Heart Rate Classification in ECG Signals Using Machine Learning and Deep Learning

Thien Nhan Vo

REVIEW 4 major objections 6 minor 10 references

Heart Rate Classification in ECG Signals Using Machine Learning and Deep Learning

T0 review · 4 major / 6 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read On an ECG heartbeat dataset, LightGBM trained on hand-crafted waveform and interval features reaches 0.94 macro F1, outperforming a CNN that classifies image-transformed beats at 0.85 macro F1.

desk verdict A plausible but unverifiable benchmark: without the dataset name and the train/test split, the LightGBM-vs-CNN F1 gap may be patient memorization, not generalization. read the letter →

arxiv 2506.06349 v2 pith:OBNGUFOW submitted 2025-06-02 eess.SP cs.CVcs.LG

Thien Nhan Vo This is my paper · ORCID

classification eess.SPcs.CVcs.LG

keywords ECGheartbeatclassificationLightGBMhand-craftedfeaturesGramianAngularFieldMarkovTransitionRecurrencePlotdeeplearning

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 which representation of a single ECG heartbeat serves automated classification best: a small set of hand-crafted statistics or an image built from the raw waveform. Using a dataset of 2-lead ECG beats with R-peak positions and beat annotations, the author trains classical models on 70-sample normalized beats plus HRV and RR-interval features, and deep CNNs on Gramian Angular Field, Markov Transition Field, and Recurrence Plot images. The best classical model, LightGBM, reaches 99% accuracy and 0.94 macro F1, beating the best image-based CNN at 0.85 macro F1. The paper's conclusion is that hand-crafted features capture temporal and morphological variation better than single-beat image encodings, and that SVM and AdaBoost are not competitive on this task.

What carries the argument

The comparison runs on two competing input representations. Pipeline one concatenates each normalized 70-sample beat with heart-rate variability statistics and the logarithms of successive RR intervals, then feeds that vector to LightGBM, a gradient-boosted decision tree ensemble. Pipeline two encodes each beat as three 32x32 images—a Gramian Angular Field (a polar-coordinate matrix of temporal correlations), a Markov Transition Field (a matrix of transition probabilities between value quantiles), and a Recurrence Plot (a matrix marking times at which values repeat)—stacked as RGB channels and classified by an Inception-style CNN. The load-bearing mechanism is the information each representation preserves: the hand-crafted vector carries both beat morphology and inter-beat timing, while each image encodes only one beat's shape.

What would settle it

Re-run the two best models with a patient-wise split—putting every beat of any given patient into either the training set or the test set, never both—and compare the macro F1 scores; if LightGBM's advantage over the image-based CNN shrinks or disappears, the claim that hand-crafted features generalize better is not supported by this dataset.

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Extended reading notes

Core claim

The central claim is a direct performance comparison: for this ECG dataset, the LightGBM model fed hand-crafted features (mean, variance, median HRV, logarithms of consecutive RR distances, and the 70-sample beat) achieves 0.94 macro F1 and 99% accuracy, while the best deep-learning pipeline, an Inception-style CNN taking GAF, MTF, and RP images of the same beats as input, achieves 0.85 macro F1. The author interprets this gap as evidence that hand-crafted features encode the full variation of the beats and their timing, whereas the image representations contain information about a single beat only. Random Forest matches LightGBM closely at 0.93 F1, and LSTM variants trail slightly, which the paper reads as confirmation that ensemble trees on well-chosen features are sufficient for this task.

Load-bearing premise

The reported numbers assume the test set is a fresh, representative sample of beats that did not influence training, but the paper never states whether beats were split by patient or by record, so the 0.94 versus 0.85 F1 gap could partly reflect memorized patient-specific waveforms rather than genuine generalization.

Editorial extensions

If this is right

On this dataset, hand-crafted features plus LightGBM give the best single-beat classification accuracy, with Random Forest close behind.
The 0.85 F1 of the image-based CNN is a concrete baseline for any future image-representation method on ECG beats.
SVM and AdaBoost perform too poorly to be competitive on these features.
The author's conclusion implies that converting beats to images for deep CNNs is not automatically the best route when interpretable features are available.

Reading between the lines

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

Because the train/test split is unspecified, the headline gap likely overstates performance on a new patient; a per-patient split is the immediate experiment that would settle it.
The comparison changes representation and model class at the same time, so it cannot separate feature engineering from architecture choice; a matched test feeding raw beats to both LightGBM and a CNN would isolate the cause.
The author's own proposal to classify a beat using four previous beats suggests that inter-beat context may matter more than either single-beat images or single-beat statistics.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

This is a competent but unverifiable benchmark: LightGBM on hand-crafted HRV/RR features beats a CNN on GAF/MTF/recurrence-plot images for ECG beat classification (0.99 accuracy/0.94 F1 vs 0.85 F1). The thing to know before recommending it to anyone is that the dataset is never named and the train/test split is never described. The headline comparison therefore may be measuring within-patient memorization rather than generalization.

Credit where due: the paper is honest about being an empirical comparison, not a new method. It tests a reasonable set of classifiers, uses standard encodings, describes the transform math correctly, says SMOTE was applied only to the training data, and reports macro-averaged metrics. The best part is that it identifies a practical question—whether hand-crafted features still earn their keep against image-based deep learning—and gives a coherent, if limited, answer.

The soft spots are substantial. First, no dataset identity. The preprocessing description (2-lead, R-peak files, beat annotations) strongly suggests MIT-BIH, but the paper never says. Second, no split details: random beat-level splitting would put beats from the same patient in both train and test, and with RR-interval features the model can memorize patient identity. That makes the 0.94 vs 0.85 F1 gap uninterpretable. The stress-test raises exactly this, and on reading the paper it holds. Third, no error bars, confidence intervals, per-class metrics, or code. There is also a red flag in Table I: the 1D CNN has accuracy 0.92 but F1 0.39, which means one or more classes are essentially never predicted correctly; the paper doesn't discuss it. Minor, and cosmetic: one citation (a supply-chain paper) does not fit the topic.

The abstract's broader claim—hand-crafted features capture temporal and morphological variation better than image encodings—is not supported by one undisclosed dataset with an unknown split. The paper is best read as a draft that needs its experimental foundation documented.

Who is it for? Practitioners wanting a quick point of comparison, but only after the authors supply the missing details. It does not deserve a serious referee in its current form. I'd desk reject with a clear invitation to resubmit once the dataset, split, and code are provided and the 1D CNN anomaly is explained.

Referee Report

4 major / 6 minor

Summary. The manuscript reports an empirical comparison of two families of ECG heartbeat classifiers on a dataset that is not named. The first family uses hand-crafted features (heart-rate variability statistics, mean/variance, RR-interval logarithms) concatenated with 70-sample beat windows, fed to SVM, Random Forest, AdaBoost, LSTM, BiLSTM, 1D CNN, and LightGBM, with SMOTE applied to the training set. The second family encodes each beat as a 32-by-32 GAF, MTF, or recurrence-plot image and classifies the concatenated three-channel image with 2D CNNs (best: a custom Inception-style network). The authors report LightGBM as best (accuracy 0.99, macro F1 0.94) and conclude that hand-crafted features capture beat variation better than image encodings.

Significance. The question addressed—hand-crafted beat/HRV features versus imaging-based deep learning for single-beat ECG classification—is practically relevant, and the paper has some good practices: SMOTE is applied only to the training partition, hyperparameters are tuned by grid search with 3-fold cross-validation, and the headline metric is macro F1 rather than raw accuracy. If the evaluation were performed on a patient-disjoint split of a standard public dataset with uncertainty estimates, the LightGBM result would be a useful data point. As written, however, the manuscript does not identify the dataset or the split rule, and the comparison between approaches is not controlled; the central claim is therefore not verifiable from the paper. The reader's stress-test concern about beat-level leakage lands and is the main reason the manuscript needs major revision.

major comments (4)

[§II-A, §II-B] The dataset is never identified and the train/test split is never described. The text states only that 'we had the dataset that contains separated beats and their labels' and that SMOTE was applied 'only to the training data, while the test data remained unchanged.' It does not state whether the split is by patient, by record, or randomly by beat. Since beats from the same patient share morphology, electrode placement, and RR-interval dynamics, a random beat-level split allows the classifier to memorize patient identity. This is load-bearing because the headline comparison in Table I (LightGBM 0.99 accuracy/0.94 F1 vs image-based CNN 0.85 F1) would then reflect within-patient discrimination rather than generalization. Please name the dataset, specify the split rule, and report results on a patient-disjoint split (or both split regimes).
[§II-B, §II-C, Table I] The two approaches are not evaluated under the same class-imbalance protocol. SMOTE is introduced in Section II-B for the hand-crafted-feature pipeline, with target counts of 100k V/S and 300k N beats, but Section II-C says only that the image pipeline uses 'the same dataset ... with the exception that no hand crafted feature was used.' If the image CNN was trained on the original imbalanced beats while LightGBM was trained on SMOTE-rebalanced data, the lower macro F1 of the image approach could be caused by the imbalance rather than by the image encoding. Please state explicitly whether SMOTE (or another rebalancing procedure) was applied to the image training set, and if not, re-run the comparison with matched protocols.
[Table I, §III] No uncertainty quantification is provided. The reported values are macro averages, but the paper gives no test-set size, per-class support, per-class precision/recall, confidence intervals, or multiple-seed repetitions. The differences among LightGBM (0.94), Random Forest (0.93), and BiLSTM (0.93) are one point in macro F1 and could easily be within noise. Add the number of test beats and patients per class and report confidence intervals or repeated-seed ranges so that the 'outperforming' and 'significantly lower' statements in the Abstract and Section IV are supported.
[Table I, 1D CNN row] The 1D CNN row reports accuracy 0.92 with macro precision 0.43, macro recall 0.39, and macro F1 0.39. Such a combination indicates that the model is accurate mostly because it assigns the majority class, while minority classes have near-chance or worse discriminating performance. Without per-class metrics or a confusion matrix, the reader cannot interpret this row, and Section IV uses these scores to discard models. Please provide the confusion matrix or a per-class breakdown for each model.

minor comments (6)

[§II-B, §V] The SMOTE counts are described inconsistently: Section II-B says 100k V/S and 300k N beats, while Section V says 'undersampling the signal 300000 times and then oversampling it 100000 times.' Please align the wording.
[§II-C, Eq. (1)] The formula is typeset incorrectly (missing brace/bracket, and the variable is given as xi rather than \tilde{x}_i), and the constant T is not defined. Please fix the equation and define all symbols.
[Section IV] The text says 'we immediately discarded the SVM, feed-forward neural network, and AdaBoost models,' but no feed-forward neural network appears in Table I or in the methods section. Please correct the list or clarify which model is meant.
[References] Reference [5] (Moreno-Camacho et al., supply-chain sustainability metrics) appears unrelated to ECG classification and is not cited in the text; please remove it or replace it with the intended citation.
[Throughout] Several typographical issues remain: 'the the unbalanced dataset' in Section II-B, '03e-4' for the learning rate in Section II-C, and the author name 'V o' in the byline. Please proofread.
[Reproducibility] The manuscript does not include a code or data availability statement, which would help reproducibility; consider adding one if permissible.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the paper is an empirical benchmark, not a derivation; all results are observed performances on a dataset.

full rationale

This paper reports a standard empirical comparison of machine-learning and deep-learning classifiers for ECG heartbeat classification. It does not claim to derive any quantity from first principles, and no equation in the paper defines an output in terms of a fitted constant or a prior result by the same author. The hand-crafted features (HRV, mean, variance, RR intervals) are extracted directly from the beats and their labels, not from the model predictions. Hyperparameters are tuned with GridSearch and 3-fold cross-validation, and the reported metrics are measured on a test set. Reference [8], used for adding two RR-interval features, is an external prior work by other authors, and no load-bearing argument rests on a self-citation. Possible concerns about the undisclosed train/test split or beat-level leakage are validity and reproducibility issues, not circularity, because they do not make any reported result equivalent to its input by construction. Therefore, the paper is self-contained as an empirical study, and no circular step is identified.

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

The empirical comparison relies on standard supervised learning assumptions: beat labels and R-peak positions are accurate, the train/test split is unbiased, and the fixed beat window plus hand-crafted features preserve class information. The tuned quantities are limited to model hyperparameters and SMOTE target counts; no new theoretical entities are introduced.

free parameters (3)

LightGBM hyperparameters = learning_rate=0.5, max_depth=10, n_estimators=1000, min_data_in_leaf=10, alpha=0.5, lambda=0.7327
Tuned via GridSearch with 3-fold cross-validation in Section II-B and listed in the Conclusion; the central claim depends on this configuration.
SMOTE target class counts = 300k normal, 100k ventricular, 100k supraventricular
Chosen by hand in Section II-B to balance training data; the paper states the ratio also improved scores, so it is a fitted design choice.
Beat window size = 70 samples (35 before and after R peak)
Chosen in Section II-A; affects the feature and image content for every model, but is not tuned.

assumptions (3)

domain assumption R-peak positions and beat annotations in the dataset are accurate.
Section II-A uses them to segment beats and assign labels; if wrong, all features and labels are corrupted.
domain assumption The train/test split is independent and representative.
The split is never specified; SMOTE is applied only to training data. Patient-level versus beat-level splitting materially changes generalization.
domain assumption The fixed beat window and image transforms preserve class-discriminative information.
Used in Section II-C to build images; if the 70-sample window or GAF, MTF, or recurrence plot transforms discard morphology, the CNN comparison is unreliable.

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Cite this review

Pith. "Pith review of Heart Rate Classification in ECG Signals Using Machine Learning and Deep Learning." pith.science (2026). https://pith.science/paper/OBNGUFOW

@misc{pith2026250606349,
  author       = {Pith},
  title        = {Pith review of: Heart Rate Classification in ECG Signals Using Machine Learning and Deep Learning},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/OBNGUFOW}},
  note         = {Machine review of arXiv:2506.06349}
}

read the original abstract

This study addresses the classification of heartbeats from ECG signals through two distinct approaches: traditional machine learning utilizing hand-crafted features and deep learning via transformed images of ECG beats. The dataset underwent preprocessing steps, including downsampling, filtering, and normalization, to ensure consistency and relevance for subsequent analysis. In the first approach, features such as heart rate variability (HRV), mean, variance, and RR intervals were extracted to train various classifiers, including SVM, Random Forest, AdaBoost, LSTM, Bi-directional LSTM, and LightGBM. The second approach involved transforming ECG signals into images using Gramian Angular Field (GAF), Markov Transition Field (MTF), and Recurrence Plots (RP), with these images subsequently classified using CNN architectures like VGG and Inception. Experimental results demonstrate that the LightGBM model achieved the highest performance, with an accuracy of 99% and an F1 score of 0.94, outperforming the image-based CNN approach (F1 score of 0.85). Models such as SVM and AdaBoost yielded significantly lower scores, indicating limited suitability for this task. The findings underscore the superior ability of hand-crafted features to capture temporal and morphological variations in ECG signals compared to image-based representations of individual beats. Future investigations may benefit from incorporating multi-lead ECG signals and temporal dependencies across successive beats to enhance classification accuracy further.

Figures

Figures reproduced from arXiv: 2506.06349 by the authors.

**Figure 2.** Models with LSTM, Bi-directional LSTM and 1D Convolutional [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗

**Figure 4.** Architecture III. RESULTS Below, we report a selected number of metrics that we observed in training the models.The metrics were the basis for the final model choice. The reported values are the macro averages of the metrics per label. Model Precision Recall Accuracy F1 score Images approach 0.83 0.87 0.97 0.85 Random Forest 0.93 0.93 0.99 0.93 SVM 0.59 0.58 0.58 0.58 LightGBM 0.94 0.94 0.99 0.94 LSTM 0.86 0.95 0.98… view at source ↗

Figures from the paper (1 more)

**Figure 3.** Figure 3: ECG image transformation Every transformation has its own peculiarities and it focuses on different parts of the signal. The idea of using all of them comes from the fact that multi-modal architectures often performs better with respect to using a single modality. In o…

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

10 extracted references · 8 canonical work pages

[1]

Arrhythmia classification on ECG using Deep Learning,

A. Rajkumar, M. Ganesan and R. Lavanya, "Arrhythmia classification on ECG using Deep Learning," 2019 5th International Conference on Advanced Computing and Communication Systems (ICACCS), 2019, pp. 365-369, doi: 10.1109/ICACCS.2019.8728362

work page arXiv 2019
[2]

Classification of arrhythmia by using deep learning with 2-D ECG signal image representation

Ullah, Amin, et al. "Classification of arrhythmia by using deep learning with 2-D ECG signal image representation." Remote Sensing 12.10 (2020): 1685

work page 2020
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A Patient-Adapting Heartbeat Classifier Using ECG Morphology and Heartbeat Interval Features,

P. de Chazal and R. B. Reilly, "A Patient-Adapting Heartbeat Classifier Using ECG Morphology and Heartbeat Interval Features," in IEEE Transactions on Biomedical Engineering, vol. 53, no. 12, pp. 2535-2543, Dec. 2006, doi: 10.1109/TBME.2006.883802

work page arXiv 2006
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U. Rajendra Acharya, Shu Lih Oh, Yuki Hagiwara, Jen Hong Tan, Muhammad Adam, Arkadiusz Gertych, Ru San Tan, A deep convolutional neural network model to classify heartbeats, Computers in Biology and Medicine, Volume 89, 2017, Pages 389-396, ISSN 0010-4825, https://doi.org/10.1016/j.compbiomed.2017.08.022

work page doi:10.1016/j.compbiomed.2017.08.022 2017
[5]

Moreno-Camacho, Jairo R

Carlos A. Moreno-Camacho, Jairo R. Montoya-Torres, Anicia Jaegler, Natacha Gondran, Sustainability metrics for real case applications of the supply chain network design problem: A systematic literature review, Journal of Cleaner Production, Volume 231, 2019, Pages 600-618, ISSN 0959-6526, https://doi.org/10.1016/j.jclepro.2019.05.278

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ECG heartbeat classification using multimodal fusion

Ahmad, Zeeshan, et al. "ECG heartbeat classification using multimodal fusion." IEEE Access 9 (2021): 100615-100662

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An Automated ECG Beat Classification System Using Deep Neural Networks with an Unsurpassed Feature Extraction Technique

Nurmaini, S.; Putra, R.; Caesareindra, W.; Dewi, T.; Naufal Rahmatullah, M.; Darmawahyuni, A.; Bhayyu, V.; Firdaus, F. An Automated ECG Beat Classification System Using Deep Neural Networks with an Unsurpassed Feature Extraction Technique. Appl. Sci. 2019, 9, 2921. https://doi.org/10.3390/app9142921

work page doi:10.3390/app9142921 2019
[8]

A Fast and Accurate Method for ECG-Based Heartbeat Classification Using Neural Networks

Alfaras, Miguel and Soriano, Miguel and Ortín, Silvia (2019). A Fast and Accurate Method for ECG-Based Heartbeat Classification Using Neural Networks. Frontiers in Physics. 7. 10.3389/fphy.2019.00103

arXiv 2019

Show all 10 references

[9]

Imaging Time-Series to Improve Clas- sification and Imputation(2015).arXiv.1506.00327

Zhiguang Wang and Tim Oates. Imaging Time-Series to Improve Clas- sification and Imputation(2015).arXiv.1506.00327

2015 arXiv
[10]

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Pith tools

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

[1] [1]

Arrhythmia classification on ECG using Deep Learning,

A. Rajkumar, M. Ganesan and R. Lavanya, "Arrhythmia classification on ECG using Deep Learning," 2019 5th International Conference on Advanced Computing and Communication Systems (ICACCS), 2019, pp. 365-369, doi: 10.1109/ICACCS.2019.8728362

work page arXiv 2019

[2] [2]

Classification of arrhythmia by using deep learning with 2-D ECG signal image representation

Ullah, Amin, et al. "Classification of arrhythmia by using deep learning with 2-D ECG signal image representation." Remote Sensing 12.10 (2020): 1685

work page 2020

[3] [3]

A Patient-Adapting Heartbeat Classifier Using ECG Morphology and Heartbeat Interval Features,

P. de Chazal and R. B. Reilly, "A Patient-Adapting Heartbeat Classifier Using ECG Morphology and Heartbeat Interval Features," in IEEE Transactions on Biomedical Engineering, vol. 53, no. 12, pp. 2535-2543, Dec. 2006, doi: 10.1109/TBME.2006.883802

work page arXiv 2006

[4] [4]

U. Rajendra Acharya, Shu Lih Oh, Yuki Hagiwara, Jen Hong Tan, Muhammad Adam, Arkadiusz Gertych, Ru San Tan, A deep convolutional neural network model to classify heartbeats, Computers in Biology and Medicine, Volume 89, 2017, Pages 389-396, ISSN 0010-4825, https://doi.org/10.1016/j.compbiomed.2017.08.022

work page doi:10.1016/j.compbiomed.2017.08.022 2017

[5] [5]

Moreno-Camacho, Jairo R

Carlos A. Moreno-Camacho, Jairo R. Montoya-Torres, Anicia Jaegler, Natacha Gondran, Sustainability metrics for real case applications of the supply chain network design problem: A systematic literature review, Journal of Cleaner Production, Volume 231, 2019, Pages 600-618, ISSN 0959-6526, https://doi.org/10.1016/j.jclepro.2019.05.278

work page doi:10.1016/j.jclepro.2019.05.278 2019

[6] [6]

ECG heartbeat classification using multimodal fusion

Ahmad, Zeeshan, et al. "ECG heartbeat classification using multimodal fusion." IEEE Access 9 (2021): 100615-100662

work page 2021

[7] [7]

An Automated ECG Beat Classification System Using Deep Neural Networks with an Unsurpassed Feature Extraction Technique

Nurmaini, S.; Putra, R.; Caesareindra, W.; Dewi, T.; Naufal Rahmatullah, M.; Darmawahyuni, A.; Bhayyu, V.; Firdaus, F. An Automated ECG Beat Classification System Using Deep Neural Networks with an Unsurpassed Feature Extraction Technique. Appl. Sci. 2019, 9, 2921. https://doi.org/10.3390/app9142921

work page doi:10.3390/app9142921 2019

[8] [8]

A Fast and Accurate Method for ECG-Based Heartbeat Classification Using Neural Networks

Alfaras, Miguel and Soriano, Miguel and Ortín, Silvia (2019). A Fast and Accurate Method for ECG-Based Heartbeat Classification Using Neural Networks. Frontiers in Physics. 7. 10.3389/fphy.2019.00103

arXiv 2019