Khobragade Nivedita, Graupe Daniel, Tuninetti Daniela
Annu Int Conf IEEE Eng Med Biol Soc. 2015;2015:2616-9. doi: 10.1109/EMBC.2015.7318928.
This paper describes the application of the LAMSTAR (LArge Memory STorage and Retrieval) neural network for prediction of onset of tremor in Parkinson's disease (PD) patients to allow for on-off adaptive control of Deep Brain Stimulation (DBS). Currently, the therapeutic treatment of PD by DBS is an open-loop system where continuous stimulation is applied to a target area in the brain. This work demonstrates a fully automated closed-loop DBS system so that stimulation can be applied on-demand only when needed to treat PD symptoms. The proposed LAMSTAR network uses spectral, entropy and recurrence rate parameters for prediction of the advent of tremor after the DBS stimulation is switched off. These parameters are extracted from non-invasively collected surface electromyography and accelerometry signals. The LAMSTAR network has useful characteristics, such as fast retrieval of patterns and ability to handle large amount of data of different types, which make it attractive for medical applications. Out of 21 trials blue from one subject, the average ratio of delay in prediction of tremor to the actual delay in observed tremor from the time stimulation was switched off achieved by the proposed LAMSTAR network is 0.77. Moreover, sensitivity of 100% and overall performance better than previously proposed Back Propagation neural networks is obtained.
本文描述了LAMSTAR（大容量存储与检索）神经网络在预测帕金森病（PD）患者震颤发作方面的应用，以实现深部脑刺激（DBS）的开-关自适应控制。目前，通过DBS对PD进行治疗是一个开环系统，即持续对脑内目标区域进行刺激。这项工作展示了一种全自动闭环DBS系统，使得刺激仅在需要治疗PD症状时按需施加。所提出的LAMSTAR网络使用频谱、熵和复发率参数来预测DBS刺激关闭后震颤的出现。这些参数从非侵入性收集的表面肌电图和加速度计信号中提取。LAMSTAR网络具有一些有用的特性，如模式快速检索以及处理大量不同类型数据的能力，这使其在医学应用中具有吸引力。在所提出的LAMSTAR网络从一名受试者进行的21次试验中，预测震颤延迟与刺激关闭后观察到的实际震颤延迟的平均比值为0.77。此外，获得了100%的灵敏度以及比先前提出的反向传播神经网络更好的整体性能。
