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REVIEW 4 major objections 6 minor 36 references

Complex Deep Learning Models for Denoising of Human Heart ECG signals

T0 review · 4 major / 6 minor · reviewed 2026-08-14 · deepseek-v4-flash

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Pith's one-line read A CNN trained on synthetic multiple-heartbeat ECG built from a patient's rest recording can, the paper argues, denoise that patient's real effort ECG.

desk verdict Patient-specific synthetic ECG augmentation is a reasonable idea, but the paper never validates it: same-record training/testing and a negative cross-record SNR sink the central claim. read the letter →

arxiv 1908.10417 v3 pith:WVMJMGSR submitted 2019-08-27 cs.LG cs.CVeess.SPstat.ML

Corneliu Arsene This is my paper · ORCID
classification cs.LGcs.CVeess.SPstat.ML
keywords ECGdenoisingconvolutionalneuralnetworksyntheticgenerationmultiple-heartbeatdesignofexperimentssignal-to-noiseratioLSTMRestrictedBoltzmannMachine
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 argues that a convolutional neural network trained on synthetic one-second ECG segments, generated from a few seconds of a patient's clean resting electrocardiogram by varying heart rate and adding heavy noise, can denoise that same patient's ECG when it is recorded at effort. Across comparisons on real and synthetic ECG data, the CNN outperforms LSTM, RBM, and wavelet denoising in the tested settings, and a Design-of-Experiments study picks a preferred CNN structure with 3 convolutional layers, 96 filters, and 13x1 kernels. The payoff would be personalized, real-time ECG denoising for wearables and ambulatory monitors without requiring large labeled ECG datasets. The paper's cross-record test also shows that a model trained on ten records does not transfer to an eleventh unseen record, where average SNR is negative.

What carries the argument

The central machinery is a 15-layer regression CNN acting on 1-second ECG windows of 360 samples: three convolutional blocks with 36 filters, 23x23 kernels, batch normalization, ReLU, and average pooling with stride [4 1], feeding a fully connected layer and a mean-squared-error regression output. The complementary machinery is the synthetic-data generator: starting from a few real rest heartbeats, it creates multiple-heartbeat 1-second windows at 72-90 beats per minute and adds real electrode-motion plus random noise at SNRs -6, 0, 6, and 12 dB to simulate effort ECG. The DoE sweep over 51 architectures is the selection mechanism: it ranks filter counts from 16 to 96 and kernel sizes from 9x1 to 99x1, plus selected square kernels, by testing RMS, SNR, and runtime, yielding the recommended 96 filters and 13x1 kernels.

What would settle it

Record one patient's clean resting ECG and then the same patient's ECG during exercise while simultaneously capturing a clean reference from a different lead or a high-grade monitor. Train the paper's CNN on synthetic 1-second effort-like windows generated from the rest ECG, apply it to the real effort recording, and compute SNR and RMS against the clean reference. If the denoised effort ECG does not reach positive SNR or does not preserve R-peak positions, the rest-to-effort transfer premise is falsified.

Watch

Extended reading notes

Core claim

On the paper's own terms, the central discovery is that a regression CNN can learn to map a heavily corrupted 1-second ECG window back to its clean form, and, more specifically, that synthetic multiple-heartbeat training windows generated from a few clean rest sequences of the same ECG record can stand in for real effort ECG. The CNN with three convolutional layers, 36 filters of size 23x23 per layer, batch normalization, ReLU, average pooling with stride 4, and a fully connected regression head reached average testing SNR of 15.02 dB when trained and tested on 1-second windows of one ECG record, and 14.77-16.45 dB on a second record. When trained on synthetic multi-heartbeat data varied over 72-90 beats per minute and noise levels from -6 to 12 dB SNR, it reached average testing SNR of 8.2 dB (or 9.67 dB with a larger training set), and it outperformed LSTM, RBM, and wavelet baselines in the heavy-noise conditions tested. The Design-of-Experiments sweep over 51 filter and kernel combinations identifies 96 filters per layer with 13x1 kernels as the best accuracy-versus-time trade-off for a three-layer CNN.

Load-bearing premise

The load-bearing premise is that synthetic multiple-heartbeat ECG made from a patient's few rest sequences, by changing heart rate and adding electrode-motion and random noise, is representative enough of that patient's real effort ECG that a CNN trained on the synthetic data will denoise the real signal.

Editorial extensions

If this is right

  • A CNN trained on per-patient synthetic effort-like ECG can denoise multi-heartbeat 1-second windows from the same source, reaching average testing SNR of 8.2 dB with the smaller training set and 9.67 dB with the larger one.
  • Single-record training and testing reaches average SNR around 15 dB on one record and 14.77-16.45 dB on another, well above the paper's 8 dB threshold for carrying useful information.
  • The CNN beats the LSTM and RBM baselines in the tested configurations and beats wavelet denoising when the noise is heavy and drifting rather than simply additive.
  • The DoE sweep shows that accuracy saturates as filters and kernel sizes grow while compute keeps increasing, so the recommended 96-filter, 13x1-kernel architecture is the best available trade-off.
  • Training on ten records and testing on a different record gives negative average SNR, so a single generic CNN is not sufficient; the paper's personalization route is essential to its proposed use.

Reading between the lines

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

  • If the rest-to-effort transfer holds, a wearable device could personalize its denoiser in the field: capture a few seconds of clean rest ECG, synthesize an effort-like training set, and fine-tune a small CNN without a central labeled ECG repository.
  • The negative cross-record result suggests the method's value is personalization rather than a universal denoiser; extending to a new patient would require per-patient synthesis or a domain-adaptation step, neither of which the paper tests.
  • A decisive same-patient validation would compare rest-trained synthetic denoising on real effort ECG against a simultaneous clean reference; the paper's evaluations stop at synthetic variants of rest data and one negative cross-record test.
  • The DoE recommendation is tied to one noise type, electrode motion artefact, one sampling rate, and one window length; other noise types or leads could shift the optimal filter and kernel counts.
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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

4 major / 6 minor

Summary. The paper compares deep learning models (CNN, LSTM, RBM) and conventional/wavelet methods for ECG denoising using synthetic signals and MIT-BIH PhysioNet records. The central proposal is to train a CNN on 1-second synthetic multiple-heartbeat ECG signals generated from a few rest sequences of a given record (record 118), varying heart rate from 72 to 90 bpm and adding noise at SNR levels -6, 0, 6, 12 dB, and then to use the trained CNN to denoise the same patient's ECG at effort (Section 4.5). The paper also reports a 'Design of Experiment' study (Section 4) that selects an optimal CNN architecture, and it compares results on same-record and cross-record settings (Sections 4.1-4.4). The headline same-record results are favorable (e.g., average SNR 15.02 in Section 4.1), but the only held-out-record test in Section 4.4 yields average SNR -2.0776 dB on record 100, and all Section 4.5 evaluations are on synthetic variants of the same record used for training.

Significance. If the central claim were validated, a per-patient CNN trained on rest-derived synthetic ECG data could enable real-time denoising for wearable and exercise ECG monitoring, which would be practically valuable. The paper has useful strengths: it provides detailed network architectures (Tables 1, 2, 5, 6, 9), compares several baselines on the same data, uses publicly available PhysioNet records, and reports the negative cross-record result honestly rather than hiding it. However, the significance is heavily limited because the main deployment claim rests on an untested synthetic-to-real transfer, and the reported performance numbers are not supported by statistical validation. The 'DoE' study, presented as a novelty, is actually a one-factor-at-a-time grid search without the design and analysis machinery usually associated with design of experiments. The paper would need new experiments on real effort ECG data, proper held-out validation across multiple records, and repeated-seed statistics to support its conclusions.

major comments (4)
  1. [Section 4.5] The central claim that a CNN trained on synthetic multiple-heartbeat signals generated from rest ECG can denoise the same patient's ECG at effort is never tested. All Section 4.5 evaluations are on synthetic test signals drawn from the same generator and the same record 118; no real effort ECG signal is used. The load-bearing premise is that time-scaled/repeated rest heartbeats plus added noise faithfully represent effort ECG, but effort ECG differs from rest ECG beyond heart rate (e.g., ST-T changes, amplitude changes, exercise-induced baseline wander). The paper's own Conclusion (Section 5) phrases the real-time use as something that 'would be' useful, not as a demonstrated result. This unsupported transfer is the basis of the abstract's headline claim.
  2. [Section 4.4] The only out-of-family evaluation in the paper, training on 10 records and testing on record 100, gives average RMS 0.3360 and average SNR -2.0776 dB (Section 4.4, final paragraph), which is far below the paper's own 8 dB usefulness threshold stated in Section 2 and far worse than the same-record results (e.g., average SNR 15.02 in Section 4.1). This negative result directly contradicts the generalization needed for the proposed real-time deployment, and it is acknowledged in the text as showing the difficulty of approximating diverse ECG signals with one model. However, the paper then proceeds to Section 4.5 without breaking the self-reference: the proposed per-patient solution is only validated on synthetic variants of the same record, so the cross-record failure remains unresolved.
  3. [Sections 4.1 and 4.5, Table 3] The reported performance metrics lack statistical rigor and are likely optimistic. In Sections 4.1, 4.2, and 4.5, training and testing are done on random splits from the same record, and because the synthetic signals are generated from a small set of rest sequences by time-scaling and repeating heartbeats, the split can place near-duplicate waveforms in both training and test sets, inflating the average RMS/SNR values. Moreover, no error bars, no multiple random seeds, and no repeated cross-validation are reported. The DoE architecture selection in Table 3 also uses the same test split to choose the 'optimal' CNN, so the selected architecture is tuned to that split and the reported SNR values (e.g., 12.36 dB for simulation 46) are not an unbiased estimate of performance.
  4. [Section 4, 'Design of Experiment'] The 'Design of Experiment' study is not a design of experiments in the usual statistical sense. It is a grid search over 51 configurations with different filter counts and kernel sizes, with no factorial structure, replication, blocking, or analysis of variance. The selection rule based on average RMS, average SNR, and computational time is ad hoc, and no statistical significance is attached to the chosen optimum. The abstract and conclusions claim novelty for a DoE study, but the actual methodology is a standard hyperparameter sweep; the claim should be reframed or the methodology upgraded.
minor comments (6)
  1. [Abstract] The abstract mentions 'the standard wavelet-based technique for denoising EEG signals'; this appears to be a typo for ECG signals and should be corrected.
  2. [Figure 23 caption] The caption for Figure 23 references 'Fig.22(a)' when describing the second panel; the cross-reference is inconsistent and should be fixed.
  3. [Section 2] The proposed RMS limit of 0.3 mV (5% of a 6 mV range) is introduced without justification or clinical basis; if it is used as an acceptance criterion, its choice should be explained and its sensitivity assessed.
  4. [Section 4.5] The exact procedure for generating the multiple-heartbeat synthetic dataset is under-specified: the text says heartbeats are varied between 12 and 15 per 10 seconds, but does not state how rest heartbeats are selected, how time-scaling is applied, or how the electrode motion artefact is scaled to the four SNR levels. This makes reproducibility difficult.
  5. [Algorithm 1] The phrase 'first order zero phase delay Butterworth filters' should be 'zero-phase Butterworth filters' for clarity and standard terminology.
  6. [Throughout] Several statements mix 'epochs' and 'iterations' (e.g., Section 4.4 says '250 iterations (i.e. 94400 iterations)'); the terminology should be made consistent and precise.

Circularity Check

2 steps flagged · score 6.0 of 10

Central 'ECG at effort' claim is validated only on synthetic data generated from the same record and same generator; the paper's own cross-record test shows negative SNR.

  1. self definitional [Abstract; Section 4.5 (dataset generation)]
    "the solution put forth in this work is to train a CNN model on 1 second ECG noisy artificial multiple heartbeat data (i.e. ECG at effort), which was generated in a first instance based on few sequences of real signal heartbeat ECG data (i.e. ECG at rest). Afterwards it would be possible to use the trained CNN model in real life situations to denoise the ECG signal."

    The paper labels synthetically generated multi-heartbeat data as 'ECG at effort' purely by construction: the data is produced from rest ECG by varying heart rate (72-90 bpm) and adding noise. No actual effort ECG is recorded or used. The claimed capability to denoise a person's ECG at effort therefore rests on the definitional identification of synthetic generator output with effort ECG, which is exactly the premise the experiments would need to validate. The conclusion repeats the same identification ('ECG at effort' generated from 'ECG at rest') instead of deriving it from any real effort signal.

  2. fitted input called prediction [Section 4.5 (evaluation); Eq. (2)-(3)]
    "The number of heartbeats was varied between 12 heartbeats per 10 seconds to 15 heartbeats per 10 seconds ... This results in a dataset of 14256 clean different training ECG signals ... This dataset (I) is divided into four parts: three parts of size 10692 representing the training dataset and the remaining 3564 signals representing the testing dataset. ... The average RMS over the testing dataset is 0.1781 and average SNR over the entire testing dataset is 8.2."

    The headline SNR/RMS numbers are computed on a held-out split of the same synthetic dataset, generated by the same procedure and from the same record 118 as the training data. Because every clean test signal is constructed from the few rest heartbeats of record 118 by the same heart-rate scaling and noise addition, the random split can place near-duplicate waveforms in both train and test; the evaluation is interpolation inside the generator family. This is not a prediction of real effort ECG. The paper's own out-of-family test (Section 4.4, record 100) gives average SNR = -2.0776, showing the claimed generalization does not hold outside the generator's inputs.

full rationale

The paper's central contribution is the proposal to generate synthetic 'ECG at effort' signals from a few rest sequences of the same patient, train a CNN on them, and then use the CNN to denoise real effort ECG. The circularity is concentrated in how that claim is supported. In Section 4.5, both the training and testing signals come from the same generator and the same record 118; the reported average SNR of 8.2-9.7 dB is therefore a self-referential measure of how well the CNN reconstructs signals inside the generator's own family, not a demonstration of transfer to actual effort ECG. The abstract and conclusion present this same synthetic construction as 'ECG at effort' by definition, without any real effort recording entering the evaluation. The paper itself provides the decisive counterexample in Section 4.4: when the CNN is trained on ten records and tested on a different record (record 100), the average SNR is -2.0776 dB, contradicting the headline positive results and confirming that the favorable numbers depend on the training and test data being drawn from the same source. The DoE architecture search is performed on a single record and its test split, which is a methodology weakness rather than a circular step, and the author's self-citations in references [34]-[36] are not load-bearing. Because the main deployment prediction reduces to an in-family synthetic evaluation, a score of 6 is appropriate.

Assumptions & free parameters 3 free parameters · 3 assumptions · 0 invented entities

The central evaluation rests on the assumption that MIT-BIH records are clean references, that added noise mimics real artifacts, and that synthetic ECG approximates real ECG. No code or data is released, and arbitrary thresholds define the quality criteria.

free parameters (3)
  • Training SNR levels = {-6, 0, 6, 12} dB
    Chosen by hand for dataset generation; central results vary with this choice.
  • CNN architecture = 36 filters, 23x23 kernel, 3 convolutional layers
    Selected via DoE on record 118, then used for all subsequent experiments despite the DoE's optimal being simulation 46 (96 filters, 13x1 kernel).
  • RMS acceptance threshold = 0.3 mV (5% of 6 mV)
    Arbitrary threshold introduced in Section 2 to judge denoising quality.
assumptions (3)
  • domain assumption Synthetic ECG from the McSharry dynamical model resembles real human ECG sufficiently for training denoisers.
    Invoked in Section 2 to generate 6888-signal synthetic datasets.
  • domain assumption MIT-BIH record signals used as clean ground truth are noise-free.
    The original records are treated as clean y_clean in RMS/SNR computations (Section 2), despite containing real-world noise.
  • domain assumption Adding synthetic or nst.c-generated noise to a clean record simulates real noisy ECG conditions.
    This is how all training and testing noisy signals are produced; the validity of this substitution is not independently verified.

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Cite this review

Pith. "Pith review of Complex Deep Learning Models for Denoising of Human Heart ECG signals." pith.science (2026). https://pith.science/paper/WVMJMGSR

@misc{pith2026190810417,
  author       = {Pith},
  title        = {Pith review of: Complex Deep Learning Models for Denoising of Human Heart ECG signals},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/WVMJMGSR}},
  note         = {Machine review of arXiv:1908.10417}
}
read the original abstract

Effective and powerful methods for denoising real electrocardiogram (ECG) signals are important for wearable sensors and devices. Deep Learning (DL) models have been used extensively in image processing and other domains with great success but only very recently have been used in processing ECG signals. This paper presents several DL models namely Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), Restricted Boltzmann Machine (RBM) together with the more conventional filtering methods (low pass filtering, high pass filtering, Notch filtering) and the standard wavelet-based technique for denoising EEG signals. These methods are trained, tested and evaluated on different synthetic and real ECG datasets taken from the MIT PhysioNet database and for different simulation conditions (i.e. various lengths of the ECG signals, single or multiple records). The results show the CNN model is a performant model that can be used for off-line denoising ECG applications where it is satisfactory to train on a clean part of an ECG signal from an ECG record, and then to test on the same ECG signal, which would have some high level of noise added to it. However, for real-time applications or near-real time applications, this task becomes more cumbersome, as the clean part of an ECG signal is very probable to be very limited in size. Therefore the solution put forth in this work is to train a CNN model on 1 second ECG noisy artificial multiple heartbeat data (i.e. ECG at effort), which was generated in a first instance based on few sequences of real signal heartbeat ECG data (i.e. ECG at rest). Afterwards it would be possible to use the trained CNN model in real life situations to denoise the ECG signal.

Figures

Figures reproduced from arXiv: 1908.10417 by the authors.

Figure 4
Figure 4. Comparison between a clean ECG signal and a typical noisy ECG sig [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
Figure 12
Figure 12. Average RMS, average SNR, computational times for 51 simulations and different CNN architectures: a) average RMS calculated over the testing dataset; b) average SNR calculated over the testing dataset; c) computational times. By looking to [PITH_FULL_IMAGE:figures/full_fig_p024_12.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

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