Although numerous R-peak detectors have been proposed in the literature, their robustness and performance levels may significantly deteriorate in low-quality and noisy signals acquired from mobile electrocardiogram (ECG) sensors, such as Holter monitors. Recently, this issue has been addressed by deep 1-D convolutional neural networks (CNNs) that have achieved state-of-the-art performance levels in Holter monitors; however, they pose a high complexity level that requires special parallelized hardware setup for real-time processing. On the other hand, their performance deteriorates when a compact network configuration is used instead. This is an expected outcome as recent studies have demonstrated that the learning performance of CNNs is limited due to their strictly homogenous configuration with the sole linear neuron model. In this study, to further boost the peak detection performance along with an elegant computational efficiency, we propose 1-D Self-Organized ONNs (Self-ONNs) with generative neurons. The most crucial advantage of 1-D Self-ONNs over the ONNs is their self-organization capability that voids the need to search for the best operator set per neuron since each generative neuron has the ability to create the optimal operator during training. The experimental results over the China Physiological Signal Challenge-2020 (CPSC) dataset with more than one million ECG beats show that the proposed 1-D Self-ONNs can significantly surpass the state-of-the-art deep CNN with less computational complexity. Results demonstrate that the proposed solution achieves a 99.10% F1-score, 99.79% sensitivity, and 98.42% positive predictivity in the CPSC dataset, which is the best R-peak detection performance ever achieved.

翻译：尽管文献中已提出大量R波检测器，但在移动心电图传感器（如Holter监护仪）采集的低质量、含噪信号中，其鲁棒性和性能水平可能显著下降。近年来，这一问题通过深度一维卷积神经网络得到解决，该网络在Holter监护仪中取得了最先进的性能水平，然而其高复杂度需要专用并行化硬件才能实现实时处理。另一方面，当采用紧凑型网络配置时，其性能会下降。这一结果符合预期，因为最新研究表明，CNN的严格同质化配置（仅使用线性神经元模型）限制了其学习能力。在本研究中，为在保证出色计算效率的同时进一步提升峰值检测性能，我们提出了具有生成神经元的一维自组织操作神经网络。与ONN相比，1-D Self-ONN最关键的优点在于其自组织能力——由于每个生成神经元都能够在训练过程中自主创建最优算子，因此无需为每个神经元搜索最佳算子集。在包含超过一百万次心搏的中国生理信号挑战赛-2020数据集上的实验结果表明，所提出的1-D Self-ONN能够以更低的计算复杂度显著超越当前最先进的深度CNN。结果显示，该方案在CPSC数据集上实现了99.10%的F1分数、99.79%的敏感性和98.42%的阳性预测值，这是迄今为止获得的最佳R波检测性能。