Alimisis Vassilis, Gennis Georgios, Touloupas Konstantinos, Dimas Christos, Uzunoglu Nikolaos, Sotiriadis Paul P
Department of Electrical and Computer Engineering, National Technical University of Athens, 15780 Athens, Greece.
Bioengineering (Basel). 2022 Apr 5;9(4):160. doi: 10.3390/bioengineering9040160.
This paper presents a new analog front-end classification system that serves as a wake-up engine for digital back-ends, targeting embedded devices for epileptic seizure prediction. Predicting epileptic seizures is of major importance for the patient's quality of life as they can lead to paralyzation or even prove fatal. Existing solutions rely on power hungry embedded digital inference engines that typically consume several µW or even mW. To increase the embedded device's autonomy, a new approach is presented combining an analog feature extractor with an analog Gaussian mixture model-based binary classifier. The proposed classification system provides an initial, power-efficient prediction with high sensitivity to switch on the digital engine for the accurate evaluation. The classifier's circuit is chip-area efficient, operating with minimal power consumption (180 nW) at low supply voltage (0.6 V), allowing long-term continuous operation. Based on a real-world dataset, the proposed system achieves 100% sensitivity to guarantee that all seizures are predicted and good specificity (69%), resulting in significant power reduction of the digital engine and therefore the total system. The proposed classifier was designed and simulated in a TSMC 90 nm CMOS process, using the Cadence IC suite.
本文提出了一种新的模拟前端分类系统，作为数字后端的唤醒引擎，目标是用于癫痫发作预测的嵌入式设备。预测癫痫发作对患者的生活质量至关重要，因为癫痫发作可能导致瘫痪甚至危及生命。现有解决方案依赖于功耗大的嵌入式数字推理引擎，通常消耗数微瓦甚至数毫瓦。为了提高嵌入式设备的自主性，提出了一种新方法，将模拟特征提取器与基于模拟高斯混合模型的二元分类器相结合。所提出的分类系统提供初始的、低功耗的预测，具有高灵敏度，以开启数字引擎进行精确评估。该分类器的电路在芯片面积方面效率高，在低电源电压（0.6V）下以最小功耗（180nW）运行，可实现长期连续运行。基于真实世界数据集，所提出的系统实现了100%的灵敏度，以确保所有癫痫发作都能被预测到，并且具有良好的特异性（69%），从而显著降低了数字引擎以及整个系统的功耗。所提出的分类器采用台积电90nm CMOS工艺，使用Cadence IC套件进行设计和仿真。
