Recent development in brain-machine interface technology has made seizure prediction possible. However, the communication of large volume of electrophysiological signals between sensors and processing apparatus and related computation become two major bottlenecks for seizure prediction systems due to the constrained bandwidth and limited computation resource, especially for wearable and implantable medical devices. Although compressive sensing (CS) can be adopted to compress the signals to reduce communication bandwidth requirement, it needs a complex reconstruction procedure before the signal can be used for seizure prediction. In this paper, we propose C$^2$SP-Net, to jointly solve compression, prediction, and reconstruction with a single neural network. A plug-and-play in-sensor compression matrix is constructed to reduce transmission bandwidth requirement. The compressed signal can be used for seizure prediction without additional reconstruction steps. Reconstruction of the original signal can also be carried out in high fidelity. Prediction accuracy, sensitivity, false prediction rate, and reconstruction quality of the proposed framework are evaluated under various compression ratios. The experimental results illustrate that our model outperforms the competitive state-of-the-art baselines by a large margin in prediction accuracy. In particular, our proposed method produces an average loss of 0.35 % in prediction accuracy with a compression ratio ranging from 1/2 to 1/16.
翻译:然而,由于带宽有限,计算资源有限,特别是可磨损和可移植医疗设备,因此在传感器和处理装置及相关计算之间交流大量电子生理信号成为缉获预测系统的两个主要瓶颈。尽管可以采用压缩遥感来压缩信号,以减少通信带宽要求,但在信号可用于预测缉获之前需要复杂的重建程序。在本文中,我们提议用单一神经网络联合解决压缩、预测和重建,以2美元SP-Net(C$2美元),以降低传输带宽要求。为降低传输带宽要求,正在建造一个插座和播放传感器压缩矩阵。压缩信号可用于缉获预测,而无需采取额外的重建步骤。重建原始信号也可以以高度忠诚的方式进行。预测准确性、敏感度、虚假预测率以及拟议框架的重建质量,将在各种压缩比率下进行评估。实验结果表明,我们的模型在预测准确性方面大大超出了具有竞争力的状态基线。特别是,我们提出的方法在1至0.15的精确率方面,从1次至0.15的准确率为1次至0.16。