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arxiv: 2404.00470 · v1 · pith:3HP4FCJPnew · submitted 2024-03-30 · 💻 cs.SD · cs.LG· eess.AS

Classification of Short Segment Pediatric Heart Sounds Based on a Transformer-Based Convolutional Neural Network

Md Hassanuzzaman , Nurul Akhtar Hasan , Mohammad Abdullah Al Mamun , Khawza I Ahmed , Ahsan H Khandoker , Raqibul Mostafa This is my paper

Pith reviewed 2026-05-24 02:05 UTC · model grok-4.3

classification 💻 cs.SD cs.LGeess.AS
keywords pediatric heart soundscongenital heart diseasesignal durationtransformer CNNMFCC featuresPCG classificationsignal quality assessment
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The pith

Pediatric heart sounds require a minimum of 5 seconds for accurate classification by a transformer-based CNN at 93.69 percent accuracy.

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

This paper examines the shortest recording length that still allows reliable automatic classification of pediatric heart sounds for detecting congenital heart diseases. It applies quality filtering with RMSSD and ZCR indicators at a 0.4 threshold, extracts MFCC features, and classifies them using a transformer-based residual one-dimensional convolutional neural network. The work concludes that 5-second segments strike the right balance, as shorter ones lack enough information and longer ones add noise. A sympathetic reader would care because shorter usable recordings could support faster screening for heart defects in children without needing long clinic visits.

Core claim

The study shows that a minimum signal length of 5s is required for effective heart sound classification, with the best accuracy of 93.69 percent obtained for the 5s signal to distinguish the heart sound. It also finds that 0.4 is the ideal threshold for the RMSSD and ZCR quality indicators to select suitable signals, while 3s heart sounds lack enough information and 15s signals may contain more noise. MFCC features serve as input to the transformer-based residual one-dimensional convolutional neural network.

What carries the argument

Transformer-based residual one-dimensional convolutional neural network that classifies MFCC features extracted from heart sound segments filtered by RMSSD and ZCR quality checks.

If this is right

  • A 3-second heart sound does not have enough information to categorize heart sounds accurately.
  • A 15-second heart sound may contain more noise that hurts classification performance.
  • The 0.4 threshold on RMSSD and ZCR selects suitable signals for the model.
  • The transformer-based CNN reaches 93.69 percent accuracy when given 5-second signals.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Shorter recording times could make portable heart sound screening more practical for infants and young children.
  • The same length optimization approach might guide data collection for classifying other types of physiological audio signals.
  • Signal quality filtering before deep learning appears essential for consistent performance on variable medical recordings.
  • Repeating the experiment on adult heart sounds or additional CHD categories could test whether 5 seconds remains the minimum across groups.

Load-bearing premise

The chosen dataset together with RMSSD and ZCR at a 0.4 threshold produces representative pediatric heart sound recordings that generalize beyond the training cases.

What would settle it

A new independent set of pediatric heart sound recordings where 3-second segments yield higher classification accuracy than 5-second segments, or where 5-second accuracy drops well below 90 percent, would falsify the minimum length claim.

Figures

Figures reproduced from arXiv: 2404.00470 by Ahsan H Khandoker, Khawza I Ahmed, Md Hassanuzzaman, Mohammad Abdullah Al Mamun, Nurul Akhtar Hasan, Raqibul Mostafa.

Figure 1
Figure 1. Figure 1: The auscultation areas from where the PCG signal is acquired (a) schematic diagram of four auscultation areas: mitral valve (MV), tricuspid valve (TV), pulmonary valve (PV), and aortic valve (AV) and (b) Collect PCG signal using EKO device. A digital stethoscope is employed to verify the minimum signal duration required for accurate classification and diagnosis by implementing a proposed deep learning meth… view at source ↗
Figure 2
Figure 2. Figure 2: The signals of CHD of four positions: mitral valve (MV), tricuspid valve (TV), pulmonary valve (PV), and aortic valve (AV) (from top to bottom) [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: The signals of non-CHD of four positions mitral valve (MV), tricuspid valve (TV), pulmonary valve (PV), and aortic valve (AV) (from top to bottom) [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: The system architecture of the proposed method to verify the heart sound classification accuracy by varying signal duration. It includes signal split, quality assessment, preprocessing, feature extraction, and training of the proposed model. III. METHODS The architecture of the proposed CHD auscultation algorithm is shown in [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Discrete wavelet transformations (DWTs) are used in the picture to decompose a signal into its detailed components. The panels below display the decomposed components, while the top panel shows the original signal. (Left) an appropriate signal with an RMSSD of 0.4 and a ZCR of 0.3; (Right) an unsuitable signal with an RMSSD of 0.2 and a ZCR of 0.2. Spike Removal: The Schmidt algorithm (37) is used to locat… view at source ↗
Figure 6
Figure 6. Figure 6: It represents all extracted MFCC feature curves of 5s heart sound: (a) MFCC features of non-CHD heart sound, (b) ∆MFCC features of non￾CHD heart sound, (c) ∆ ଶMFCC features of non-CHD heart sound, (d) MFCC features of CHD heart sound, (e) ∆MFCC features of CHD heart sound, and (f) ∆ ଶMFCC features of CHD heart sound. C. CHD Detection Model In order to improve the performance in classifying the heart sound … view at source ↗
Figure 7
Figure 7. Figure 7: The system architecture of transformer-based residual 1D CNN deep learning model. 1) Block 1: Block -1 process uses a Conv1D, positional encoder (PE), transformer, BN, ReLU, dropout, and max pooling layer. It also has a residual connection with the Conv1D layer and BN layer. The parameter of the Conv1D layer in block-1 is the same as before used Conv1D. a. Positional Encoder: This experiment's positional e… view at source ↗
Figure 8
Figure 8. Figure 8: Based on the RMSSD and ZCR signal quality indicator criteria, this chart shows the accuracy obtained in recognising suitable signals for 15 seconds. Remarkably, the 15-second signal exhibits the maximum accuracy of 93.67% when the RMSSD threshold is set to 0.4 and the ZCR threshold is set to 0.3. The efficacy of these thresholds in signal selection is demonstrated [PITH_FULL_IMAGE:figures/full_fig_p013_8.png] view at source ↗
Figure 10
Figure 10. Figure 10: The model's performance decreases if ZCR is [PITH_FULL_IMAGE:figures/full_fig_p014_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Analyzing network activations layers to determine how well the model identifies CHD. The figure depicts the internal activations of transformer layers that are grouped using the t-distributed stochastic neighbor embedding (t-SNE) method. This allows for a visual evaluation of the model's ability to discern the existence of CHD. Specifically, (a) through (e) correspond to the activations of the 1st to 5th … view at source ↗
read the original abstract

Congenital anomalies arising as a result of a defect in the structure of the heart and great vessels are known as congenital heart diseases or CHDs. A PCG can provide essential details about the mechanical conduction system of the heart and point out specific patterns linked to different kinds of CHD. This study aims to investigate the minimum signal duration required for the automatic classification of heart sounds. This study also investigated the optimum signal quality assessment indicator (Root Mean Square of Successive Differences) RMSSD and (Zero Crossings Rate) ZCR value. Mel-frequency cepstral coefficients (MFCCs) based feature is used as an input to build a Transformer-Based residual one-dimensional convolutional neural network, which is then used for classifying the heart sound. The study showed that 0.4 is the ideal threshold for getting suitable signals for the RMSSD and ZCR indicators. Moreover, a minimum signal length of 5s is required for effective heart sound classification. It also shows that a shorter signal (3 s heart sound) does not have enough information to categorize heart sounds accurately, and the longer signal (15 s heart sound) may contain more noise. The best accuracy, 93.69%, is obtained for the 5s signal to distinguish the heart sound.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The manuscript investigates the minimum signal duration required for automatic classification of pediatric heart sounds (PCG) to detect congenital heart diseases. It employs MFCC features fed into a Transformer-based residual 1D CNN, reports that 0.4 is the optimal threshold for RMSSD and ZCR quality indicators, and concludes that a minimum of 5 s is required for effective classification (achieving 93.69% accuracy), while 3 s lacks sufficient information and 15 s introduces more noise.

Significance. If the experimental controls and generalizability hold, the result on minimum recording length could inform practical guidelines for efficient pediatric CHD screening devices, reducing patient burden while preserving diagnostic utility. The hybrid transformer-CNN architecture on 1D signals is a contemporary choice that, with proper benchmarking, might advance signal-processing approaches in this domain.

major comments (3)
  1. [Abstract] Abstract: The reported peak accuracy of 93.69% for the 5 s segments is presented without any mention of dataset size (number of recordings or subjects), cross-validation procedure, baseline comparisons, confidence intervals, or ablation results. These omissions render the central claim—that 5 s is the minimum effective length—impossible to evaluate for statistical reliability or robustness against the 3 s and 15 s conditions.
  2. [Abstract] Abstract: The assertion that 0.4 constitutes the ideal RMSSD/ZCR threshold is stated without describing how the value was selected, whether it was tuned on held-out data, or if it was validated independently of the accuracy numbers. If the threshold was chosen after inspecting performance on the same segments used for the length comparison, the length-dependent conclusions are at risk of post-hoc selection bias.
  3. [Abstract] Abstract: No information is supplied on segment counts per length, patient-wise versus segment-wise splitting, or controls for class imbalance and recording quality distribution. Without these, it is unclear whether the reported superiority of 5 s over 3 s and 15 s arises from genuine information content or from unequal sample sizes or leakage artifacts.
minor comments (2)
  1. [Abstract] Abstract: The sentence 'the best accuracy, 93.69%, is obtained for the 5s signal to distinguish the heart sound' is ambiguous; it should explicitly state the classification task (e.g., normal vs. pathological or specific CHD subtypes).
  2. [Abstract] Abstract: Minor grammatical and phrasing issues (e.g., 'This study also investigated the optimum signal quality assessment indicator (Root Mean Square of Successive Differences) RMSSD and (Zero Crossings Rate) ZCR value') reduce readability and should be revised.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their constructive comments highlighting areas where the abstract could be strengthened for better evaluation of our claims. We agree that adding key experimental details to the abstract will improve clarity and have revised it accordingly. Below we respond point by point to the major comments.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The reported peak accuracy of 93.69% for the 5 s segments is presented without any mention of dataset size (number of recordings or subjects), cross-validation procedure, baseline comparisons, confidence intervals, or ablation results. These omissions render the central claim—that 5 s is the minimum effective length—impossible to evaluate for statistical reliability or robustness against the 3 s and 15 s conditions.

    Authors: We agree the abstract is too concise on experimental details. The full manuscript reports the dataset (recordings from X subjects), uses 5-fold cross-validation, compares against SVM and ResNet baselines, provides confidence intervals in results tables, and includes architecture ablations. We have revised the abstract to briefly note the dataset size, cross-validation procedure, and that 5 s outperforms the other lengths with statistical support from the full experiments. revision: yes

  2. Referee: [Abstract] Abstract: The assertion that 0.4 constitutes the ideal RMSSD/ZCR threshold is stated without describing how the value was selected, whether it was tuned on held-out data, or if it was validated independently of the accuracy numbers. If the threshold was chosen after inspecting performance on the same segments used for the length comparison, the length-dependent conclusions are at risk of post-hoc selection bias.

    Authors: The 0.4 threshold was identified by evaluating RMSSD and ZCR across a grid of values on a held-out validation partition (distinct from the test segments used for length experiments) to maximize retained signal quality while preserving classification utility. This is described in the methods. We have updated the abstract to state that the threshold was tuned on held-out data prior to length comparisons, removing any risk of post-hoc bias in the reported conclusions. revision: yes

  3. Referee: [Abstract] Abstract: No information is supplied on segment counts per length, patient-wise versus segment-wise splitting, or controls for class imbalance and recording quality distribution. Without these, it is unclear whether the reported superiority of 5 s over 3 s and 15 s arises from genuine information content or from unequal sample sizes or leakage artifacts.

    Authors: The manuscript uses patient-wise splitting to prevent leakage, reports per-length segment counts in the experimental setup, applies weighted loss for class imbalance, and enforces uniform RMSSD/ZCR quality filtering across lengths. These controls ensure fair comparison. We have added a concise statement to the abstract summarizing the patient-wise split and quality controls to address this concern directly. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical ML results on signal length and quality thresholds

full rationale

The paper reports an empirical investigation: MFCC features fed to a Transformer-CNN yield 93.69% accuracy on 5 s segments after applying an RMSSD/ZCR quality filter at 0.4. No equations, uniqueness theorems, or self-citations are invoked to derive the accuracy or the 5 s minimum; both are direct training outcomes on the chosen dataset. The length and threshold choices are presented as experimental findings rather than predictions forced by prior fits or definitions. The result is therefore self-contained against external benchmarks and receives the default non-circularity score.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review supplies no explicit free parameters, axioms, or invented entities; the model implicitly contains many unfixed hyperparameters typical of CNN training.

pith-pipeline@v0.9.0 · 5787 in / 1129 out tokens · 49957 ms · 2026-05-24T02:05:59.445100+00:00 · methodology

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Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Automated detection of pediatric congenital heart disease from phonocardiograms using deep and handcrafted feature fusion

    cs.LG 2026-04 unverdicted novelty 4.0

    A deep and handcrafted feature fusion model detects pediatric congenital heart disease from phonocardiograms with 92% accuracy, 91% sensitivity, and 96% AUROC on a patient-wise held-out test set from 751 subjects.

Reference graph

Works this paper leans on

45 extracted references · 45 canonical work pages · cited by 1 Pith paper

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    Discrete Fourier Transform (DFT): DFT is used to convert the time-domain heart sound signal, 𝑥(𝑛), into a frequency domain signal to obtain a spectrum 𝑋(𝑘) following the equation (8): 𝑋(𝑘)= ෍ 𝑥(𝑛) ேିଵ ௡ୀ଴ ∙ 𝑒ି௝ଶగ ே ௞௡ (8) where 𝑋(𝑘) is the 𝑘th frequency component of the DFT, 𝑥(𝑛) is the 𝑛-th input data point, 𝑁 is the total number of data points, 𝑗 is the...

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    Power spectrum calculation: By utilizing the signal spectrum 𝑋(𝑘) as the square of its modulus, one can obtain the power spectrum 𝑆(𝑘) using the subsequent equation (9): 𝑆(𝑘) = 1 𝑁 |𝑋(𝑘)|ଶ (9)

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    Log Transformation: The logarithm energy spectrum Smel(m) at each frame is then obtained by applying a logarithmic operation, shown in equation (12), to simulate human loudness perception. 𝑆௠௘௟(𝑚) =𝑙𝑛 ቌ෍ 𝑆(𝑘) ேିଵ ௞ୀ଴ ∙𝐻௠ (𝑘)ቍ , 0 ≤𝑚 ≤ 𝑀 (12) where 𝑆(𝑘) is the power spectrum and 𝐻௠ (𝑘) is the filter bank, and 𝑀 is the number of filter banks

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    𝑀𝐹𝐶𝐶௜ = ෍ 𝑆௠௘௟ ேିଵ ௜ୀ଴ (𝑚) ∙𝑐𝑜𝑠cosቆ𝜋𝑛(𝑚− 0.5) 𝑀 ቇ, 𝑛= 1,2, … … … ,𝐿 (13) where L is the order of the MFCC coefficient, and M denotes the number of filter banks

    Discrete Cosine Transform (DCT): To decorrelate the mel-frequency cepstral coefficients 𝑀𝐹𝐶𝐶௜, the above logarithmic spectrum is subjected to the DCT. 𝑀𝐹𝐶𝐶௜ = ෍ 𝑆௠௘௟ ேିଵ ௜ୀ଴ (𝑚) ∙𝑐𝑜𝑠cosቆ𝜋𝑛(𝑚− 0.5) 𝑀 ቇ, 𝑛= 1,2, … … … ,𝐿 (13) where L is the order of the MFCC coefficient, and M denotes the number of filter banks

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    Input: Each signal has extracted MFCC characteristics utilized as an input. In the experiment, the features' sizes are 39 X 155 for the 15-second signal, 39 X 51 for the 5-second signal, and 39 X 30 for the 3-second signal

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    These layers establish the foundation for additional analysis by capturing close links within the sequence

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    Instead, every layer in block -2 has parameters identical to those in block -1

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    The decoder transforms the encoded representation of the input data into an output-helping format

    Decoder: The global average pooling layer, dropout, fully connected (FC) layer, and softmax layer make up the decoder. The decoder transforms the encoded representation of the input data into an output-helping format. The decoder processes the retrieved features obtained from the last layer. A global average pooling layer pools the temporal sequence and o...

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    However, around 16% of signals are considered suitable at those values, which is ineffective

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