A Neuromorphic Model With Delay-Based Reservoir for Continuous Ventricular Heartbeat Detection.
A Neuromorphic Model With Delay-Based Reservoir for Continuous Ventricular Heartbeat Detection.
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用于连续心室心跳检测的具有基于延迟的储层的神经形态模型。
DOI：
10.1109/tbme.2021.3129306
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Liang X
中科院分区：
文献类型：
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作者：
Liang X
神经形态硬件的兴趣越来越大，因为它提供了一种更直观的方式来实现生物启发算法。提出了一种用于连续心电信号智能处理的神经元模型。该模型旨在开发一种基于硬件的信号处理模型，并避免采用数字密集型操作，如信号分割和特征提取，这是不希望在模拟神经形态系统。我们采用基于延迟的水库计算作为信息处理的核心，沿着一种新的训练和标记方法。与传统的ECG分类技术不同，该计算模型是一个端到端的动态系统，模拟了神经形态硬件中的实时信号流。输入是原始ECG流，而输出的幅度表示室性异位心跳的风险因素。库的固有忆阻特性使系统能够保留历史ECG信息以用于高维映射。该模型与MIT-BIH数据库在患者间范例下进行了评估，灵敏度为81%，准确度为98%。在这种架构下，推理过程中所需的最小内存大小可以低至3.1兆字节（MB），因为大部分计算都发生在模拟域中。这种计算建模通过简化计算过程和最小化未来可穿戴设备所需的内存来提高内存效率。
There is a growing interest in neuromorphic hardware since it offers a more intuitive way to achieve bio-inspired algorithms. This paper presents a neuromorphic model for intelligently processing continuous electrocardiogram (ECG) signal. This model aims to develop a hardware-based signal processing model and avoid employing digitally intensive operations, such as signal segmentation and feature extraction, which are not desired in an analogue neuromorphic system. We apply delay-based reservoir computing as the information processing core, along with a novel training and labelling method. Different from the conventional ECG classification techniques, this computation model is a end-to-end dynamic system that mimics the real-time signal flow in neuromorphic hardware. The input is the raw ECG stream, while the amplitude of the output represents the risk factor of a ventricular ectopic heartbeat. The intrinsic memristive property of the reservoir empowers the system to retain the historical ECG information for high-dimensional mapping. This model was evaluated with the MIT-BIH database under the inter-patient paradigm and yields 81% sensitivity and 98% accuracy. Under this architecture, the minimum size of memory required in the inference process can be as low as 3.1 MegaByte(MB) because the majority of the computation takes place in the analogue domain. Such computational modelling boosts memory efficiency by simplifying the computing procedure and minimizing the required memory for future wearable devices.