Department of Electrical and Computer Engineering, The University of British Columbia, 2332 Main Mall, Vancouver, BC V6T 1Z4, Canada.
Sensors (Basel). 2014 Jan 24;14(2):2036-51. doi: 10.3390/s140202036.
The emergence of wireless sensor networks (WSNs) has motivated a paradigm shift in patient monitoring and disease control. Epilepsy management is one of the areas that could especially benefit from the use of WSN. By using miniaturized wireless electroencephalogram (EEG) sensors, it is possible to perform ambulatory EEG recording and real-time seizure detection outside clinical settings. One major consideration in using such a wireless EEG-based system is the stringent battery energy constraint at the sensor side. Different solutions to reduce the power consumption at this side are therefore highly desired. The conventional approach incurs a high power consumption, as it transmits the entire EEG signals wirelessly to an external data server (where seizure detection is carried out). This paper examines the use of data reduction techniques for reducing the amount of data that has to be transmitted and, thereby, reducing the required power consumption at the sensor side. Two data reduction approaches are examined: compressive sensing-based EEG compression and low-complexity feature extraction. Their performance is evaluated in terms of seizure detection effectiveness and power consumption. Experimental results show that by performing low-complexity feature extraction at the sensor side and transmitting only the features that are pertinent to seizure detection to the server, a considerable overall saving in power is achieved. The battery life of the system is increased by 14 times, while the same seizure detection rate as the conventional approach (95%) is maintained.
无线传感器网络（WSN）的出现推动了患者监测和疾病控制的范式转变。癫痫管理是特别受益于使用 WSN 的领域之一。通过使用微型化的无线脑电图（EEG）传感器，可以在临床环境之外进行动态脑电图记录和实时癫痫发作检测。在使用这种基于无线 EEG 的系统时，一个主要考虑因素是传感器端严格的电池能量限制。因此，非常需要降低此端功耗的不同解决方案。传统方法会导致高功耗，因为它需要将整个 EEG 信号无线传输到外部数据服务器（在该服务器上进行癫痫发作检测）。本文研究了数据减少技术的使用，以减少必须传输的数据量，并从而降低传感器端的所需功耗。研究了两种数据减少方法：基于压缩感知的 EEG 压缩和低复杂度特征提取。根据癫痫发作检测的有效性和功耗来评估它们的性能。实验结果表明，通过在传感器端执行低复杂度特征提取，并仅将与癫痫发作检测相关的特征传输到服务器，可以实现相当大的整体功率节省。系统的电池寿命延长了 14 倍，同时保持了与传统方法相同的癫痫发作检测率（95%）。
