Hardware-Mappable Cellular Neural Networks for Distributed Wavefront Detection in Next-Generation Cardiac Implants.
Hardware-Mappable Cellular Neural Networks for Distributed Wavefront Detection in Next-Generation Cardiac Implants.
复制标题
用于下一代心脏植入物中分布式波前检测的硬件可映射细胞神经网络。
DOI：
10.1002/aisy.202200032
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Adam,GinaC
中科院分区：
文献类型：
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作者：
Yang,Zhuolin;Zhang,Lei;Aras,Kedar;Efimov,IgorR;Adam,GinaC
人工智能算法被用于分析医疗数据，有望更快地解释以支持医生的诊断。下一个前沿是将这些强大的算法应用于植入式医疗设备。在本文中，提出了一种闭环解决方案，其中细胞神经网络用于检测在人体组织中记录的心脏信号中的异常波前，并且当假设浮点精度权重时，训练波闸以实现>96%的准确度、>92%的精度、>99%的特异性和>93%的灵敏度。不幸的是，当前用于浮点精度的硬件技术对于医疗植入物中的紧凑独立应用来说过于庞大或能量密集。忆阻器等新兴设备技术可以提供紧凑且节能的硬件结构来支持这些努力，并且可以可靠地嵌入植入式设备中的现有传感器和致动器平台。一个分布式的设计，考虑硬件的限制，在开销和有限的位精度进行了讨论。所提出的分布式解决方案可以很容易地适应其他需要紧凑和高效计算的医疗技术，如可穿戴设备和芯片实验室平台。
Artificial intelligence algorithms are being adopted to analyze medical data, promising faster interpretation to support doctors’ diagnostics. The next frontier is to bring these powerful algorithms to implantable medical devices. Herein, a closed‐loop solution is proposed, where a cellular neural network is used to detect abnormal wavefronts and wavebrakes in cardiac signals recorded in human tissue is trained to achieve >96% accuracy, >92% precision, >99% specificity, and >93% sensitivity, when floating point precision weights are assumed. Unfortunately, the current hardware technologies for floating point precision are too bulky or energy intensive for compact standalone applications in medical implants. Emerging device technologies, such as memristors, can provide the compact and energy‐efficient hardware fabric to support these efforts and can be reliably embedded with existing sensor and actuator platforms in implantable devices. A distributed design that considers the hardware limitations in terms of overhead and limited bit precision is also discussed. The proposed distributed solution can be easily adapted to other medical technologies that require compact and efficient computing, like wearable devices and lab‐on‐chip platforms.