QuPCG: Quantum Convolutional Neural Network for Detecting Abnormal Patterns in PCG Signals
This work addresses cardiac disease diagnosis for resource-constrained healthcare environments, but it is an incremental step as it applies a known quantum method to a new biomedical domain.
The paper tackled the problem of early detection of cardiac disease by classifying abnormal heart sound patterns using a hybrid quantum-classical convolutional neural network, achieving 93.33% accuracy on a test set.
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.