ECG generation (Lai et al., 2025a).
Corresponding author: Carl Yang (j cawlyyaggemooyy...o Code is available at https: github. tttuub.comiign aedittteebbddddidaaaaee

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Published as a conference paper at ICLR 2026
Despite these advances, there are still two gaps limit the practical adoption of text-to-ECG gener- ation. (i) Missing physiological simulator knowledge. Most diffusion models for ECG learn mor- phology and timing purely from data, with minimal incorporation of known cardiac physiological dynamics. Decades of physiological modeling have produced compact ordinary differential equation (ODE) simulators that yield realistic P. P-QRS-T morphologies and heart rate variability under trollable parameters (McSharry et al., 2003; Malik, 1996). Yet these simulators are rarely integrated as priors constraints during diffusion training, leaving disconnect between statistical generation and mechanistic plausibility. (ii) Under-use of experience-base
knowledge at scale. Prior text-
to-ECG works often condition on narrow patient metadata, but do not leverage broader experience knowledge--ase caatllgee--ae--bbbee case-based regularities distributed across large electronic health record (EHR) corpora. auugent generation (RAG) offers a principled means to inject such non-parametric knowledge to generators (Lewis et al., 2020), including via lexical retrieval schemes, yet its po- tential for conditioning medical time-series generation remains underexplored.
To address these challenges, we introduce SE-Diff, conditional latent-diffusion framework that synthesizes comprehensive ECG waveforms from natural-language clinical descriptions. SE-Diff couples lightweight ODE-based ECG simulator to the denoising dynamics through beat de- coder-ree reconstiicnn a -Ri-nlig single cycle from the latent spectral and rate constraints, thereby injecting mechanistic priors that steer generation toward phys- iologically plausible signals. In parallel, an L.M -----oeeeeed retrieval pipeline identifies clinically similar patients from EHRs, retrieves their ECG diagnoses and measurements, and distills them into a concise, physiologically grounded description that is fused with available metadata for condition- ing. In summary, our main contributions are as follows:
simulator-consistemm
Problem Identification. We identify the problem of generating realistic 10-second, 12-lead ECG waveforms directly from natural-language clinical descriptions. We propose SE-Diff, which can incorporate various patient metadata (age. sex, heart rate, thythm/conduuciion) as soft clinical constraints steer morphology toward clinically meaningful generation.
@ Sinulator-Inoormed Diffusion. SE-Diff is the first to integrate a lightweight ODE-based ECG simulator with latent diffusion. We introduce a beat decoder that reconstructs single-cycle beat from the latent representation, injecting simulator-consistent mechanistic priors that guide the de- noising process toward physiologically plausible waveforms.
@ Experience Retrieval-Augmenter Conditioning. We design an LLM-powered retrieval pipeline that identifies clinically similar patients based on EHR data and retrieves ECG diagnoses and measurements. The LLM generates concise, physiologically grounded description, which is fused with available metadata to form the conditioning context.
! Experimental Validation. Across real-world ECG datasets, SE-Diff surpasses baselines in both signal fidelity and text-ECG semantic alignment. Ablations quantify the contribution of simulator- based and experience-base knowledge conditioning. We further show that SE-Diff improves down- stream ECG classification when used for augmentation.
2
PRELIMINARIES
2.1 DENOISING DIFFUSION PROBABILISTIC MODELS
Denoising Diffusion Prohabilistic Models (DDPMs) (Sohl-Dickstein et al., 2015; Ho et al., 2020) define a fixed forward Markov noising process that maps clean sample zo ~ q(1r) to Gaussian noise over 7' steps, and a parametric reverse process that approximately inverts it. With variance schedule {8) C (0, 1), set a₂ = 1 - 33 and ā = -I' ag- The forward chain is
q( xv) II ((c x1-1),
where q( | [x-1) = N(Va, 2:-1, 3z I). This implies the closed form 0, = Vān To + Oe 6t with e ~ (O, I). The exact reverse-time posterior q( xx) is intractable, so DDPMs approx- imate it with a Gaussian transition Pa (x,-1 22)) := M/o (2,, t), Ex t)). where neural network predicts either the forward noise e, the clean signal va. the velocity v. Under the com-
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mon noise-prediction parameterization with c0(2t, t), the mean is
(z(,t)
Vas 2t v1 - co(xe:t))
1
(2)
and we set tz), E [3,I, I. Training maximizes a variational lower bound on log pe (To) "Sohl-Dicksterd et al., 2015), which in practice reduces to the simple loss (Ho et al., 2020) with optional step-dependent weights wy:
Ssimple (0) = Ee- "o. wong. ew We ||e -e---olaaaa + vI 91%
(3)
2.2 THE ECG PHYSIOLOGICAL SIMULATOR
In a resting heart, the ECG follows the P- P-QRS-T sequence. To reproduce this morphology, Mc- Sharry et al. (2003) proposed a three-ODE "ECG simulator" that generates realistic P-QRS-T waves while allowing control of heart-rate statistics and HRV spectrum (Malik & Camm, 1990).
The model evolves a 3D state (z=(t), vu(),, =()):: (x, y) traverse a unit-radius limit cycle whose angle encodes cardiac phase (one revolution per beat), and 2(t) is the ECG voltage given by excursions about this cycle. The governing ODEs are:
dx dt
dy dt
((x,,)) wy,
= (((x.)) + wx.
(4)
dz dt (E(.R.R.T.))
A0 (00 2)² - 20())(t)]
Σ
ag Af1 v) exp
(5)
2
where axx, y) = / y² drives (x, toward the unit limit cycle, 0xx y) = atan2 x) E [-x, x] is the phase, and A0)(r, = (0(x,)) -0g) mod 2nr is the phase offset to landmark 3 E B The parameter w controls angular velocity (thus average heart rate), and =o(t) is slow baseline (e.g., respiratory wander modeled =t) A A (TT frespt) with small amplitude (Sörnmo Laguna, 2005)). All morphology parameters are collected as
with B {P.Q, R. R. T}.
7 = (0p,00 0q RR, 0s, Or, ap, aQ, aR, as, aT, bp, bp, bR. bs, br}
(6)
where these parameters-phaat landmarks 0g. amplitude coefficients a,s and width coefficients by for each (P.Q, R, S,T)),oveeTrr the ECG morphology. When the (a, y) state passes the phase 0g. the Gaussian Gaussian-shaped term as Adg exp( ( (263)) in transiently perturbs away from baseline, producing the corresponding P/QRS/T deflection. The sign of ag sets polarity (upward for 02 > 0. downward for ag < 0); lasl controls peak amplitude; and bi sets the temporal spread (wave duration). The restoring term - zo))] then pulls the signal back toward baseline. Unless otherwise specified, we adopt the parameter values recommended by McSharry et al. (2003).
The Euler Method.
To simulate the synthetic ECG 2(t), we numerically solve the ODE system with a fixed-step explicit Euler method (the first-order Runge- Kutta scheme) (Butcher & Butcher, 1987; Süli & Mayers, 2003). We choose the step size At = 1/fs to match the desired sampling frequency (e.g. f. 500 Hz). Using the difiteedifeerenc approximation Minnehhhoon 2000): du
u(t At) - u(t)
di
Al.
which leads to the update rule u(t At) = u(t) + v() At, for an ODE of the form dufdt = v(t). Starting from initial conditions (Zo, yo, 20), we iterate this update for each time step. At the l-th step (time l At), let 14 (Jr we: fy ye; m), fz Ve, 21, (e))) denote the right-hand side of Equation and 5. The state is then advanced as:
re+1 = zt + fa we: n) At.
(8)
ye+1 ye + fy y8s 1) At.
(9)
26+1 22 + fz( Ve, 26, te: At,
(10)
and this process is repeated for = 0, up to the desired number of samples L. In other words, each iteration uses the derivatives fz, fy- f fz at the current state to step the solution forward by At. This simple explicit scheme is computationally efficient and sufficient for our purposes, though higher-order integration methods could be used for greater accuracy if needed.
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3 METHOD: SE-DIFF
We present SE-Diff, a conditional latent-diffusion framework that synthesizes 10s, 12-lead ECGs from clinical text. Diffusion operates in the VAE latent space (Sec. 2.1). To make physiology- aware supervision tractable, we attach a lightweight Beat Decoder that predicts a single QRS-ay aelignd cardiac cycle from the latent; its output drives simulator-informed regularizers derived from the ECG physiology model in Sec. 2.2. To strengthen conditioning, SE-Diff also incorporates experience knowledge retrieved based on EHRs (Sec. 3.4). In inference, we sample latent space and decode with the full VAE.
Problem Formulation. Each ECG record is multivariate sequence x 122xL representing s, 12-lead waveform sampled at fs- Our goal is to learn a conditional generator p(x c; Φ 0, 0) that uses throughout denoising to produce physiologically plausible ECGs. The conditioning is (t, r), comprising original diagnoses I, basic metadata (age, sex, optionally heart rate), and petnnderree-aaggeennni report r. Concretely, we first train a VAE (E,, D₂) together with a Beat Decoder Dheat, the encoder maps full recording to a latent sequence 2g = Fxx(x) E RdxT where T = L/S and S is the VAE temporal stride, and Dheat maps 2g to single-cycle prediction 112XLe We then freeze Es, De. and D and train DDPM latent space using U-Net During diffusion training, simulator- guided penalties (Sec. 3.3) are applied to the Beat Decoder output Dheat (zo), while experience -noweeedee features augment the text pathway (Sec. 3.4). At test time, we run the reverse process to obtain 2g and decode (2g) (Sec. 3.5).
denoiser c0(2,, tt c) with cross- aesssatttnttoon
(d)
x
Encoder
Zg
Forward Diffusion
Metedoto
Diognoses
EHR
ECG
(a]
(b)
Kandom
&
Decoder
20
Denoising U-Net
ZT
Report
Diagnosia
byas Lpui BRCkas
Beat Decoder
Simulator
LOoPM
Conditions
bboat capeec
(e)
(c)
Leuautater
Lexter
U-Net
Decoder
P.Q.R.
+ +
+ ECG Waveform
Random Noise
Nl parameters
Training
Inference
Figure I: Overview Framework SE-Diff. (a) Variational Autoencoder: encoder-decodes with lightweight beat decoder for a single QRS aligned cycle. (b) Conditional latent diffusion: U-Net de- noiser with cross-attention to text, metadata, and retrieved report. (c) comulator-informen diffiusion: Fuler and inter-lead constraints Conditioning: tri-view EHR similarity with L.M distillation into a concise report. (e) Inference: reverse diffusion and decoding to 10-second, 12-lead ECG.
the beat decoder output. (d) Experience retrieval-augmented
3.1 VARIATIONAL AUTOENCODER
We learn latent representation for 12-lead ECGs with variational autoencoder (VAE). Given full recording x E IR 122 the encoder Es parameterizes a diagonal Gaussian posterior
(=(= xxx= = -N{;; (x)x,, diag(o"(x))),
20 = (x) + (x) Ge, e~ ((0, (11)
where 20 E RdxT with T L/S and the VAE temporal stride. The decoder Dg reconstructs the signal x = (2(2 R 12x1 To expose morphology at the beat scale, attach lightweight Beat Decoder: hheat d + 12 te to get the single cycle signal: =D Dheat(20).
Dbeat
Training. Let ro denote the first R-peak index sampling rate fa (Golany et al., 2020b); define C(x) = x(: тo 0.2 f. то 0.4 fa] and L. 0.2 fa 0.4 fa. We train the encoder, decoder and Beat Decoder with length-normalizer mean-squared errors (MSE) and a single KL term:
1
Liml 12T.
||x (x)((
Lxt. KL(go(z x) (O, I)),
(12)
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Lheat
|[((x) Deear (Fg (F)(
(13)
12Lc
The Beat Decoder's single beat should also reflect the statistics of all beats in the 10s window. Rather than tiling h to length I, we detect all R-peaks within the window, )= (with J de- termined by the number of detected beats), and extract per-beat crops of identical length Le: (x(x) x|: rj 0.2 fz r + 0.4 fe] E IR 12, be h D (zu) E R 122x,, For each lead and each cycle j, we remove the mean and compute the one-sided log- magnitude spectrum (real FFT of length I..) up to fmax:
L.....J. Let the Beat Decoder output
/ [k] log( |=FF((C(()), -x (x))
[. = log(e |1F [B]], (14)
l with > 0 small and frequencies fx T.e f.. The spectral loss averages the (optionally band-weighted) squared discrepancy between the Beat Decoder's spectrum and the spectrum of each observed cycle:
Spee
1
ΣΣ E
w(fa) (S(k) 5 1k))
(15)
12JK
where K = [[k: fA < fmax H and (f)) can emphasize physiologically salient bands (e.g.. higher weights on 0.5-3 Hz for heart rate). We jointly optimize the encoder, the full decoder, and the beat decoder with:
fVVE Liull + 3KL LxL + Locat + O'spee Lapes where Ospec > O. Length normalization makes Liull and Leaat commensurate; Syeee incorporates full-wing full-window frequency statistics into the single-cycle prediction.
(16)
3.2 CONDITIONAL LATENT DIFFUSION
We train the diffusion in the VAE latent space. Given a latent sequence 20 = (x) E Rdx the forward process follows Sec. 2.1 with 24 + 2t: = vā 2g + vI āi e, and e ~ (), We
train conditional denoiser eo (2,,,, t, c) with the standard objective
E,, s0. [||-- - E0 f, c)
CUDPM
(17)
The denoiser is ID U-Net (Ronneberger et al., 2015) that treats the latent sequence 2, RdxT (channels d, length 7), Conditioning enters via cross-attention to a context representation C' built from (t, m. m, (Rombach al., 2022: Vaswani al., 2017); a final 1 1x1 convolution maps fea- tures to cg. Timestep and context embeddings modulate intermediate features through FiLM-style affine transformations (Perez al., 2018). sampling, we use standard DDPM transitions with classifier-free guidance (Ho & Salimans, 2022) and common improvements such cosine sched- ules and optional learned variances (Nichol & Dhariwal, 2021); further architectural and training details are provided in the appendix.
3.3 STMULLAOR-
DIFFUSION
We estimate class-specific parameters 7elass offline by fitting the simulator to representative real beats using explicit Euler integrator, together with lightweight stabilizers and morphology pri- ors that improve convergence and preserve physiological plausibility (details in Appendix B). For each training sample, we obtain single-cycle waveform i Dheat (zo) R 12 from the Beat Decoder (Sec. 3.1). This beat is used to enforce mechanistic plausibility via ODE-based ECG simulator (Sec. 2.2). The simulator's 's morphology parameters = {0g. ag. by (B.... R.S,T) enter the right-hand side fz(-: of Eq. 5, which defines fa. During diffusion training, the simulator provides two complementary regularizers:
Simulator-guided Euler Loss. Given the single-cycle waveform h 12x we integrate the simulator with parameters y and fixed initials (æo, Wo) to obtain reference trajectory (x,, Wc) at t PAi. We penalize per-lead deviations from the ODE and the simulator-guidest Euler loss is:
L--1
het1 he
LEuler 12(L. - 1) lead E=
ff(xx. ye, be, te:
(18)
At
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Inter-lead dependency constraint. Realistic 12-lead synthesis requires not only accurate per-lead morphology but also correct physiological interdependencies among leads. We therefore enforce the classical frontal-plane identities implied by the standard ECG configuration (Einthoven's 's triangle and Goldherger's central terminal), constraining the generated limb and augmented leads to remain mutually consistent:
I 1 1I III, -
(1-
(19)
II I++++III, aVF= ( III,
Let Ltrontal 11,, R, aV L, FVF be the frontal-plane leads (a subset of the 12 leads). For any identity the form = Bp + " with y: P.q E Lirontals we treat y as the child lead and p,q its parent leads. Denote by hy the sample at index l of lead L from the predicted 12-lead beat h E R 12x Frc We obtain parent simulator states (xZ, ve) and (2.. vi) ) by integrating the sim- ulator with class-specific parameters 7pp Tg. Defining C (1, III, 11), (1I, I, III, 1, 1), (111, I1,1, 1, -)), (aVR, I,I,, - -²), I, 2 22) (aV F, 11, 111, 1 2 })3 and the loss aggregates the six constraints time:
Le-1
F+1 h2
2
Linter-led
Σ
At B B pe: (2.661)) - (2 ve 12. Le mn)
(v.P.,,8,))CC
(20)
Here P.P.q are specific leads (elements of the 12-lead set), and C enumerates each frontal-plane identity as a tuple (y. P: 9: 3, This construction directly matches the child's discrete derivative to the corresponding linear combination of the parents' simulator derivatives, enforcing physiologically grounded inter-lead consistency.
3.4 EXPERIENCE RETRIEVAL RETRIEVAL-AUUMENTED CONDITIONING
We augment text conditioning with clinical experience retrieved from electronic health records (EHR). Specifically, we link MIMIC-IV-ECC (Gow et al., 2023) to et al., 2023), build compact tri-view profile (diagnoses, medications, procedures) (Ou et al., 2025), and retrieve the top-k clinically similar admissions. For an index admission u, let EDias, EMed and EProe denote the of diagnosis, medication, and procedure codes, respectively. Given another admission u', we compute set similarities using the Jaccard index J(A, B) for X E (Diag, Med, Proc}:
(John-
Tx(u, u') = [EE F. These similarities are aggregated with nonnegative weights Å1 A2, As to yield single similarity:
(21)
r(u, u²) = x TDiagg (u, u²) + A2TMea u²) + >g u').
(22)
We retrieve the top-k most similar admissions according the tri-view similarity over diagnoses, medications, and procedures, and then pass their diagnostic profiles together with (t, m) to the LLM using the prompt shown in Fig. 5 in the Appendix obtain a concise, physiologically grounded report r, where t denotes the original diagnoses and encodes basic metadata (age, sex, option- ally heart rate). Finally, the conditioning input (t., m. r), which conditions the denoiser via cross-attention.
3.5 TRAINING AND INFERENCE
Training objective. We combine the latent-space diffusion loss with simulator- regularizers:
Liotal LDDPM C + 7 Linter-load;
A >>>>0 0.
(23)
We first train the VAE using Eq. 16 and then freeze Ez, Do. and Dhoat, During diffusion training, we optimize only the denoiser eØ; the Beat Decoder appears only through these regularizers-wve we use Dbeat to produce h D)beat (zo) for LEuler and Linter-lead. All simulator-driven terms are training- and do not modify the reverse process.
Inference. Given conditioning c, we draw 27 ~ NN(0, and apply the learned reverse diffusion from / = 7 to with the standard DDPM parameterization (variance schedule {8.) 0t 1 Br,

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0 IT. ag):
24 vI
ai t
80(2t,t,c)
(24)
Vār
po(2,t,c) c) vas
1
2t
Be
so(zattc)).
(25)
v1
3, and sample 2t-1 - ne (z i c) + V&& with (V I). After the final step, decode to the signal domain, x Do(2o), optionally using classifier-free guidance during sampling.
We set 3t = -
4 EXPERIMENTS
Dataset and Preprocessing. We train on MIMIC-IV-ECG (Gow et al., 2023; Johnson et al., 2023), which contains 800,035 de-identified 12-lead, 10 ECGs sampled 500 Hz. Heart rate (HR) is taken from metadata when available; otherwise is re-estimated via QRS detection (WFDB XQRS). Waveforms
encoded by VAE into 128 latents that serve inputs to the diffusion model. We use the MIMIC-IV-Clinical (Johnson et al., 2023) to obtain each patient's EHR for experience knowledge conditioning. For external validation, we additionally experiment on the PTB-XL dataset (Wagner et al., 2020), which consists of 12-lead clinical ECGs with standardized diagnostic labels.
Baselines. We compare against four strong ECG generation baselines: (i) DiffuSETS (Lai et al., 2025a), to our knowledge the only prior method that generates 12-lead, 10s ECGs from clinical text; (ii) a BANN-ased model (WGAN) originally proposed for arrhythmia classification using cGAN- augmented ECG signals (Adib al., 2022), which we adapt to synthesize 10 12-lead BCGs under our setting: (iii) SSSD, diffusion-
conditional ECG generator built on structured state space models (López Alcaraz & Strodthoff, 2023); and (iv) BeatDiff, an ECG beat diffusion model de- signed for morphology-awee aware reconstruction from indirect signals (Bedin & Coauthors, 2024). To quantify the contribution of each component of SE--ifff we report ablations trained under identical schedules and seeds: (i) SE-Diff w/o Sim (removing the Euler consistency term LEuter); (ii) SE- Diff w/o InterLead (dropping Linter-lead); (iii) SE-Diff w/o Exp (disabling EHR retrieval and LLM distillation conditioning uses only text+metadata).
4.1 ECG GENERATION RESULTS
We evaluate SE-Diff along four clinically aligned levels: signal-level stability. feature-level physiol- ogy, diagnostic/semanic alignment, and beat-level morphology and interval fidelity. At each level, define the metrics and report aggregate results both MIMIC-IV-ECG and PTB-XL.
Signal-level Stability. Given matched real and generated ECGs (x, x) under the same condition c, compute per-lead mean absolute error (MAE), normalized root mean squared error (NRMSE), and Pearson correlation r to assess waveform fidelity and temporal consistency.
Feature-level Physiology. To evaluate preservation of basic physiology, we compare heart rate (HR) estimated from X and x via the absolute error MAEHR-
Diagnostic Alignment. We adopt CLIP-style evaluation for ECG-tex: pairs: ECG encoder (.) and a text encoder flea(-) produce fn-orrmllized embeddings; cosine similarity quantifies alignment, s(x, text)= (Seay (x), Jieu(text), To control encoder bias, report the relative CLIP
score and the relative FID score:
rLLIP 5(8, text)
FID(2. ((2))
FFID
(26)
s(x, text)
FID(E,X,) FID(X, x(22)
Distributional coverage/quality is measured with the Fréchet distance (FID) in the ECG embedding space, FID ||,,- My 122 TYE, + Eg Ex,, where (H4, E,) and (Pg. Eg) denote the mean and covariance of real and generated ECG embeddings, X denotes generated samples, and (11) x(22) are disjoint splits of the real set. And report its normalized relative score rFID.
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Table ECG generation performance on MIMIC-IV-ECG and PTB-XL datasets
Model
MAE
NRMSE
MAERR 1
rCLIP Score 1
rFID Score f
MIMIC-IV-ECG (internal)
SSSD
0,4337
0,0300
0.2027 = 0.0441
27.37 = 14.84 14.84
0.7213 + 0.0402
0.9096 + 0.0398
WGAN
0.1896
0.0605
0.1301 = 0.0316
31.54 2.15
0.5688 + 0.0347
0.5497 = 0.0192
BeatDiff'
0.7464
0,0070
0.4756 1 0.0117
27.74 1.49
0.5167 + 0.0180
0.8612 1 0.0039
DiffuSETS
0.1092
0.0022
0.0851 = 0.0012
13.29 + 1.13
0.9309 0.0036
0.9209 + 0.0038
SE-DiIT
0.0923 O. 0.0021
0.0714 1 0.0010
8.43 1 0.42
0.9470 1 0 0.0029
0 0.9509 1 0.0035
w/o Exp
0.0926 = 0.0022
0,0730 = 0.0008
15.06 + 0.34
0.9099 + 0.0026
0,9032 + 0.0062
w/o InterLeaad
0.0934 0.0023
0.0733 = 0.0006
19.21 + 1.27
0.9216 0.0029
0.9128 + 0.0052
w/o Sim
0.0965 0.0024
0.0768 1 0.0014
14.28 1 1.35
0.9303 0.0041
0.9138 1 0.0056
PTB-XL (external)
SSSD
0.6103 0.0204
0.3818 = 0.0670
15.22 11.51
0.8618 0.0599
0.7168 + 0.0355
WGAN
0,2458 0.0653
0.1197 + 0.0313
13.82 + 18.69
0.5880 0.0000
0.3377 1 0.0232
BeatDiff
0,9888 0,0059
0.4731 = 0.0104
13.86 +: 0.78
0.8799 + 0.0022
0.8503 + 0.0035
DiffuSETS
0.1281 0.0030
0.0797 = 0.0011
17.88 # 0.72
0.8690 + 0.0011
0.8456 + 0.0035
SE-Diff
0.1076
0.0033
O. 0.0630 - 0.0006
8.24 1 0.43
0.8901 + 0.0060
0 0.8583 1 0.0056
w/o Sim
0.1138
0.0032
0.0680 = 0.0007
14.72 + 0.90
0.8896 0.0010
0,8001 + 0.0061
w/o InterLead
0,1084 0,0034
0.0640 - 0.0007
12.02 1 0.78
0.7484 1 0.0076
0.8568 1 0.0051
Tables report MAE, NRMSE, MAEHR. ICLIP, rFID on MIMIC-IVEECG and PTB-XL dataset. Across both datasets, SE-Diff consistently outperforms SSSD,, WGAN, BeatDiff, and DiffuSETS on all metrics, indicating improved waveform reconstruction, more accurate heart rate estimation, and stronger ECG-text alignment. The ablations (SE-Diff w/o Sim, w/o InterLead, w/o Exp) each degrade one or more metrics, highlighting the importance of simulator guidance, inter-lead con- straints, and experience-based conditioning.
Beat-level Morphology and Interval Fidelity. To assess beat-level fidelity beyond global signal errors and heart rate, we introduce set of quantitative morphology and interval features. For each ECG, we automatically extract PR interval, QRS duration (QRSd), QT interval, seeatt-aae-coorreett QT (QTeF), ST-segment deviation J+60 ms (ST@J+60), and P- and T-wave durations (P dur, dur), and compute record-level median absolute errors between generated and real ECGs.
Beat-level morphology & interval fidelity (MAB) MIMIC-IV-ECG and PTB-XL datasets. All values are record-level medians; lower is better (L).
Table 2:
Model
PR
QRS&
QT 1
QTeF
ST&J+60
P duu
T dur
MIMIC-IV-ECG (internal)
SSSD
7.70 1
32.00
25.11
23.25
0.16 1:
5.30 1:
23.71
WGAN
18.01 1
29.64 9
21.02 1
23.83 12
0.13 1
6.01 1 0
24.01 1
BeatDiff
10.34+5
18.23
14.23 6
9.43 +
0.17 +
7.00 +
30.02 +
DiffuSETS
14.81 + 6
11.71
8.20 3:3
9.71 +
0.04 +
5.60 +
8.71 +:
SE-Diff
7.30 3
10.71 +
4.50
7.88 2
0.03 0
2.50 + 0
6.80 +
w/o Lete
11.11 3:3
15.01 5
11.91 1:5
13.84 7
0.03 :
4.00 +
8.10 d:
w/o Exp
9.81 19
14.11 1 13
8,20 1
12.61 13
0.04 10
4.00 1
6,90 1
w/o Sim
12.11
13.21
8.40 +
8.89 +
0.04
5.10 +
13.11 +
PTB-XL (external)
SSSD
13.71 +
13.61 -
30.22
27.69 14
0.33 +
12.71 +
19.11 + 10
WGAN
14.01 +
17.45 +: 13
12.01 + 10
12.97 10
0.18 1:
7.33 1:
25.68 :: 13
BeatDiff'
13.41 14
17.40
9.81 1
8.94 1
0.73 11
4.10
18.81
1
DiffuSETS
7.70 2
12.61 7
11.51 +
13.75
0.10
0.11+6
13.20 +
SE-Diff
3.90 1
10.01 1 2
5.20 1 1
8.68 2
0.07 0
3.00 11
9.31 1
w/o Interl.ead
7.00 1
10.31
- 5
8.40 +
10.91 +
0.07 + 0
4.40
13.61 +
w/o Sim
9.21 4
12.91
12.51
1:4
14.20 +
0.07 :
4.50 +
12.31 1:3
Tables summarize the beat-level morphology and interval metrics on MIMIC-IV-ECG and PTB- XL. SE-Diff achieves the lowest median errors across all intervals (PR, QRSd, QT/QTeF, ST@ TTJ+600 dur. T dur). demonstrating that not only matches global signal statistics but also preserves beat- level timing and morphology more faithfully than competing models.
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4.2 DOWNSTREAM ECG CLASSIFICATION
We evaluate whether SE-Diff mitigates severe class imbalance in downstream ECG classification by augmenting minority classes with model-generated ECGs. Training distributions are intentionally skewed; evaluation uses a fixed, class-balanced test set. We compare four regimes: (i) Unbalanced (real-only, skewed). (ii) Balanced (real-only, fully balanced; reference upper bound), (ii) several synthetic augmentation strategies that oversample the minority class using SSSD, WGAN, BeatDiff, DiffuSETS, and (iv) SE-Difff We report F1, accuracy (Acc.), and AUROC (AUC).
Imbalanced Gender Classification. We train a binary classifier to predict sex (Female vs. Male). As shown in Table 3, all generative augmentation methods improve over the Unbalanced baseline, but SE-Diff yields the largest gains, raising Fl from 42% to 58% and AUC from 46% to 58%. This substantially narrows the gap to Bulanced upper bound (FI/AUC 62%), and SE-Diff consistently outperforms SSSD, WGAN, BeatDiff, and DiffuSETS under the same skew.
Rare-disease Classification. We train a classifier to distinguish Sinus rhythm from supraventricular tachycardia (SVT), treating SVT as the minority class. Table 3 shows larger relative gains on SVT, indicating that synthetic augmentation is particularly effective when physiological heterogeneity is high and labeled minority examples are scarce. SE-Diff recovers substantial fraction of this gap, especially on the minority SVT class.
Table 3: Downstream ECG classification under severe class imbalance.
Model
Male = 10% Female
SVT = 10% Sinus
F1 (%,
Acc. (%, f)
AUC (%, f)
F1 (%,
Acc. (%, f)
AUC (%, 1)
SSSD
42 0
54 +
49 1 2
56 +
63 1 2
81 +
WGAN
42 10
54 1
49 1
57 1
63 1 2
82 1
BeatDiff
4
55 +
51 +
60 00
84 +
DiffuSETS
4443
54
5441
70
84 +
SE-Diff
58 =1
58 1
58 4
72 1
71 0
85 + 2
Unbalanced
40
54
46 +
56 +
62
80 +
Balanced
6
62 + 0
62 +
79 +
80+-2
93 +
4.3 MECHANISTIC ANALYSIS OF SE-DIFF
Noise Scheduling Analysis. Figure 2 summarizes the forward process under our linear noise sched- ule. Panel (a) shows the per-step noise increment increasing steadily, while (b) displays the corre- sponding signal retention factor decreasing slightly each step. The cumulative signal fraction (c) drops smoothly from and saturates in late timesteps. This profile yields a gradual, well-conditioned reverse trajectory: global rhythm and cross-lead coherence, and later steps refine P/QRS/T mor- phology and suppress residual artifacts. We therefore adopt this schedule for SE-Diff as it offers an interpretable progression and stable behavior across sampling budgets.
zero, and the accumulated noise in (d) rises monotonically
early steps
2012
5310
1.90
0.00
400
300
222
-20
650
1300
200
430
3000
856
300
(a) Beta Schedule 3.
(b) Alpha Schedule as
(c) Alpha Cumprod ā
(d) Noise Level 1 5
Figure 2: Noise scheduling analysis showing the progression of noise and signal factors throughout the diffusion process.
Case Study for ECG Simulator. To visualize the simulator's morphology, Fig. 3 presents four random single-cycle templates (one lead per label): (A) Simus rhythm, Lead Upright wave, nar- QRS complex, and concordant wave provide clean normal reference for comparison. (B) Ventricular pacing, Lead VL. A wide, predominantly negative QRS complex (QS/deep S), reflect- ing Bacing/LBBB-likke activation, clearly departs from normal conduction. (C) Sinus rhythm with

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Published as a conference paper at ICLR 2026
first-degree AV block, Lead II. A P wave followed by elongated isoelectric segment before the QRS complex qualitatively indicates PR-interval prolongation. (D) Consider acute ST-elevation MI, Lead V3. Convex ST-segment elevation after the J point is characteristic of anteroseptal involve- ment. The simulator serves as a morphology prior and qualitative oracle within SE-Diff, enabling clear visual audits and morphology-awan ablations without confounding rhythm variability, and providing guidance to the diffusion model.
0.10
0.055
0.005
0.055
00.05
02500
-0.003
0215
0.010
Time tmrl
Time (ms)
Time imm)
Time Jmsi
(a) Lead
(b) Lead
(c) Lead II
(d) Lead
Figure 3: Representative single-cycle ECG waveforms generated from our simulator, Panel A: sinus rhythm (Lead I). Panel B: ventricular pacing (Lead V1). Panel C: sinus rhythm with first-degree AV
block (Lead II). Panel D: consider acute ST-elevation MI (Lead V3).
Case Study for ECG Generation. Figure 4 compares a 10s, 12-lead ECG generated by SE-Diff with its paired reference for a case conditioned on "Sinus rkythm" (male, 65 y, HR 94 bpm). The generation preserves canonical sinus morphology-ett plex with an appropriate PR interval-and shows coherent R-wave progression across the precordial leads, with R-R intervals consistent with the target rate. Clinically, the SE-Diff tracing appears cleaner than the reference: baseline wander and high-frequency artifacts are attenuated, yielding crisper ST segments and T-wave contours without distorting morphology. This qualitative finding aligns with the model's design: sinulator-informed constraints and aeggreetee condi- tioning steer the diffusion process toward physiologically plausible, low-noise signals.
P wave preceding narrow QRS com-
(a) SE-Diff generated ECG.
(b) Real ECG.
Figure 4: Case Study for ECG Generation.
CONCLUSION
We introduced SE-Diff, a conditional latent-diffusion framework for 10-second, 12-lead ECG syn- thesis that couples VAE latent space with Beat Decoder and simulator-inforet regularizers, and strengthens conditioning via experience retrieval from EHRs. Across benchmarks, SE-Diff improves signal fidelity, preserves inter-lead physiology, and achieves tighter diagnostic/semantic alignment, while also enhancing downstream classification when used for data augmentation. Ablations con- firm that both the ODE-based guidance (Buler and inter-lead constraints) and retrieval-augmenner conditioning contribute materially to performance. Future work will extend SE-Diff to more clin- ically meaningful applications (e.g., arrhythmia risk stratification, therapy response modeling, and long-term ambulatory ECG) and evaluate robustness across institutions and rare presentations. We believe SE-Diff offers a principled step toward physiologically grounded, clinically aligned genera- tive modeling of ECGs.
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6 ACKNOWLEDGEMENT
This research was partially supported by the U.S. National Science Foundation under Award Num- bers 2442172, 2312502, 2319449, 2211557, 2303037, 2312501, 2531008, and 2106859, and by the U.S. National Institutes of Health under Award Numbers K25DK135913, RFINSI 139325, R01DK143456, U18DP006922, OT20D038003, RO1HL175135, U540D036472, U54HG012517, and U24DKO This research was also partially supported by internal funds and GPU servers provided by the Computer Science Department of Emory University, the SRC JUMP 2.0 Center, Amazon Research Awards, Snapchat Gifts, Optum AI, NEC, and the Easton Center.
7 ETHICS STATEMENT
We adhere to the ICLR Code of Ethics. This study uses only de-identified data from MIMIC-IV- ECG (Gow et al., 2023; Johnson et al., 2023) and associated de-identified records from MIMIC- IV-CLINICAL (Johnson al., 2023) under the applicable Data Use Agreements and credentialed- access requirements. No direct interaction with human subjects occurred; no personally identifiable information (PII/HHI) was accessed released, and we made no attempt at re-identification. Heart- rate estimation (WFDB XQRS), resampling, and VAE-based encoding were performed on the de- identified waveforms; EHR linkage relied only the dataset's de-identified subject and admission keys within documented admission windows. This work is for research purposes only and is not a medical device. No conflicts of interest or sensitive sponsorships are present.
REPRODUCIBILITY STATEMENT
All information necessary to reproduce our results is documented in Appendix C, including dataset curation and preprocessing, model architectures, training and inference hyperparameters and sched- ules, and so on.
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A USE OF LARGE LANGUAGE MODELS
We used a large language model (LLM) solely for writing celistanne--sccffiiie to correct gram- mar, improve wording, and enhance clarity. The LLM did not contribute to research design, data analysis, modeling, experiments, or interpretation of results. All technical content and conclusions were authored and verified by the authors, who take full responsibility for the manuscript.
A.1 RELATED WORK
A.1.1 GENERATIVE MODELS FOR ECG
Simulation has repeatedly improved data efficiency sequential decision making- both imita- tion learning and in reinforcement learning-bi by narrowing the gap between training and deploy- ment (Zadok al., 2019; Mnih al., 2013; Ni al., 2025: Zeng et al., 2025). parallel, gen- erative models have been used to expand training corpora: in vision, SimGAN refines synthetic images with unlabeled real data (Shrivastava et al., 2017); in cardiology, augmenting classifiers with GAN-generated heartbeats improves performance (Golany et al., 2020a). Beyond beat-level augmentation, adversarial models have produced realistic multi-lead "DeepFake" ECGs for privacy and data scarcity mitigation (Thambawita et al., 2021). and mechanism- aware variants embed ordi- nary differential equations to better capture depolarization appolarrzationrrroollaaazioooooi dynamics (Golany et al., 2021). However, GANs be unstable and prone mode collapse in multi-lead, lalti-label
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regimes. Denoising diffusion and coore-baeed models offer likeiihoodggroueee alternative with strong mode coverage and stable training (Ho et al., 2020; Song et al., 2021). Recent ECG adapta- tions include conditional diffusion with structured state-space backbones (SSSD-ECG) (Lopez Al- caraz & Strodthoff, 2023), generalized diffusion for gemerraioonmmpuaiioooooeeecattte 2023), tatteesspaccrrrasfooeeerr hybrids (Zama & Schwenker, 2023), and text/metadattacconiiooon synthesis (DiffuSETS) (Lai et al., 2025a). The field is also trending toward personalization and physiological consistency: conditional models incorporate patient metadata anatomy to produce more plausible 12-lead signals (Sang et al., 2025), and diffusion frameworks create patient "digital twins" (Lai et al., 2025b). Hybrid uses couple generative modeling with signal-quality assessment and anomaly detection (Han et al., 2025), while semi-supervised GANs aim to better capture tem- poral dynamies (Li et al., 2025). Very recent work explores flow-matching as a faster alternative to iterative diffusion for ECG synthesis, reducing sampling cost while targeting comparable fidelity (Bondar et al., 2025).
(Neifar et al.,
A.1.2 PHYSIOLOGICAL ECG SIMULATORS
Compact physiological simulators capture stereotyped P- P-QRS-TT morphology with low-dimensional differential equations. The canonical ECGSYN model uses three-dimensional limit-cycle oscil- lator whose phase-locked Gaussian components generate P. QRS, and T deflections, while stochas- tic control of instantaneous heart rate reproduces realistic RR patterns and HRV statistics (e.g.. mean/SD of RR, low- and high-frequency spectral peaks) (McSharry et al., 2003; Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology, 1996). Open implementations (c.g.. PhysioNet ECGSYN) enable reproducible waveform synthesis and stress-testing (Goldberger et al., 2000). However, globally fixed morphology templates and lin- lead projections limit expressivity under rhythm changes, conduction abnormalities, and nonsta- tionary repolarization. Hybrid approaches mitigate these issues by coupling mechanistic priors with learnable components-vit neural ODEs or universal differential equations-10 preserve physical structure while fitting data (Han et al., 2024); conditioning on anatomy further improves inter-lead realism (Chen al., 2018; Rackauckas et al., 2021: Sang et al., 2025).
A.1.3 ECG CLASSIFICATION
Classical ECG pipelines segment signals into beats with robust QRS detectors (e.g.. Pan-Tompkins; Afonso et al.) and derive interval/moorhoolgg descriptors before applying shallow classifiers such as linear discriminants or SVMs (Afonso et al., 1999; De Chazal et al., 2004; Nasrabadi, 2007). With deep learning, end-to-end models on raw waveforms supplanted hand-crafted features and reached cardiologist-level performance in single- and ambulatory arrhythmia detection (Rajpurkar et al., 2017: Hannun et al., 2019); at the beat level, residual CNNs are particularly effective, and large multi-lead corpora such as PTB-XL have enabled high-capacity models and rigorous multi-label benchmarking (Kachuee et al., 2018; Wagner et al., 2020). Recent work refines architectures and training regimes size region-of oo-oon-f--iterestt cues (Wang et al., 2025a), ECG- Specifcciicc scaling laws suggest shallower but wider networks outperform rision-oriented designs (Lee al., 2023), and transfer learning trans- formed signals improves performance under class imbalance (Mavaddati, 2025). Synthetic data from GANs and diffusion models is now routinely used for augmentation, with semi-supervised variants further boosting diagnostic accuracy (Li et al., 2025). In our experiments, we adopt strong ResNet heartbeat classifier and evaluate whether SE-Diff improves generalization under class imbalance and limited labels by augmenting training with physiologically plausible, label-consistent synthetic beats.
regimes-duullchanee dual-channel networks that fuse ResNet-ICBAM with 2D-CNN features empha-
B ECG SIMULATOR CALIBRATION WITH STABILIZERS AND MORPHOLOGY PRIORS
Motivation. Naive least-squares calibration of the ODE-based simulator (Sec. 2.2) often fits the sharp QRS complex yet drifts later in the window and may flip polarity. The main causes small baseline trends and scale mismatches between simulated and observed signals, and an under- constrained morphology (especially T-wave width). We introduce lightweight, differentiable sta-
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bilizers that improve convergence and yield physiologically plausible parameters without altering simulator dynamics.
Trend-aware alignment and fidelity. Let (t) denote the simulated voltage with parameters π)- Rather than compare 2n directly to the observation y(t), align via three- saree-parameter affine-trend model with scale 8, offset e, and linear trend b:
i(t) = c s (2)(t +b( 1),
i=
t
(27)
At each iteration (c, 8,,88, b, are obtained by least squares and are differentiable in Zny The fidelity term
mms (1) - + 1000 -
(28)
augmented by a small scale regularizer prevent rare runaway gains:
(,(m) = X s², A E 610- [10-).10-1)
(29)
Morphology priors (widths and amplitudes). Using the McSharry parameterization, each deflec- tion / E Q, R, S, } has amplitude as, width by > 0. and phase Ag. Enforce positivity with by softplus(b3) + E (E 10 1) and shrink widths toward physiological targets by:
Cwidtth (1) A Σ wa (bs bz)², (bp, bb DR. bs. by) (0.20, 0.08, 0.10, 0.08, 0.32). (30)
8
with wy and Wg otherwise to prevent absorbing baseline drift via an overly broad T wave. A mild amplitude penalty
Camp (n) Aa Σ a
(31)
discourages attributing variability solely to the global scale s in Eq. 27.
Phase ordering. To preserve the physiological ordering of (P.Q S,T) on the unit circle, in- troduce global phase shift AO and wrap phases 0g wrap(0g + A0). A hinge penalty with margin m enforces monotonicity:
(nd = Aved Σ max 0, 0; - 01+1 + m),
m Rs 0.05 rad.
(32)
This term typically decays after few epochs and can be disabled once ordering stabilizes. Objective and optimization. The calibration loss is
L(n) Lise + L. Lwidth + Lamp + Lond-
(33)
Optimize with A (cosine decay with warmup), followed by brief L-BFGS refinement. The same Euler sub-stepping and burn-in used at inference are applied during training to maintain inte- grator consistency.
Polarity canonicalization. Because lead inversions are common, canonicalize polarity after fitting: re-simulate Zn, re-estimate (t) = + (t) (no slope), and, if < O, flip all amplitudes {a.} once. This step is outside the loss and standardizes reported parameters.
C IMPLEMENTATION DETAILS.
All models are trained in PyTorch with AMP on single NVIDIA H200 using AdamW (Ir x 10-4 with cosine decay to 1 10-5), gradient clipping/accumulation (global batch 4096). for 200 epochs with early stopping. Diffusion uses T=1000 steps, linear 3r E [8.5 x 10-4 1.2 x 10 (DDPMScheduleri) and classifier-free free guidance. The VAE has 4 latent channels; the coder/decoder are multi-resolution with residual blocks, attention, and skip connections; training uses LMSE LKL. 11..=1). A lightweight beat decoder predicts the first beat (I,=3000 at 500 Hz;
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R- R-peaks via NeuroRit2). The denoiser is 7-stage ID U-Net (kernel 7) with self/cross-attention (8 heads, width 16 16-64) consuming text embeddings (1536-d) plus metadata (age, sex, heart rate). Physiology-awarce training adds the Euler simulator loss (A=3xx x 1003 3) and the inter-lead constraint (0=5x10-3) 9); class-wise simulator parameters are prefit from 200 beats/label.
attention
We use MIMIC-IV-ECG with simplified rhythm labels. Free-text diagnostic reports cleaned and normalized, then mapped to a compact multi-label taxonomy (e.g.. sinus thythm/bradyttachy, atrial fibrillation/ffutter, PAC/PVC, bundle-branch block, LVH/RVH, prolonged QT, ST/T abnormalities, ischemia/infarct), Texts are embedded with pretrained text encoder For simulator-informer diffusion, we pre-compute class-wise simulator parameters for the top-20 ECG categories and use them during training. For ECG gen- eration, we sample 100 waveforms per setting and compute metries. For downstream ECG clas- sification, we form balanced subsets with 200 samples per class and train lightweight MIP that flattens latents (512-d) and applies two fully connected layers (128 +64) with BatchNorm, ReLU,, and Dropout (0.5), followed by a linear output; optimization uses cross-entropy with AdamW and early stopping. In all experiments, we set A = X2 = X = and use k = 3 retrieved admissions for experience- based conditioning. And employ the "gpt-4.1 1-mini' model for generating the experi- ence report r. The Beat Decoder is trained jointly with the VAE, and when R-peak detection fails or class- wiie simulator parameters are available for given sample, simply skip the beat/spectral or corresponding simulator losses for that sample to avoid instability while keeping the rest of the training unchanged.
seett-embbdddngg-ddd----
From an ethics and privacy perspective, all experiments are conducted on de-identified public datasets MMIMIC-IV-ECGEEHR and PTB-XL), and we enforce striet patient-level splits: records used at validation/test time are never included in the retrieval pool, so the EHR retrieval context cannot contain labels or text from the same patient. Retrieval is restricted the training cohort only. Moreover, the simulator-informe on PTB-XI. as an external cohort, suggesting that SF.-Diff provides utility without simply memoriz- ing individual training cases or overfitting to specific MIMIC records.
and experience-augmented model maintains its advantage
D PROMPT EXAMPLE
The prompt example in our SE-Diff can be shown in Figure 5.
SYSTEM PROMPT
You are cardiology-focused language model, Your job is to convert structured inputs into concise, clinically accurate narrative suitable cardiology note, You must not invent findings and you must not provide treatment recommendations. You should prioritize the current patient's measurements and observations over any retrieved examples from similar patients.
The report you generate will serve the conditioning text for a downstream text-to-ECG synthesis model. Therefore, the narrative must be internally consistent, and physiologically grounded that it can guide waveform generation.
You will receive (1) current patient context, (2) the original machine generated diagnosis, and (3) retrieval diagnosis from clinically similar patients. Based on these inputs, you will produce exactly paragraph of 60-120 words. Your language should be precise and use standard ECG terminology and units so downstream generator can align text with waveform features.
== DATA PROMPT ===
Original machine-gent machine-generated diagnosis (most important first): Sinus tachycardia Normal ECG except rate Heart rate (bpm): 100
Age (years): 52
Gender:
Retrieved similar patients' diagnases: Sinus rhythm
Normal ECG Possible ectopic atrial tachycardia
Figure 5: Prompt examples.
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E CONDITIONAL LATENT
DETAILS
Context construction. The context concatenates (i) token embeddings from the clinical report Exext E Rmmx (from frozen clinical text encoder) and (ii) a single metadata token émeta E I formed by projecting age/sex/etc. de- We set C Eiesti Emetal IR ((m++))xd, and feed C' to cross-attention at the bottleneck and (optionally) the two highest-resolution decoder blocks.
U-Ner blocks. Each block consists of Conv(k=3) - GroupNorm - SiLU Conv(k=3) with a residual connection. Self-attention (multi-head, follows normalization at lower resolutions. Down- sampling uses stride-2 convolutions; upsampling uses nearest-neighbor followed by Conv(k=3). We use K resolution levels (e.g., K=4) and N blocks per level (e.g., 2-2); channel width doubles on downsampling and halves on upsampling.
Attention.
multi-head with head dimension de (c.g.. heads). Queries at the bottleneck attend to keys/values derived from C: text tokens use sinusoidal positional encodings. Metadata is represented as a single learned token (Huang et al., 2019; Liu et al., 2025).
Time embedding and FILM. The timestep embedding uses exponentially spaced sinusoids and a two- layer MLP (SiLU) produce (Y,, (1) per block. A small MLP on Pool(C) (token average) yields (Ye: Be). FiLM is applied after normalization to every residual block.
Training setup. We use cosine noise schedule and optional learned variance (Nichol & Dhari- wal, 2021), AdamW with EMA, and classifier-free guidance with unconditional dropout Puncond E 10.1, 0.2] and tuned guidance scale. Min-SNR-a weighting (Hang et al., 2023) is optional for stabi- lizing early and late timesteps. Simulator losses are computed on / Dbeat (zo) and removed at inference.
F ABLATION STUDY OF BEAT AND SPECTRAL LOSSES ON THE BEAT DECODER
To further clarify the relationship between the time-domain beat reconstruction loss Leaa in Eq. (13) and the spectral consistency loss Lspee in Eq. (15), we conduct an ablation study where we remove each term in turn while keeping the rest of the architecture and training protocol unchanged. Locat encourages the Beat Decoder output h to match a single R-peak-cent centered cycle C(x) in the time domain, whereas Lxpec matches the log- magnitude spectrum of h to the distribution of spectra com- puted over all detected beats {C,(z)) in the same 10 s window. The two objectives therefore act on complementary aspects of the canonical beat: Treaat anchors à to an actual observed cycle, while Tyspeo regularizes its frequency content toward the ensemble of beats rather than the full 10 signal.
Table 4 reports ECG generation performance on MIMIC-IV-ECG when using both losses (full SE- Diff), removing Lbear ("w/o Beat Loss"), and removing Lipec ("w/o Spec Loss"). Using both losses yields the best performance across all metrics. In particular, dropping Liea leads to a drastic degra- dation in heart-rate accuracy (MAEμR increases from 8.43 to 23.48) and noticeably worse rCLIP and rFID scores, indicating that the canonical beat no longer well aligned with the temporal struc- ture of the underlying rhythm. Removing Tyyee instead mainly harms signal-level fidelity (higher NRMSE and weaker ICLIP//FID), showing that spectral regularization is important for capturing realistic morphology and fine-scale waveform details.
These results empirically support that Loeat and Legse are complementary rather than confliciig:: the pest-performing model is obtained when the canonical beat simultaneously constrained to resemble an actual beat in the time domain and to share spectral statistics with the set of beats present in the 10 s record.
Table 4: ECG generation performance on MIMIC-IV-ECG dataset.
Model
MAE
NRMSE
MALER 1
rCLIP Score f
IFID Score
SE-Diff
0.0923 0.0021
0.0714 + 0.0010
8.43
0.42
0.9470 + 0.0029
0.9509 0.0035
w/o Beat Loss
0.0951 + 0.0020
0.0743 1 0.0016
23.48 + 1.07
0.9393 + 0.0026
0.9140 +: 0.0055
w/o Spec Loss
0.0941 0.0023
0.0797 + 0.0014
16.47 + 1.23
0.9257 = 0.0030
0.9073 0.0055
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G INFERENCE EFFICIENCY OF SE-DIFF FOR 10-SECOND, 12-LEAD ECG GENERATION
To show the inference efficiency, we report the wall-clock latency of SE-Diff for generating 10 12-lead ECGs in Table 5. For each batch size, we measure the time required to run 1000 diffusion sampling steps and average the runtime over three batches.
The results show that the total time per batch remains nearly constant for batch sizes between 32 and 256, the per-sample latency decreases from 0.425 at batch size 32 to 0.050 at batch size 256. Even for very large batches (up to 4096), the wall-clock time grows sublinearly with the batch size, and the per-sample latency can be reduced 0.0205 s. This indicates that SE-Diff can efficiently exploit batching during inference, making large-scale ECG synthesis practical under our current implementation.
Table 5: Inference latency of the model for generating one 10-second, 12-lead ECG under different batch sizes. Times are averaged over 3 batches per configuration, with 1000 diffusion sampling steps.
Batch size
Time batch (s)
Time / sample (s)
32
13.55
0.425
128
13.65
1.505
256
13,40
0.050
512
15.05
0.0295
1024
24.90
0.0245
2048
45.00
0.0220
4096
84.75
0.0205
H EFFECTS OF NOISE SCHEDULE AND SAMPLING STEPS
We first study how the number of diffusion sampling steps affects ECG generation quality when keeping the sampler (DDPM) and noise schedule (linear) fixed. As shown in Table 6, performance generally improves as we increase the number of steps: reducing the steps from 1000 800 and 500 leads to progressively higher NRMSE, larger heart-rate error (MAEnR). and lower rCLIP scores. The default setting of 1000 steps achieves the best trade-off among these metrics, and is therefore used in our main experiments; smaller step counts can still be used in catency-constrained scenarios at the cost of degraded fidelity and semantic alignment.
Table 6: ECG generation performance under different sampling steps.
Sampling Steps
NRMSE +
MAEAR
rCLIP Score
1000
0.0714 0.0010
8.43 + 0.42
0.9470 + 0.0029
800
0.0877 + 0.0011
13.59 + 1.05
0.8671 + 0.0040
500
0.1097 + 0.0010
15.60 + 1.19
0.7888 + 0.0084
Next, compare different sampler types while fixing the number of sampling steps (1000) and using a linear noise schedule. Table shows that our default DDPM sampler in SE-Diff achieves the best overall balance across MAEHR- and rCLIP. While the SDE-based sampler attains slightly lower NRMSE, it substantially worsens heart-rate accuracy and semantic alignment (higher MA FuE and lower rCLIP). DDIM and the second-order solver also underperform DDPM least one key metric. These results support our choice of DDPM the default sampler for ECG generation in SE-Diff.
Finally, we investigate the impact of the noise schedule under the same DDPM sampler and 1000 sampling steps. As reported in Table 8, both the linear and cosine schedules yield comparable performance, but the linear schedule achieves slightly better NRMSE, MAEHR. and rCLIP. This confirms that our default linear schedule is strong and stable choice for ECG generation in SE- Diff, while alternative schedules can be used if desired without dramatically changing performance.
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Table ECG generation performance under different samplers.
Sampler
NRMSE
MA +
rCLIP Score +
SE-Diff (DDPM)
0.0714 0.0010
8.43 + 0.42
0.9470 0.0029
DDIM
0.1219 + 0.0010
9.05 + 0.44
0.7783
0.0072
Second Order
0.0877 + 0.0011
13.59 + 1.05
0.8671
0.0040
SDE
0.0693 + 0.0106
19.21 + 1.27
0.9213 = 0.0028
Table 8: ECG generation performance under different noise schedules.
Sampler
NRMSE +
MAF
rCLIP Score
SE-Diff (Linear)
0.0714 + 0.0010
8.43 0.42
0.9470 + 0.0029
Cosine
0.0728 + 0.0032
8.90 0.78
0.9446 + 0.0131
I ROBUSTNESS OF RCLIP AND RFID TO ENCODER CHOICES
To assess the potential dependence of rCLIP and rFID on the particular ECG/text encoder pair, we compare SE-Diff and DiffuSETS under three encoder configurations. Config A uses our baseline ECG and text encoders. Config B replaces the ECG encoder with alternative ECG representation while keeping the text encoder fixed. Config instead replaces the text encoder while keeping the ECG encoder fixed. For each configuration, we jointly report rCLIP, rFID, signal-level reconstruc- tion error (MAE), and heart-rate error (MAFHR)
As shown in Table 9, SE-Diff consistently outperforms DiffuSETS across all encoder configurations and metrics: MAE and MAEHR are always lower for SE-Diff, and both rCLIP and rFID are always higher. Moreover, the relative ranking between SE--Dff and DiffuSETS is stable when switching ECG or text encoders, indicating that our conclusions are not tied to specific encoder choice. We also observe that improvements in rCLIP/FID co-occur with gains in heart-rate and signal-Ievel fidelity, suggesting that these representation-badt metrics are aligned with clinically meaningful waveform quality rather than being purely encoder-specific artifacts.
Table ECG generation performance comparison between SE-Diff and DiffuSETS under different calibration configurations.
Config
Model
MAE 4
MAEER 4
rCLIP Score f
rFID Score f
DiffuSETS
0.1092 = 0.0022
13.29 1.13
0.9309 + 0. 0.0036
0.9209 0.0038
SE-Diff
0.0923 0.0021
8.43 + 0.42
0.9470 + 0.0029
0.9509
0.0035
B
DiffuSETS
0.1092
0.0022
13.29 + 1.13
0.9347 1 0.0032
0.9197 1: 0.0037
B
SE-Diff
0.0923 0.0021
8.43
0.42
0.9472 + 0.0032
0.9505
0.0035
C
DiffuSETS
0.1092
0.0022
13.29 1 1.13
0.9242 1: 0.0039
0.9209 +: 0.0038
C
SE-Diff
0.0923 0.0021
8.43 + 0.42
0.9393 + 0.0031
0.9509 + 0.0035
J SENSITIVITY ANALYSIS OF EXPERIENCE RETRIEVAL--AGGEETTR CONDITIONING
Table 10 summarizes the sensitivity of the retrieval performance to the tri-view weights (A1, A2, Aa) (diagnoses / prescriptions / procedures) and the number of retrieved admissions k. With equal weights (λ₁, A2, Aa) (1, 1, 1). varying k E 2, 3, 4 yields very similar mean scores (94.4-94.8), with a slight peak at & = 3 (94.9 + 2.3), indicating limited sensitivity to k in this range. For A: = 3, time while keeping the others at I shows that upweighting any single view changes performance by at most ~ 1.5 points, suggesting that the tri-view retrieval is reasonably robust and that the symmetric setting (A1, d2, Aa) = (1, 1,1) with k = 3 is simple and competitive
varying weight
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default used in all main experiments. Retrieval performance is quantified by asking LLM (GPT- 5) to score each retrieved experience report on a 1-100 scale and reporting the mean + standard deviation of these scores.
Table 10: Sensitivity of tri-view weights (A1, X2, As) and top-k on retrieval performance.
A1
2
X
k
LLM Score
1
3
94.9 - 2.3
1
1
2
9.1
1
1
4
2.
2
3
94.7
1
2
1
3
1.5
1
2
3
3.3
3
3
94.3
1
3
1.6
1
3
3
94.2
K EXAMPLE OF EXPERIENCE RETRIEVAL-AUGMENTI CONDITIONING
To make the experience etrreval--agmmeter conditioning pipeline more concrete, we provide a representative example below.
Original machine-generated diagnosis and metadata.
Original machine-generated diagnosis: Sinus rhythm. Heart rate (bpm): 83.
Age (years): 91.
Gender:
Top-k retrieved similar admissions (diagnosis view, =3)
Sinus rhythm
Sinus rhythm
Sinus rhythm with borderline 1st degree A-V block
LLM-generated experience report.
The ECG demonstrates sinus rhythm at a rate of 83 beats per minute in a 91- year-old female. The rhythm regular with normal wave morphology preced- ing each QRS complex, consistent with sinus node origin. There is no evidence of atrioventricular conduction delay or block; PR interval is within normal lim- its. QRS complexes are narrow, indicating normal ventricular conduction. No arrbythmias or ectopic beats are noted. Overall, the tracing reflects stable si- rhythm appropriate for the patient's age without signs of sinus tachycardia conduction abnormalities.
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Reviews:
[{"review_id": "h84cJrpl8c", "reviewer": "Reviewer_f2NA", "summary": "This article presents SE-Diff, a diffusion model for ECG generation enhanced by physiological simulator and retrieved clinical records. Experiments show that SE-Diff prevails over current ECG generation baseline and is competent for enhancing downstream ECG screening model.", "questions": "See Weakness.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "1. The method of combining ODE-based physiological simulator with diffusion-based generative learning is novel in ECG synthesis and provides interpretability and physiological plausibility.\n2. Comprehensive evaluation across fidelity, semantic alignment, and downstream classification demonstrates consistent improvement over baselines.\n3. Visualization of model generated ECG and single-cycle waveform from Beat Decoder provide clear evidence to model efficacy and the validity of proposed method.", "weaknesses": "1. Description of “experience retrieved-augmented condition” is too brief. In the main paper, the condition comprises $(t, m, r)$ while the appendix only refers the concatenation of text token and meta token, which may confound the readers who are not familiar with conditional ECG generation. Moreover, what is value of top-k’s k in practical? \n2. The quality of the “experience-augmented” text conditioning depends heavily on LLM summarization, but neither the type of LLM nor the output case are shown in paper.\n3. In the ECG generation results section, a proper discussion to the model performance is absent, which may critically hinder a further understanding to the benefit of designed module.\n4. The inference efficiency of model is not reported.\n5. The entire work is based on MIMIC ECG dataset, the generalization ability of proposed method is not discussed.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "VLfkYIYG1S", "reviewer": "Reviewer_R6Yk", "summary": "This paper proposed SE-Diff which generates 10-second, 12-lead ECGs by running conditional 1D diffusion in a VAE latent space augmented with an ECG ODE simulator (physics priors) and EHR-based experience (RAG).\nText, metadata, and retrieved reports are injected via cross-attention and FiLM, and Euler/inter-lead constraints are applied on the beat-decoder output to enforce physiological consistency.\nOn MIMIC-IV-ECG, SE-Diff outperforms DiffuSETS across signal-level, alignment, and distribution-level", "questions": "- Can you add unconditional/flow-matching/GAN·SSM/ODE-aware baselines under the same length/leads/conditioning/steps to demonstrate generality?\n\n- Can you report per-case PR, QRSd, QT/QTc, ST shift/slope, and P/T features to quantify morphology and interval fidelity?\n\n- Can you show external-cohort/device results with rFID, HR-MAE, and interval MAE to evidence domain generalization and robustness?", "limitations": "", "rating": 2, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 2, "strengths": "The dual-guidance design (physics + experience) cleanly integrates simulator regularization and EHR-RAG conditioning to jointly improve morphology preservation and text-to-ECG alignment within a well-structured latent-diffusion pipeline.", "weaknesses": "- Baseline coverage is narrow because comparisons center primarily on DiffuSETS instead of including strong adjacent baselines such as unconditional diffusion, flow-matching, GAN/SSM, or ODE-aware variants (e.g.,  https://www.sciencedirect.com/science/article/abs/pii/S1746809425010559\n, https://proceedings.neurips.cc/paper_files/paper/2024/file/9988f2c8e07c1f98af7ba9ca31ccae0b-Paper-Conference.pdf\n, https://arxiv.org/html/2409.17833v1\n).\n\n- Case analyses would be stronger with quantitative morphology and interval metrics per case, such as PR, QRSd, QT/QTc, ST shift/slope, and P/T amplitude/width.\n\n- Evidence for domain generalization is limited because results concentrate on MIMIC-IV-ECG, so small external cohort or device tests reporting rFID, HR-MAE, and interval MAE would better support robustness.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "QDPLtkRoCL", "reviewer": "Reviewer_xoXY", "summary": "The paper proposes SE-Diff: a framework for text-to-10s-12-lead ECG generation that combines a physiological simulator and empirical retrieval in latent space diffusion. Specifically, SE-Diff uses a VAE to encode the waveform into a latent representation, and aligns the Beat Decoder output to the single QRS cardiac cycle. During diffusion training, an Euler consistency loss and a cross-lead (Einthoven/Goldberger) constraint are applied to the Beat Decoder output, incorporating the morphological priors of the ODE simulator. Finally, empirical knowledge is derived through EHR three-view similarity + LLM distillation, which is conditioned on the original diagnosis and metadata. Compared to DiffuSETS, SE-Diff outperforms DiffuSETS in waveform error, heart rate error, and text-to-ECG semantic alignment, and also offers improvements in imbalanced classification augmentation.", "questions": "1. Can the λ1/λ2/λ3, top-k, LLM prompts and temperature of EHR three-view retrieval be fully disclosed in the appendix, and the sensitivity under different settings be reported?\n\n2. Can external validation be performed on PTB-XL or external data, and can ≥3 seed mean ± variance/significance be given (especially corresponding to Tables 1/2)?\n\n3. Do the precordial leads (V1–V6) take into account geometric/body surface projection constraints or learnable linear projections? Can the transconductance joint formulation be extended?\n\n4. Simulator parameters are fitted offline by category. If a case has multiple lesions or rhythmic variations, will there be a fitting mismatch? Are there any explorations of dynamic/case-adaptive fitting and analysis of failure cases?\n\n5. Could you provide additional curves showing the effects of noise schedule and sampling steps (SDE/DDIM/second-order solver) on rCLIP/MAE_HR? Currently, only qualitative analysis of the linear schedule is provided.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. Dual-channel injection of mechanism priors and empirical knowledge: The Beat Decoder maps the latent representation to a single cycle, making the Euler residual and cross-lead identity differentiable and trainable. It also uses Jaccard similarity of EHR three views and LLM distillation to supplement clinical context. The combined design is novel and clearly motivated.\n\n2. Solid quantitative results: On the MIMIC-IV-ECG, SE-Diff reduced MAE/NRMSE to 0.0923/0.0714 and heart rate error MAE_HR to 8.43 compared to DiffuSETS, while also improving rCLIP and rFID (0.947/0.951). Removing \"Experience Search/Across Leads/Simulator\" individually resulted in significant degradation, indicating that all three factors contribute significantly.\n\n3. Downstream value demonstration: In the augmentation of gender and SVT rare class scenarios, SE-Diff improves F1/AUC compared to imbalanced training and DiffuSETS, approaching the true balanced upper bound, demonstrating the practicality of synthetic data.", "weaknesses": "1. Reproducibility details are still incomplete: the three-view weights λ1/λ2/λ3 for empirical retrieval, top-k, and LLM distillation hint templates and versions are not clearly disclosed; the implementation/initialization and failure mode details of the Beat Decoder and simulator fitting are limited, affecting third-party reproduction and fair comparison.\n\n2. The evaluation scope is relatively narrow: it mainly focuses on the MIMIC-IV-ECG single database verification; there is a lack of cross-institutional/external data (such as PTB-XL) system evaluation and robustness reporting (multiple random seeds ± std, significance testing), and currently most tables contain single values.\n\n3. Boundaries of the simulator and constraints: Cross-lead constraints only cover the six identities of the limb/augmented leads, and no structural constraints are imposed on the precordial leads (V1–V6); although the Euler first-order integral + linear noise schedule has an intuitive explanation, there is a lack of comparison with more powerful numerical methods/schedules.\n\n4. Indicator dependence and potential bias: rCLIP/rFID ​​depends on the selected ECG/Text encoder. Although relative scores are used to alleviate this, it is still recommended to report the correlation between encoder variant sensitivity and multiple heart rate/morphology indicators to avoid \"encoder-specific\" conclusions.\n\n5. Ethics/privacy and leakage risks are not fully quantified: EHR retrieval is used for conditional construction (although de-identified), but there is no discussion on whether the retrieval context will leak target case labels/text clues, and there is little assessment of \"synthetic privacy\" and training-test information leakage.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "KdYODwiT1w", "reviewer": "Reviewer_Mxjg", "summary": "The paper proposes SE-Diff, a diffusion-based framework for text-conditioned 12-lead ECG generation that integrates a lightweight ODE ECG simulator via a beat decoder and simulator-consistent constraints, and augments conditioning with EHR-retrieved “experience” knowledge. The topic is timely and relevant. The writing is generally clear and the method is carefully engineered. That said, I have several technical concerns and suggestions that should be addressed.", "questions": "(1) The Eq(13) seems to have a direct optimization conflict with Eq(15). Firstly, in Eq(13), the Mean Squared Error (MSE) is directly used to make the output of the beat decoder equal to the heartbeat cycle sequence \\(C(x)\\) of the first - segment ECG. However, Eq(15) aims to make the output consistent with the overall ECG spectrum. In short, when the model is optimized to the extreme for Eq(13), the output will be equal to \\(C(x)\\), which will inevitably conflict with the loss in Eq(15). Because its spectral information will be equal to the spectral information of the first heartbeat cycle, rather than the overall spectral information expected in Eq(15). \n\n(2) As shown in Eq(18), after fixing the parameters and the initial point of the ECG PHYSIOLOGICAL SIMULATOR, the generated ECG signals from the modeling should be unchanged. Will this lead to a problem? In Eq(18), will the \\(h\\) corresponding to different ECG signals generated by the diffusion model always be optimized to the same target? \n\n(3) The biggest problem of this article is that it does not compare with any of the most advanced ECG generation methods. It is impossible to measure whether the generated ECG has the highest authenticity. The ablation comparison within the method alone is insufficient.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "The paper integrates a physiological ECG simulator into the generative diffusion framework, which enhances the interpretability and physiological plausibility of the generated signals. This simulator-guided design ensures that the outputs are not only visually realistic but also consistent with known electrophysiological mechanisms, representing a meaningful advancement over purely data-driven generative models.", "weaknesses": "(1) The Eq(13) seems to have a direct optimization conflict with Eq(15). Firstly, in Eq(13), the Mean Squared Error (MSE) is directly used to make the output of the beat decoder equal to the heartbeat cycle sequence \\(C(x)\\) of the first - segment ECG. However, Eq(15) aims to make the output consistent with the overall ECG spectrum. In short, when the model is optimized to the extreme for Eq(13), the output will be equal to \\(C(x)\\), which will inevitably conflict with the loss in Eq(15). Because its spectral information will be equal to the spectral information of the first heartbeat cycle, rather than the overall spectral information expected in Eq(15). \n\n(2) As shown in Eq(18), after fixing the parameters and the initial point of the ECG PHYSIOLOGICAL SIMULATOR, the generated ECG signals from the modeling should be unchanged. Will this lead to a problem? In Eq(18), will the \\(h\\) corresponding to different ECG signals generated by the diffusion model always be optimized to the same target? \n\n(3) The biggest problem of this article is that it does not compare with any of the most advanced ECG generation methods. It is impossible to measure whether the generated ECG has the highest authenticity. The ablation comparison within the method alone is insufficient.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)