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REVIEW 3 major objections 5 minor 49 references

Sensitivity of ECG QRS Complexes to His-Purkinje Structure in Computational Heart Models

T0 review · 3 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read Minor variations in the heart's His-Purkinje conduction structure barely change most QRS features on their own, but certain parameter combinations can distort QRS timing or morphology and even obscure the P-wave.

desk verdict A serious, expensive sensitivity study whose central clinical claim is undermined by a 10 Hz ECG low-pass filter that likely distorts the very QRS metrics being analyzed. read the letter →

arxiv 2505.16696 v1 pith:PLMWDN6F submitted 2025-05-22 q-bio.QM stat.OT

Preetam V. Tanikella , Laryssa Abdala , Karin Leiderman , Annie Green Howard , Boyce E. Griffith This is my paper · ORCID
classification q-bio.QMstat.OT
keywords His-PurkinjesystemQRScomplexECGsimulationSobolsensitivityanalysiscardiacdigitaltwinmonodomainmodelventriculardepolarization
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper asks whether the fine details of the His-Purkinje system, the heart's specialized conduction network, matter for the QRS complex seen on an electrocardiogram. The authors generate thousands of plausible Purkinje networks on a single heart mesh, varying nine structural parameters such as branch number, branch length, fascicle angles, and repulsivity, and measure ten QRS features across four leads. They find that changing any one parameter alone rarely moves wave amplitudes, durations, or timing outside normal clinical bounds. The exceptions arise from parameter combinations: joint settings can produce morphologically abnormal QRS complexes, and some networks trigger premature QRS formation that obscures the P-wave. The paper concludes that minor structural differences between a healthy patient's conduction system and a generic model are unlikely to undermine digital-twin fidelity when both are physiologically normal.

What carries the argument

The central machinery is Sobol sensitivity analysis applied to a monodomain reaction-diffusion heart model. Purkinje networks are built by a rule-based fractal-tree algorithm controlled by nine structural parameters; Saltelli sampling generates 22,528 joint parameter combinations; and the ECG is reconstructed from extracellular potentials recovered via the monodomain source model. First-order Sobol indices separate each parameter's direct contribution to variance in a QRS quantity of interest, while total-order Sobol indices capture its contribution through interactions, so the gap between the two reveals how much of the variability is interaction-driven.

What would settle it

Run the same Saltelli–Sobol sampling on two or more additional ventricular geometries from different patients; if a single HPS parameter within the stated ranges shifts a QRS amplitude, duration, or peak time beyond the normal ECG thresholds used here, the claim that minor HPS differences are clinically negligible would be contradicted.

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Extended reading notes

Core claim

The paper's central claim is that QRS morphology is mostly insensitive to individual HPS structural parameters, while interaction effects among parameters carry the clinically visible variability. Sobol first-order indices show that no single parameter exceeds roughly a 10% share of the observed variance for any QRS feature, and most main effects fall below the study's 5% significance threshold; the one consistent exception is the number of branches, which directly affects QRS peak timing. Total-order indices near one indicate that the rare outlier trials, including a QRS with a deep, long S-wave and a cluster of early-formed QRS complexes, come from specific parameter combinations rather than isolated changes. The paper therefore argues that a generic but physiologically normal HPS is adequate for most digital-twin ECG interpretation, while warning that certain joint parameter settings can generate abnormal morphology or premature QRS complexes that future models should account for.

Load-bearing premise

The conclusions assume that results from one fixed heart mesh, with HPS parameters varied only within ±30% of a single nominal set, represent how a healthy patient's His-Purkinje system varies across the human population.

Editorial extensions

If this is right

  • Cardiac digital twins built from a generic healthy HPS should reproduce normal QRS amplitudes and wave durations even if their branching geometry differs slightly from the patient's.
  • Reproducing an abnormal QRS in a model will likely require matching combinations of HPS parameters, not adjusting one branch or fascicle value alone.
  • Because the number of branches has a direct effect on QRS peak timing, timing-sensitive simulations should prioritize getting Purkinje density right.
  • Some HPS configurations produce premature QRS complexes that merge with or obscure the P-wave, which could mislead arrhythmia simulations that depend on P-wave presence.
  • The near-total interaction indices for durations and amplitudes imply that future sensitivity studies of cardiac models should include interaction terms rather than main effects alone.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Because the study samples parameters on a single ventricular mesh, the sensitivity indices likely mix HPS effects with that geometry's own response; repeating the design on several patient-derived meshes would test how far the 'healthy patient' conclusion generalizes.
  • The ±30% parameter ranges were chosen without population data on HPS variability, so wider natural variation, if it exists, could re-scale the low-sensitivity conclusion even without changing the interaction structure.
  • The premature-QRS result suggests a clinical hypothesis the paper does not pursue: unexplained early QRS complexes with apparently missing P-waves on real ECGs could reflect unusual Purkinje architecture rather than primary atrial disease.
  • A targeted follow-up could map the interaction surface by fixing all but two of the influential parameters (branch angle, second fascicle angle, repulsivity) and locating the boundary where QRS timing jumps to the early secondary point.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. The paper performs a global, variance-based sensitivity analysis of His-Purkinje system (HPS) structural parameters on QRS-morphology metrics in a computational human heart model. Nine HPS generation parameters are varied with Saltelli sampling across 22,528 simulations, and ten QRS-related quantities of interest (Q-, R-, S-wave durations, amplitudes, peak times, and overall QRS duration) are extracted from simulated 12-lead ECG traces. First- and total-order Sobol indices are computed with SALib. The authors report low first-order sensitivity for wave amplitudes and durations, large total Sobol indices interpreted as interaction-driven variability, and a small number of outlier trials, including one trial with markedly abnormal QRS morphology and a cluster of trials with premature QRS formation. They conclude that minor individual HPS structural differences are unlikely to affect model fidelity or clinical interpretation in physiologically normal hearts, while certain parameter combinations can produce clinically relevant abnormalities.

Significance. If the conclusions are correct, the study would be a valuable contribution to cardiac digital twin personalization: it is a large-scale application of global sensitivity analysis to a full 3D cardiac electrophysiology model, uses a standard and reproducible sampling and sensitivity pipeline (Saltelli sampling, SALib), and directly targets an understudied source of model uncertainty (HPS structure). However, the central quantitative claims currently rest on QRS features extracted from signals processed with a 10 Hz low-pass filter, which is not representative of diagnostic ECG bandwidth and could compromise every reported amplitude, duration, and sensitivity index. The single-geometry design and arbitrary ±30% parameter ranges also limit the stated clinical generalization. The computational scale and the careful reporting of distributions are strengths, but the filter issue must be resolved before the paper's main claims can be accepted.

major comments (3)
  1. [Section 2.2] Raw extracellular potentials were low-pass filtered with a 10 Hz cutoff before QRS delineation. A 10 Hz cutoff is far below the diagnostic ECG bandwidth (typically 0.05-150 Hz or at least 100 Hz); it severely attenuates sharp QRS peaks and broadens the apparent wave boundaries. Because all 22,528 trials and all ten QOIs are derived from this filtered signal, the reported low sensitivity and the outlier morphologies used to claim interaction-driven clinical changes describe a 10 Hz-filtered signal rather than a clinically representative QRS. The benchmark in the Supplementary validates the forward extracellular-potential computation, not the filter or delineation pipeline, and no validation of the 10 Hz choice is provided. The authors should either correct this if it is a typo, justify the cutoff, or re-run the analysis at a diagnostic bandwidth and show that the conclusions are unchanged.
  2. [Section 3.3] The text reports that all total Sobol indices 'either equaled one or contained one in their confidence intervals,' but no confidence intervals or estimator diagnostics are reported anywhere in the paper or supplementary materials. Reporting uncertainty on Sobol indices is necessary because ST near one for every parameter is an extreme claim (it implies every parameter participates in interactions that collectively explain essentially all output variance) and can be an artifact of estimator bias or finite sample size. Please report bootstrap confidence intervals, replicate-based estimates, or convergence checks for both S1 and ST, and temper the interaction-driven interpretation accordingly.
  3. [Sections 2.2, 2.3, and 5] The conclusion that 'minor structural differences between a healthy patient's HPS and that of a generic model are unlikely to significantly impact model fidelity or clinical interpretation' is an inference about human populations, but the evidence comes from a single bi-ventricular mesh derived from one de-identified CT scan and from parameter ranges chosen as ±30% of nominal values because 'little is known about the true distribution of HPS structure among human populations.' This is not an internal inconsistency, but it is an overreach: the sensitivity indices and outlier rates could change with ventricular geometry, torso anatomy, or population-informed parameter ranges. The conclusion should be restricted to the studied mesh and ranges, or supported by additional geometries and population-informed distributions.
minor comments (5)
  1. [Section 3.2] The text says 'Table 4 presents the summary statistics for the peak time distributions,' but Table 4 reports QRS duration statistics; the peak-time statistics are in Table 5. Please correct the cross-reference.
  2. [Section 4] The claim that premature QRS formation 'obscured P-wave formation' is based on visual inspection of Figure S10, while the paper explicitly states that P waves were not characterized. This should be framed as a qualitative observation or supported by quantitative P-wave analysis.
  3. [Section 2.2 and Table S1] There are minor unit and notation inconsistencies: '2.16mS cm' should be '2.16 mS/cm'; Table S1 uses 'mv' rather than 'mV'; and Eq. (1) uses a scalar σ_b although the text refers to a conductivity tensor.
  4. [Section 3.1] The sentence 'The number of outlier trials for these QOI was relatively small compared to the total number of trials conducted was very low (<1%)' contains a duplicated predicate and should be rephrased for clarity.
  5. [Figure 6] The right panel's y-axis label 'Percent Difference from Nominal Trial (%)' is unclear; please clarify whether the plot is a scatter plot, rug plot, or histogram of the 25 outlier trials, and define the parameter color mapping in the caption.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the Sobol sensitivity results are computed from direct simulation outputs, and the only self-citations are background modeling tools, not load-bearing evidence.

full rationale

The derivation chain runs from nine HPS generation parameters, through fractal-tree network generation, monodomain simulation, ECG recovery, 10 Hz filtering, MATLAB wave delineation, and finally to ten QRS QOIs, with Sobol indices computed externally using SALib. At no point is a fitted parameter folded into the reported statistics: the nominal parameter set is chosen to produce a normal QRS morphology, but the sensitivity result is not an optimization residual and the QOIs are not regression targets. The 22,528 trials are direct model outputs, and the first- and total-order indices are defined by standard variance decompositions and computed with an independent library. The cited prior work by the authors ([27] heart mesh; [29] monodomain implementation) supplies infrastructure rather than the conclusion, and the ECG forward calculation is checked against the external Bishop–Plank benchmark. The 'healthy patient' generalization is limited by the single heart mesh and the assumed ±30% parameter ranges, and the 10 Hz low-pass filter is a potential correctness risk for diagnostic QRS bandwidth, but these concern external validity and measurement fidelity, not circularity. Accordingly, no circular step can be exhibited, so the circularity score is 0.

Assumptions & free parameters 5 free parameters · 6 assumptions · 0 invented entities

The central claim rests on several unverified modeling choices: single heart geometry, chosen parameter ranges, the fractal-tree generator's realism, a 10 Hz low-pass filter, and one random realization per parameter set. These are inputs pulled from prior work or chosen by hand rather than derived, and they set the domain in which the sensitivity indices are meaningful.

free parameters (5)
  • Variation range ±30% for HPS parameters
    Chosen to represent 'moderately large differences' because the true population distribution of HPS structure is unknown; the central conclusion depends on these ranges capturing realistic minor differences.
  • Number of branch generations range = 9 to 17
    Varied as a free range; the number of branches is found to affect QRS peak timing, central to one of the conclusions.
  • Nominal HPS parameters = initial branch length 50 mm, median branch length 6 mm, branch angle 0.2 rad, etc.
    Nominal values chosen by hand so the baseline simulation produces a normal adult QRS; the sensitivity analysis samples around this hand-picked point.
  • Sobol significance threshold = 0.05
    Indices above 0.05 are labeled significant; the threshold is a convention from [43] and affects which parameters are called main drivers.
  • Torso conductivity sigma_b = 2.16 mS/cm
    Set from prior patient-based studies, not fitted here; enters the ECG potential formula and influences absolute amplitudes.
assumptions (6)
  • domain assumption The monodomain reaction-diffusion equation with an unbounded volume conductor recovers ECG accurately.
    Equations (1)-(2) in Section 2.2 assume unbounded homogeneous torso; ECG morphology may differ with more realistic torso and boundary conditions.
  • domain assumption The fractal-tree rule-based algorithm generates physiologically realistic HPS structures.
    Section 2.1 uses fractal-tree [17] to generate networks; the conclusions about 'healthy patients' inherit this assumption that generated networks resemble human HPS anatomy.
  • ad hoc to paper A 10 Hz low-pass filter preserves the QRS features being measured.
    Section 2.2 states raw signals were low-pass filtered with cutoff 10 Hz using ECG-Deli; a 10 Hz cutoff can attenuate high-frequency components of QRS and bias duration and amplitude measurements, and this choice is not standard for QRS morphology analysis.
  • domain assumption One fixed heart mesh and one torso geometry represent a generic healthy adult male.
    Section 2.2 describes a single heart mesh from one de-identified CT and a torso scaled to an adult male; all trials use this one geometry, so geometric variability is not sampled.
  • domain assumption Nash-Panfilov ionic model captures relevant human ventricular and Purkinje electrophysiology.
    Section 2.2 uses the Nash-Panfilov model for atria, Purkinje, and ventricles; the QRS outputs depend on this ionic model's properties.
  • domain assumption One random realization per parameter set is sufficient for variance-based sensitivity analysis.
    The fractal-tree algorithm is stochastic, but each Saltelli sample is simulated once, so seed-to-seed variability is not separated from parameter-driven variability.

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Cite this review

Pith. "Pith review of Sensitivity of ECG QRS Complexes to His-Purkinje Structure in Computational Heart Models." pith.science (2026). https://pith.science/paper/PLMWDN6F

@misc{pith2026250516696,
  author       = {Pith},
  title        = {Pith review of: Sensitivity of ECG QRS Complexes to His-Purkinje Structure in Computational Heart Models},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/PLMWDN6F}},
  note         = {Machine review of arXiv:2505.16696}
}
read the original abstract

Cardiac digital twins (CDT) are emerging as a potentially transformative tool in cardiology. A critical yet understudied determinant of CDT accuracy is the His-Purkinje system (HPS), which influences ventricular depolarization and shapes the QRS complex of the electrocardiogram (ECG). Here, we quantify how structural variations in the HPS alter QRS morphology and identify which parameters drive this variability. We generated HPS structures using a fractal-tree, rule-based algorithm, systematically varying nine model parameters and assessing their effects on ten QRS-related metrics. We conducted a Sobol sensitivity analysis to quantify direct and interaction-driven contributions of each parameter to observed variability. Our results suggest that most minor changes in HPS structure exert minimal influence on individual QRS features; however, certain parameter combinations can produce abnormal QRS morphologies. Wave durations and peak amplitudes of the QRS complex exhibit low sensitivity to individual HPS parameter variations; however, we found that specific parameter combinations can result in interactions that significantly alter these aspects of QRS morphology. We found that certain HPS structures can cause premature QRS formation, obscuring P-wave formation. QRS timing variability was primarily driven by interactions among branch and fascicle angles and branch repulsivity, though other parameters also showed notable interaction effects. In addition to interactions, individual variations in the number of branches in the HPS also affected QRS timing. While future models should account for these potential sources of variability, this study indicates that minor anatomical differences between a healthy patient's HPS and that of a generic model are unlikely to significantly impact model fidelity or clinical interpretation when both systems are physiologically normal.

Figures

Figures reproduced from arXiv: 2505.16696 by the authors.

Figure 1
Figure 1. Purkinje fiber network generated using fractal-tree. The network is visualized on a bi-ventricular model to illustrate its branching pattern and spatial distribution across the left and right ventricular surfaces. Nash-Panfilov ionic model [30]. Ionic dynamics were integrated using a second-order strong-stability preserving Runge-Kutta method [31]. ECG signals were computed by recovering the extracellular potentials… view at source ↗
Figure 2
Figure 2. Whole heart model with overlaid Purkinje fiber system, embedded in an open-source torso model to determine spatial electrode distribution for 12-lead ECG. To determine electrode locations, the heart model was placed within an open source torso model, which was modified to reflect anatomically accurate dimensions of an adult male, with a torso height of 46.5 cm and a heart depth of 3.75 cm [35, 36]. Electrodes were t… view at source ↗
Figure 3
Figure 3. illustrates the identification of peaks in the filtered signal recorded at electrode V2, generated from the initial simulation using the nominal parameters. 150 200 250 300 350 Time (ms) -1 -0.5 0 0.5 1 1.5 2 Amplitude (mV) Q wave start Q wave peak Q wave end R wave peak S wave start S wave peak S wave end [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: Distribution of R Wave peak times in lead V6 outliers. 𝑧IQR and 𝑧range values ignoring outliers for peak times was similar to those of the previously discussed QOI, with z-score quartiles within 0.7 standard deviations of the mean and nearly all data falling within 2.5…
Figure 5
Figure 5. Figure 5: First-order Sobol indices for wave durations, highlighting cases where at least one lead shows a statistically significant contribution. of the observed variability (𝑆1 = 0.0474; see Table S8 in the Supplementary Tables). In the case of wave durations, branch angle had…
Figure 6
Figure 6. Figure 6: (Left) First-order Sobol sensitivity indices for the number of branches across all peak-time simulations. (Right) Percent differences from nominal values for each of the 9 parameters across the 25 outlier trials, with the y-axis indicating the percent deviation from no…
Figure 7
Figure 7. Figure 7: Total Sobol Sensitivity Indices for all Parameters across Peak Times all data points fell within 2.5 standard deviations of the mean, with the majority concentrated within 0.5–0.75 standard deviations. The greatest standard deviations for peak amplitudes and wave durat…

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Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.