Mohammed Ameer, Zamani Majid, Bayford Richard, Demosthenous Andreas
Annu Int Conf IEEE Eng Med Biol Soc. 2015 Aug;2015:1528-31. doi: 10.1109/EMBC.2015.7318662.
Continuous deep brain stimulation for Parkinson's disease (PD) patients results in side effects and shortening of the pacemaker battery life. This can be remedied using adaptive stimulation. To achieve adaptive DBS, patient customized PD detection is required due to the inconsistency associated with biomarkers across patients and time. This paper proposes the use of patient specific feature extraction together with adaptive support vector machine (SVM) classifiers to create a patient customized detector for PD. The patient specific feature extraction is obtained using the extrema of the ratio between the PD and non-PD spectra bands of each patient as features, while the adaptive SVM classifier adjusts its decision boundary until a suitable model is obtained. This yields individualised features and classifier pairs for each patient. Datasets containing local field potentials of PD patients were used to validate the method. Six of the nine patient datasets tested achieved a classification accuracy greater than 98%. The adaptive detector is suitable for realization on chip.
对帕金森病（PD）患者进行持续的深部脑刺激会产生副作用并缩短起搏器电池寿命。这可以通过自适应刺激来补救。为了实现自适应深部脑刺激，由于患者之间以及不同时间生物标志物存在不一致性，因此需要针对患者定制PD检测。本文提出使用患者特定的特征提取方法，并结合自适应支持向量机（SVM）分类器，为PD创建一个针对患者定制的检测器。通过将每位患者的PD与非PD频谱带之间的比率极值作为特征来进行患者特定的特征提取，而自适应SVM分类器会调整其决策边界，直到获得合适的模型。这为每位患者生成了个性化的特征和分类器对。使用包含PD患者局部场电位的数据集来验证该方法。在测试的九个患者数据集中，有六个的分类准确率超过了98%。这种自适应检测器适合在芯片上实现。
