Real-Time Ultra-Low Power ECG Anomaly Detection Using an Event-Driven Neuromorphic Processor
Real-Time Ultra-Low Power ECG Anomaly Detection Using an Event-Driven Neuromorphic Processor
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DOI：
10.1109/tbcas.2019.2953001
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发表时间：
2019-12-01
影响因子：
5.1
通讯作者：
Indiveri, Giacomo
中科院分区：
文献类型：
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作者：
Bauer, Felix Christian;Muir, Dylan Richard;Indiveri, Giacomo
人类受试者中病理状况的准确检测可以通过对记录的生物信号（例如心电图（ECG））的离线分析来实现。然而，人类诊断是耗时和昂贵的，因为它需要医疗专业人员的时间。当生物信号中的指示性模式不频繁时，这尤其低效。此外，具有疑似病理的患者通常被长时间监测，这需要存储和检查大量非病理数据，并且给诊断专业人员带来困难的视觉搜索任务。在这项工作中，我们提出了一个紧凑的和亚mW的低功耗神经处理系统，可用于执行在线和实时的初步诊断的病理条件，提出警告可能的病理条件的存在，或触发离线数据记录系统的进一步分析由医疗专业人员。我们将该系统应用于ECG数据的实时分类，以区分健康的心跳和病理性心律。多通道模拟ECG迹线被编码为二进制事件的异步流，并使用在水库计算范例中操作的尖峰递归神经网络进行处理。然后训练事件驱动的神经元输出层以识别几种病理之一。最后，该输出层的过滤活动用于生成指示病理模式的存在或不存在的二进制触发信号。我们验证的方法，提出了使用动态神经形态异步处理器（DYNAP）芯片，实现使用标准的180纳米CMOS超大规模集成电路工艺，并提出从芯片测量的实验结果。
Accurate detection of pathological conditions in human subjects can be achieved through off-line analysis of recorded biological signals such as electrocardiograms (ECGs). However, human diagnosis is time-consuming and expensive, as it requires the time of medical professionals. This is especially inefficient when indicative patterns in the biological signals are infrequent. Moreover, patients with suspected pathologies are often monitored for extended periods, requiring the storage and examination of large amounts of non-pathological data, and entailing a difficult visual search task for diagnosing professionals. In this work we propose a compact and sub-mW low power neural processing system that can be used to perform on-line and real-time preliminary diagnosis of pathological conditions, to raise warnings for the existence of possible pathological conditions, or to trigger an off-line data recording system for further analysis by a medical professional. We apply the system to real-time classification of ECG data for distinguishing between healthy heartbeats and pathological rhythms. Multi-channel analog ECG traces are encoded as asynchronous streams of binary events and processed using a spiking recurrent neural network operated in a reservoir computing paradigm. An event-driven neuron output layer is then trained to recognize one of several pathologies. Finally, the filtered activity of this output layer is used to generate a binary trigger signal indicating the presence or absence of a pathological pattern. We validate the approach proposed using a Dynamic Neuromorphic Asynchronous Processor (DYNAP) chip, implemented using a standard 180nm CMOS VLSI process, and present experimental results measured from the chip.