轻量级网络提升长序列心电图分类精度与效率
EcoScaleNet: A Lightweight Multi Kernel Network for Long Sequence 12 lead ECG Classification
- 分层设计控制卷积核大小，减少冗余计算
- 参数减少90%，计算量降低99%，准确率提升2.4%
- 适合在普通硬件上实时部署心电图分析系统
准确解读12导联心电图对早期发现心脏异常至关重要，但人工读图易出错，现有基于CNN的分类器难以为长序列心电图选择合适的感受野。Omni Scale CNN（OS CNN）通过枚举素数大小的卷积核覆盖所有尺度，但其设计过于冗余，导致计算开销巨大，阻碍更深更宽模型的发展。本文提出高效卷积全尺度网络（EcoScaleNet），一种分层变体，在保持完整感受野覆盖的同时消除冗余。每个阶段将最大卷积核长度限制在下采样后仍需覆盖的尺度内，并在每个全尺度块前后引入瓶颈卷积，控制通道增长并融合多尺度特征。在大规模CODE 15% ECG数据集上，EcoScaleNet相较OS CNN参数减少90%，浮点运算量（FLOPs）降低99%，同时宏平均F1分数提升2.4%。结果表明，EcoScaleNet以极低计算成本实现了长序列心电图分类的最先进性能，可实现在消费级硬件上的实时部署。代码已开源。
原文摘要 · Abstract (English)
Accurate interpretation of 12 lead electrocardiograms (ECGs) is critical for early detection of cardiac abnormalities, yet manual reading is error prone and existing CNN based classifiers struggle to choose receptive field sizes that generalize to the long sequences typical of ECGs. Omni Scale CNN (OS CNN) addresses this by enumerating prime sized kernels inspired by Goldbach conjecture to cover every scale, but its exhaustive design explodes computational cost and blocks deeper, wider models. We present Efficient Convolutional Omni Scale Network (EcoScale-Net), a hierarchical variant that retains full receptive field coverage while eliminating redundancy. At each stage, the maximum kernel length is capped to the scale still required after down sampling, and bottleneck convolutions inserted before and after every Omni Scale block curtail channel growth and fuse multi scale features. On the large scale CODE 15% ECG dataset, EcoScaleNet reduces parameters by 90% and FLOPs by 99% compared with OS CNN, while raising macro averaged F1 score by 2.4%. These results demonstrate that EcoScaleNet delivers SOTA accuracy for long sequence ECG classification at a fraction of the computational cost, enabling real time deployment on commodity hardware. Our EcoScaleNet code is available in GitHub Link.
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