# QuPCG: Quantum Convolutional Neural Network for Detecting Abnormal Patterns in PCG Signals

arXiv: https://arxiv.org/abs/2511.02140 · HTML: https://mlsanity.com/paper/2511.02140/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 34 references that are in the map.
- First posted on arXiv 4 Nov 2025.

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

- [Variational Quantum Algorithms](https://mlsanity.com/paper/2012.09265/) (2020)
- [Molecular Property Prediction: A Multilevel Quantum Interactions Modeling Perspective](https://mlsanity.com/paper/1906.11081/) (2019)
- [Large Language Model-based Nonnegative Matrix Factorization For Cardiorespiratory Sound Separation](https://mlsanity.com/paper/2502.05757/) (2025)
- [A Novel Image Classification Framework Based on Variational Quantum Algorithms](https://mlsanity.com/paper/2312.07932/) (2023)
- [Quantum Annealing for Automated Feature Selection in Stress Detection](https://mlsanity.com/paper/2106.05134/) (2021)
- [quEEGNet: Quantum AI for Biosignal Processing](https://mlsanity.com/paper/2210.00864/) (2022)

## Abstract (arXiv)

Early identification of abnormal physiological patterns is essential for the timely detection of cardiac disease. This work introduces a hybrid quantum-classical convolutional neural network (QCNN) designed to classify S3 and murmur abnormalities in heart sound signals. The approach transforms one-dimensional phonocardiogram (PCG) signals into compact two-dimensional images through a combination of wavelet feature extraction and adaptive threshold compression methods. We compress the cardiac-sound patterns into an 8-pixel image so that only 8 qubits are needed for the quantum stage. Preliminary results on the HLS-CMDS dataset demonstrate 93.33% classification accuracy on the test set and 97.14% on the train set, suggesting that quantum models can efficiently capture temporal-spectral correlations in biomedical signals. To our knowledge, this is the first application of a QCNN algorithm for bioacoustic signal processing. The proposed method represents an early step toward quantum-enhanced diagnostic systems for resource-constrained healthcare environments.