# Model-driven Heart Rate Estimation and Heart Murmur Detection based on Phonocardiogram

arXiv: https://arxiv.org/abs/2407.18424 · HTML: https://mlsanity.com/paper/2407.18424/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 19 references that are in the map.
- First posted on arXiv 25 Jul 2024.

## Builds on (most cited first, 6 of 6)

- [PPGnet: Deep Network for Device Independent Heart Rate Estimation from Photoplethysmogram](https://mlsanity.com/paper/1903.08912/) (2019)
- [On Extended Long Short-term Memory and Dependent Bidirectional Recurrent Neural Network](https://mlsanity.com/paper/1803.01686/) (2018)
- [Investigating salient representations and label Variance in Dimensional Speech Emotion Analysis](https://mlsanity.com/paper/2312.16180/) (2023)
- [Heart Sound Segmentation using Bidirectional LSTMs with Attention](https://mlsanity.com/paper/2004.03712/) (2020)
- [Estimating Respiratory Rate From Breath Audio Obtained Through Wearable Microphones](https://mlsanity.com/paper/2107.14028/) (2021)
- [Learning Front-end Filter-bank Parameters using Convolutional Neural Networks for Abnormal Heart Sound Detection](https://mlsanity.com/paper/1806.05892/) (2018)

## Built on by (most cited first, 2 of 2)

- [Foundation Model Hidden Representations for Heart Rate Estimation from Auscultation](https://mlsanity.com/paper/2505.20745/) (2025)
- [Breathing and Semantic Pause Detection and Exertion-Level Classification in Post-Exercise Speech](https://mlsanity.com/paper/2509.15473/) (2025)

## Abstract (arXiv)

Acoustic signals are crucial for health monitoring, particularly heart sounds which provide essential data like heart rate and detect cardiac anomalies such as murmurs. This study utilizes a publicly available phonocardiogram (PCG) dataset to estimate heart rate using model-driven methods and extends the best-performing model to a multi-task learning (MTL) framework for simultaneous heart rate estimation and murmur detection. Heart rate estimates are derived using a sliding window technique on heart sound snippets, analyzed with a combination of acoustic features (Mel spectrogram, cepstral coefficients, power spectral density, root mean square energy). Our findings indicate that a 2D convolutional neural network (textbftexttt2dCNN) is most effective for heart rate estimation, achieving a mean absolute error (MAE) of 1.312 bpm. We systematically investigate the impact of different feature combinations and find that utilizing all four features yields the best results. The MTL model (textbftexttt2dCNN-MTL) achieves accuracy over 95% in murmur detection, surpassing existing models, while maintaining an MAE of 1.636 bpm in heart rate estimation, satisfying the requirements stated by Association for the Advancement of Medical Instrumentation (AAMI).