# The CirCor DigiScope Dataset: From Murmur Detection to Murmur Classification

arXiv: https://arxiv.org/abs/2108.00813 · HTML: https://mlsanity.com/paper/2108.00813/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 58 references that are in the map.
- First posted on arXiv 2 Aug 2021.

## Built on by (most cited first, 25 of 25)

- [Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook](https://mlsanity.com/paper/2310.10196/) (2023)
- [CaReAQA: A Cardiac and Respiratory Audio Question Answering Model for Open-Ended Diagnostic Reasoning](https://mlsanity.com/paper/2505.01199/) (2025)
- [A Comprehensive Survey on Heart Sound Analysis in the Deep Learning Era](https://mlsanity.com/paper/2301.09362/) (2023)
- [Dual Bayesian ResNet: A Deep Learning Approach to Heart Murmur Detection](https://mlsanity.com/paper/2305.16691/) (2023)
- [Listen2YourHeart: A Self-Supervised Approach for Detecting Murmur in Heart-Beat Sounds](https://mlsanity.com/paper/2208.14845/) (2022)
- [Heart Murmur and Abnormal PCG Detection via Wavelet Scattering Transform & a 1D-CNN](https://mlsanity.com/paper/2303.11423/) (2023)
- [Do stable neural networks exist for classification problems? -- A new view on stability in AI](https://mlsanity.com/paper/2401.07874/) (2024)
- [BUET Multi-disease Heart Sound Dataset: A Comprehensive Auscultation Dataset for Developing Computer-Aided Diagnostic Systems](https://mlsanity.com/paper/2409.00724/) (2024)
- [Searching for Effective Neural Network Architectures for Heart Murmur Detection from Phonocardiogram](https://mlsanity.com/paper/2303.02988/) (2023)
- [TinyML Design Contest for Life-Threatening Ventricular Arrhythmia Detection](https://mlsanity.com/paper/2305.05105/) (2023)
- [Electromechanical Dynamics of the Heart: A Study of Cardiac Hysteresis During Physical Stress Test](https://mlsanity.com/paper/2410.19667/) (2024)
- [Scattering Transformer: A Training-Free Transformer Architecture for Heart Murmur Detection](https://mlsanity.com/paper/2509.18424/) (2025)
- [Language Models as Semantic Teachers: Post-Training Alignment for Medical Audio Understanding](https://mlsanity.com/paper/2512.04847/) (2025)
- [StethoLM: Audio Language Model for Cardiopulmonary Analysis Across Clinical Tasks](https://mlsanity.com/paper/2603.00355/) (2026)
- [Deep CardioSound-An Ensembled Deep Learning Model for Heart Sound MultiLabelling](https://mlsanity.com/paper/2204.07420/) (2022)
- [A Method for Detecting Murmurous Heart Sounds based on Self-similar Properties](https://mlsanity.com/paper/2306.05283/) (2023)
- [FunnelNet: An End-to-End Deep Learning Framework to Monitor Digital Heart Murmur in Real-Time](https://mlsanity.com/paper/2405.09570/) (2024)
- [Synthetic Time Series Data Generation for Healthcare Applications: A PCG Case Study](https://mlsanity.com/paper/2412.16207/) (2024)
- [Comparative Analysis of CNN and Transformer Architectures with Heart Cycle Normalization for Automated Phonocardiogram Classification](https://mlsanity.com/paper/2507.07058/) (2025)
- [QiVC-Net: Quantum-Inspired Variational Convolutional Network, with Application to Biosignal Classification](https://mlsanity.com/paper/2511.05730/) (2025)
- [H-LDM: Hierarchical Latent Diffusion Models for Controllable and Interpretable PCG Synthesis from Clinical Metadata](https://mlsanity.com/paper/2511.14312/) (2025)
- [Patient-Level Multimodal Question Answering from Multi-Site Auscultation Recordings](https://mlsanity.com/paper/2603.13362/) (2026)
- [Classification of systolic murmurs in heart sounds using multiresolution complex Gabor dictionary and vision transformer](https://mlsanity.com/paper/2604.16563/) (2026)
- [Selection of Heart Sound Segments for Synchronous Classification of Multi-channel Heart Sounds](https://mlsanity.com/paper/2608.21499/) (2026)
- [The Vote Hides the Failure: Aggregation Choice and Noise Robustness in Heart Murmur Detection](https://mlsanity.com/paper/2609.37161/) (2026)

## Abstract (arXiv)

Cardiac auscultation is one of the most cost-effective techniques used to detect and identify many heart conditions. Computer-assisted decision systems based on auscultation can support physicians in their decisions. Unfortunately, the application of such systems in clinical trials is still minimal since most of them only aim to detect the presence of extra or abnormal waves in the phonocardiogram signal, i.e., only a binary ground truth variable (normal vs abnormal) is provided. This is mainly due to the lack of large publicly available datasets, where a more detailed description of such abnormal waves (e.g., cardiac murmurs) exists. To pave the way to more effective research on healthcare recommendation systems based on auscultation, our team has prepared the currently largest pediatric heart sound dataset. A total of 5282 recordings have been collected from the four main auscultation locations of 1568 patients, in the process, 215780 heart sounds have been manually annotated. Furthermore, and for the first time, each cardiac murmur has been manually annotated by an expert annotator according to its timing, shape, pitch, grading, and quality. In addition, the auscultation locations where the murmur is present were identified as well as the auscultation location where the murmur is detected more intensively. Such detailed description for a relatively large number of heart sounds may pave the way for new machine learning algorithms with a real-world application for the detection and analysis of murmur waves for diagnostic purposes.