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A Dynamic Parametric Simulator for Fetal Heart Sounds

Yingtong Zhou, Yiang Zhou, Zhengxian Qu, Kang Liu, Ting Tan

TL;DR

Abdominal fetal phonocardiography faces challenges from low SNR and maternal interference, hindering benchmarking. The authors introduce a reproducible dynamic parametric simulator that decomposes signals into fetal S1/S2 events, maternal interference, and a cascaded abdominal transmission model, with cycle-level parameters calibrated from real recordings. Key contributions include data-driven per-cycle parameter sampling, an explicit transmission mechanism, configurable noise, and open-source Python and web tools enabling controlled, reproducible benchmarking under varied conditions. The approach demonstrates realism by matching envelope-based temporal structure and frequency-domain content, supporting robust development of fPCG processing methods and exploration of beat-to-beat variability and abnormal scenarios.

Abstract

Research on fetal phonocardiogram (fPCG) is challenged by the limited number of abdominal recordings, substantial maternal interference, and marked transmissioninduced signal attenuation that complicate reproducible benchmarking. We present a reproducible dynamic parametric simulator that generates long abdominal fPCG sequences by combining cycle-level fetal S1/S2 event synthesis with a convolutional transmission module and configurable interference and background noise. Model parameters are calibrated cyclewise from real abdominal recordings to capture beat-to-beat variability and to define data-driven admissible ranges for controllable synthesis. The generated signals are validated against real recordings in terms of envelope-based temporal structure and frequency-domain characteristics. The simulator is released as open software to support rapid, reproducible evaluation of fPCG processing methods under controlled acquisition conditions.

A Dynamic Parametric Simulator for Fetal Heart Sounds

TL;DR

Abdominal fetal phonocardiography faces challenges from low SNR and maternal interference, hindering benchmarking. The authors introduce a reproducible dynamic parametric simulator that decomposes signals into fetal S1/S2 events, maternal interference, and a cascaded abdominal transmission model, with cycle-level parameters calibrated from real recordings. Key contributions include data-driven per-cycle parameter sampling, an explicit transmission mechanism, configurable noise, and open-source Python and web tools enabling controlled, reproducible benchmarking under varied conditions. The approach demonstrates realism by matching envelope-based temporal structure and frequency-domain content, supporting robust development of fPCG processing methods and exploration of beat-to-beat variability and abnormal scenarios.

Abstract

Research on fetal phonocardiogram (fPCG) is challenged by the limited number of abdominal recordings, substantial maternal interference, and marked transmissioninduced signal attenuation that complicate reproducible benchmarking. We present a reproducible dynamic parametric simulator that generates long abdominal fPCG sequences by combining cycle-level fetal S1/S2 event synthesis with a convolutional transmission module and configurable interference and background noise. Model parameters are calibrated cyclewise from real abdominal recordings to capture beat-to-beat variability and to define data-driven admissible ranges for controllable synthesis. The generated signals are validated against real recordings in terms of envelope-based temporal structure and frequency-domain characteristics. The simulator is released as open software to support rapid, reproducible evaluation of fPCG processing methods under controlled acquisition conditions.
Paper Structure (26 sections, 18 equations, 4 figures, 1 table)

This paper contains 26 sections, 18 equations, 4 figures, 1 table.

Table of Contents

  1. Introduction
  2. Method
  3. Overview
  4. Signal model
  5. Event-based parametrization of fetal heart sounds
  6. Cycle timing and event placement
  7. Asymmetric damped-sinusoid kernel
  8. Per-cycle parameter vector
  9. Maternal heart sound component
  10. Abdominal propagation model
  11. Cascaded impulse response
  12. Noise and artifact model
  13. Sampling of cycle-to-cycle variability
  14. Heart-rate specification and weak HRV
  15. Scenario control and abnormal presets
  16. ...and 11 more sections

Figures (4)

  • Figure 1: Interactive web front end of preset-based fPCG generation.
  • Figure 2: Envelope comparison between real and simulated multi-cycle signals on a representative stable window.
  • Figure 3: Comparison between real and simulated signals under identical preprocessing: (left) cycle-averaged envelope ACF; (right) PSD (dB) with error band.
  • Figure 4: Corner plot of fitted parameters $\{A_{S1},A_{S2},\tau_{S1},\tau_{S2},\Delta T\}$.