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arxiv: 2605.23516 · v1 · pith:GRT23A2Inew · submitted 2026-05-22 · 📡 eess.SP

Comprehensive Dataset and Signal Processing Framework for Phonocardiogram-Based Heart Rate and Blood Pressure Estimation

Abdul Ahad Mamun , Utsab Saha , Md Hasibul Hasan , Shahed Ahmed , MD Jahin Alam This is my paper

Pith reviewed 2026-05-25 03:27 UTC · model grok-4.3

classification 📡 eess.SP
keywords phonocardiogramheart rate estimationblood pressure estimationpeak detectionmultiple regressionnon-invasive monitoringPCG signalscardiovascular monitoring
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The pith

Phonocardiogram signals alone can estimate heart rate via peak detection and blood pressure via multiple regression, as shown on a 15-participant dataset.

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

The paper sets out to show that a single phonocardiogram recording contains enough information to compute both heart rate and blood pressure without any other sensors. It collects data from fifteen people, applies three different peak-finding routines to the signals, and reports correlations above 0.95 with root-mean-square errors below 2.5 beats per minute. The same signals are then fed into a multiple-regression model that produces systolic and diastolic pressure estimates whose standard deviations across the group are 2.10 mmHg and 3.20 mmHg. If these numbers hold outside the original fifteen subjects, the work would remove the need for separate cuffs or ECG leads in routine cardiovascular checks. This matters for anyone who needs frequent monitoring but currently faces cost or convenience barriers.

Core claim

Using only phonocardiogram recordings collected from fifteen participants, the authors demonstrate that heart rate can be recovered by applying Hilbert Transform, Shannon Entropy, or WES peak detection, yielding Pearson correlations of 0.965, 0.973, and 0.955 and RMSE values of 2.467, 1.688, and 1.992 bpm respectively; the same recordings are then used to fit a semi-empirical multiple-regression model that estimates systolic blood pressure to a standard deviation of 2.10 mmHg (correlation 0.89) and diastolic blood pressure to 3.20 mmHg (correlation 0.70).

What carries the argument

The PhonoTrack pipeline that first locates heart-sound peaks with one of three detection algorithms and then feeds extracted features into a multiple-regression model for blood-pressure prediction.

If this is right

  • Heart rate can be obtained from phonocardiogram signals alone at clinical accuracy using any of the three tested peak detectors.
  • Systolic and diastolic pressures can be estimated from the same signals with standard deviations of 2.10 mmHg and 3.20 mmHg across the study group.
  • A single low-cost sensor suffices for simultaneous heart-rate and blood-pressure tracking, removing the need for multimodal hardware.
  • The reported error levels support development of portable devices intended for continuous or at-home cardiovascular monitoring.
  • The fifteen-person dataset supplies a public benchmark for future phonocardiogram-based estimation algorithms.

Where Pith is reading between the lines

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

  • Releasing the full dataset would let other groups test whether the regression coefficients transfer to different age groups or recording hardware.
  • Embedding the peak-detection and regression steps in a smartphone app could turn ordinary microphones into basic vital-sign monitors.
  • The same feature set might be examined for additional cardiac markers such as murmur detection or arrhythmia classification.
  • Long-term recordings from the same subjects would reveal whether day-to-day blood-pressure drift remains within the reported error bounds.

Load-bearing premise

The multiple regression model fitted to features from the fifteen-participant dataset will produce reliable blood-pressure estimates for new subjects and real-world recording conditions.

What would settle it

Recording phonocardiograms from a fresh group of fifteen or more participants, applying the published regression coefficients, and obtaining standard deviations above 5 mmHg or correlations below 0.5 for either systolic or diastolic pressure.

Figures

Figures reproduced from arXiv: 2605.23516 by Abdul Ahad Mamun, Md Hasibul Hasan, MD Jahin Alam, Shahed Ahmed, Utsab Saha.

Figure 1
Figure 1. Figure 1: Overview of the Data Acquisition System: (i) Diaphragm placement for phonocar [PITH_FULL_IMAGE:figures/full_fig_p007_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Flowchart of the proposed signal processing-based algorithms for estimating heart [PITH_FULL_IMAGE:figures/full_fig_p011_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Energy spectrum (ES) of a 2-sec PCG frame showing different features for blood [PITH_FULL_IMAGE:figures/full_fig_p014_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: (a) Graphical visualization of raw phonocardiogram (PCG) signal collected using [PITH_FULL_IMAGE:figures/full_fig_p017_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: (a) Normalized energy spectrum (ES) and (b) continuous wavelet energy spectrum [PITH_FULL_IMAGE:figures/full_fig_p018_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Heart rate (HR) estimation from electrocardiogram (ECG) and phonocardiogram [PITH_FULL_IMAGE:figures/full_fig_p021_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Box plot for all subjects using three envelope detection methods in heart rate (HR) [PITH_FULL_IMAGE:figures/full_fig_p023_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Heart rate estimation performance with Pearson’s correlation plot for three en [PITH_FULL_IMAGE:figures/full_fig_p024_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Blood pressure (BP) estimation result, along with Pearson’s coefficient using [PITH_FULL_IMAGE:figures/full_fig_p025_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Bland–Altman plots for BP estimation. (a) SBP and (b) DBP agreement between [PITH_FULL_IMAGE:figures/full_fig_p026_10.png] view at source ↗
read the original abstract

Cardiovascular diseases (CVDs) represent significant global health challenges today, necessitating regular and reliable monitoring to enable early intervention. Phonocardiogram (PCG) signals present a promising non-invasive method for assessing cardiovascular health. While recent studies have focused on estimating heart rate (HR) from PCG signals and blood pressure (BP) through multimodal combinations with other physiological data, reliable and cost-effective systems that can predict both HR and BP using only PCG signals remain largely unexplored. In this study, we proposed and developed a lab-scale cost-effective Phonocardiogram Tracking (PhonoTrack) system that can measure both HR and BP using only the PCG signal. We also introduced a corresponding dataset collected from 15 participants to evaluate the effectiveness of the proposed system. HR was determined using several peak detection methods, such as Hilbert Transform (HT), Shannon Entropy (SE), and WES, achieving notable Pearson correlation coefficients of 0.965, 0.973, and 0.955, respectively. The corresponding root mean square errors (RMSEs) were 2.467 bpm, 1.688 bpm, and 1.992 bpm for HT, SE, and WES, respectively. Additionally, we developed an advanced semi-empirical model based on multiple regression techniques to estimate systolic blood pressure (SBP) and diastolic blood pressure (DBP). This model demonstrated standard deviations of 2.10 mmHg for SBP and 3.20 mmHg for DBP across all subjects, with Pearson correlation coefficients of 0.89 and 0.70, respectively. These findings pave the way for developing a non-invasive, low-cost, and portable PhonoTrack device, positioning it as a promising solution for continuous cardiovascular monitoring settings.

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

1 major / 1 minor

Summary. The manuscript presents the PhonoTrack system for estimating both heart rate (HR) and blood pressure (BP) from phonocardiogram (PCG) signals alone. It introduces a 15-participant dataset and evaluates three peak-detection methods (Hilbert Transform, Shannon Entropy, WES) for HR, reporting Pearson correlations of 0.965/0.973/0.955 and RMSEs of 2.467/1.688/1.992 bpm. For BP, a multiple-regression semi-empirical model is fitted to yield standard deviations of 2.10 mmHg (SBP) and 3.20 mmHg (DBP) with correlations 0.89 and 0.70 “across all subjects.”

Significance. A validated single-signal PCG method for joint HR and BP estimation would be useful for low-cost continuous monitoring. The HR component is benchmarked against an external reference and shows strong numerical agreement. The BP regression, however, supplies the novel modeling contribution; its reported metrics cannot be interpreted as evidence of generalization without out-of-sample validation.

major comments (1)
  1. [Abstract] Abstract (BP estimation paragraph): The reported SBP/DBP standard deviations and Pearson correlations are stated to be obtained “across all subjects” from a multiple regression model whose coefficients are free parameters fitted to the identical 15-subject dataset. No description of hold-out testing, subject-wise cross-validation, or external test set is supplied, so the figures are in-sample fit statistics whose out-of-sample behavior is unknown. This directly affects the central claim that the model produces reliable BP estimates.
minor comments (1)
  1. [Abstract] The abstract refers to an “advanced semi-empirical model” and “multiple regression techniques” but supplies neither the explicit regression equation nor the list of PCG-derived features used as predictors.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the detailed review and for identifying the need to clarify the validation procedure for the blood-pressure regression model. We address the single major comment below and will revise the manuscript to strengthen the presentation of results.

read point-by-point responses

  1. Referee: [Abstract] Abstract (BP estimation paragraph): The reported SBP/DBP standard deviations and Pearson correlations are stated to be obtained “across all subjects” from a multiple regression model whose coefficients are free parameters fitted to the identical 15-subject dataset. No description of hold-out testing, subject-wise cross-validation, or external test set is supplied, so the figures are in-sample fit statistics whose out-of-sample behavior is unknown. This directly affects the central claim that the model produces reliable BP estimates.

    Authors: We agree that the reported SBP and DBP statistics reflect an in-sample fit of the multiple-regression coefficients to the full 15-subject dataset. The manuscript does not currently describe any hold-out, cross-validation, or external testing procedure for the blood-pressure model. In the revised manuscript we will add a subject-wise leave-one-out cross-validation analysis for the semi-empirical BP regression, report the resulting out-of-sample Pearson correlations and standard deviations, and update both the abstract and the main text to reflect these metrics. This change will directly address the concern about generalization. revision: yes

Circularity Check

1 steps flagged

BP regression reports in-sample fit metrics on n=15 dataset with no hold-out or cross-validation described

specific steps
  1. fitted input called prediction [Abstract]
    "we developed an advanced semi-empirical model based on multiple regression techniques to estimate systolic blood pressure (SBP) and diastolic blood pressure (DBP). This model demonstrated standard deviations of 2.10 mmHg for SBP and 3.20 mmHg for DBP across all subjects, with Pearson correlation coefficients of 0.89 and 0.70, respectively."

    The regression coefficients are fitted directly to features extracted from the identical 15-participant dataset; the reported SD and correlation values are then computed on that same dataset ('across all subjects'), so the quoted performance metrics are in-sample fit statistics rather than independent predictions.

full rationale

HR peak-detection results (HT, SE, WES) are benchmarked against an external reference signal, yielding independent correlation and RMSE values. The novel BP contribution, however, is a multiple-regression model whose coefficients are estimated on the same 15-subject PCG dataset used to compute the reported SD and Pearson r values 'across all subjects.' No subject-wise partitioning, regularization, or external test set is described, so the quoted performance numbers reduce to in-sample fit statistics.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The claims rest on regression coefficients fitted to the 15-subject data and the domain assumption that PCG signals alone contain sufficient information for blood-pressure estimation; no independent evidence for the latter is supplied.

free parameters (1)
  • multiple regression coefficients for SBP and DBP
    Fitted to the collected dataset to produce the reported standard deviations and correlations
axioms (1)
  • domain assumption Phonocardiogram signals contain extractable features sufficient for accurate blood pressure estimation
    Invoked to justify development of the semi-empirical regression model

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discussion (0)

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