A highly accurate spike sorting processor with reconfigurable embedded frames for unsupervised and adaptive analysis of neural signals

Future implantable devices demand ultra-low power consumption with self-calibration capability providing real-time processing of biomedical signals. This paper introduces an adaptive processing framework for highly accurate on-chip spike sorting processing by learning the signal model in the recorded neural data. The novel adaptive spike sorting processor employs dual thresholding detection, adaptive feature extraction and online clustering with sorting threshold self-tuning capability. A prototype chip was fabricated in 180 nm CMOS technology. It achieves 84.5% overall clustering accuracy, provides up to 240X data reduction and consumes 148 μW of power from a 1.8 V supply voltage

Patient specific Parkinson's disease detection for adaptive deep brain stimulation

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.</p