用量子卷积网络分析心音信号，识别异常波形。
QuPCG: Quantum Convolutional Neural Network for Detecting Abnormal Patterns in PCG Signals
- 将心音信号转为8像素图像，仅需8个量子比特处理
- 在HLS-CMDS数据集上测试准确率达93.33%
- 首次将量子卷积网络用于生物声学信号分析
早期识别异常生理模式对及时发现心脏疾病至关重要。本文提出一种混合量子-经典卷积神经网络（QCNN），用于分类心音信号中的S3和杂音异常。通过小波特征提取与自适应阈值压缩相结合，将一维心音信号转换为紧凑的二维图像，压缩成8像素图像，使量子阶段仅需8个量子比特。在HLS-CMDS数据集上的初步结果显示，测试集分类准确率为93.33%，训练集达97.14%，表明量子模型能高效捕捉生物医学信号中的时频相关性。据我们所知，这是首个将QCNN算法应用于生物声学信号处理的工作。该方法代表了在资源受限医疗环境中迈向量子增强诊断系统的重要一步。
原文摘要 · Abstract (English)
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.
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