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Neural stimulation therapies continue to evolve as new technologies are introduced into clinical practice. It has been over a decade since the initial descriptions of fully implantable, bidirectional neural systems, which allowed for concurrent sensing and stimulation, have been published. A major confounding issue in these types of neural recordings is the contamination of the signal of interest with electrical stimulus artifact, which can obscure short latency evoked activity and corrupt spectral analysis of longer duration signals. Approach. Here we describe the design and early preclinical evaluation of a neurostimulator with improved capabilities for sensing, with particular emphasis on managing stimulus artifact. The system was tested in three ovine deep brain stimulation (DBS) subjects, one with a DBS lead targeting the hippocampus, and two with DBS leads targeting the subthalamic nucleus (STN). All leads were externalized with percutaneous lead extensions. Main results. Results demonstrate that it was possible to record evoked potentials with a latency of 1–2 ms following stimulation in all subjects with the new system. Recordings from the hippocampal target showed clear short-latency responses exhibiting behavior consistent with evoked compound action potentials (ECAPs). In contrast, recordings from the STN target demonstrated highly resonant activity, dependent upon stimulus frequency, which could persist for 20–30 ms following individual stimuli. Both directional stimulation and directional recordings were evaluated to determine their influence on this evoked resonant neural activity (ERNA). The system was also characterized for sensing in one spinal cord stimulation (SCS) ovine subject and one sacral nerve modulation ovine subject. Significance. The bidirectional stimulation and evoked-response sensing system presented here enables sensing evoked responses elicited from stimulation, empowering continued research to expand the understanding and optimization of DBS therapy. Additionally, the example recordings from other therapy spaces demonstrate the capability of the system across neural stimulation therapies.

IntroductionSpinal cord injury (SCI) leads to significant changes in morbidity, mortality and quality of life (QOL). Currently, there are no effective therapies to restore function after chronic SCI. Preliminary studies have indicated that epidural spinal cord stimulation (eSCS) is a promising therapy to improve motor control and autonomic function for patients with chronic SCI. The aim of this study is to assess the effects of tonic eSCS after chronic SCI on quantitative outcomes of volitional movement and cardiovascular function. Our secondary objective is to optimise spinal cord stimulation parameters for volitional movement.Methods and analysisThe Epidural Stimulation After Neurologic Damage (ESTAND) trial is a phase II single-site self-controlled trial of epidural stimulation with the goal of restoring volitional movement and autonomic function after motor complete SCI. Participants undergo epidural stimulator implantation and are followed up over 15 months while completing at-home, mobile application-based movement testing. The primary outcome measure integrates quantity of volitional movement and similarity to normal controls using the volitional response index (VRI) and the modified Brain Motor Control Assessment. The mobile application is a custom-designed platform to support participant response and a kinematic task to optimise the settings for each participant. The application optimises stimulation settings by evaluating the parameter space using movement data collected from the tablet application and accelerometers. A subgroup of participants with cardiovascular dysautonomia are included for optimisation of blood pressure stabilisation. Indirect effects of stimulation on cardiovascular function, pain, sexual function, bowel/bladder, QOL and psychiatric measures are analysed to assess generalisability of this targeted intervention.Ethics and disseminationThis study has been approved after full review by the Minneapolis Medical Research Foundation Institutional Review Board and by the Minneapolis VA Health Care System. This project has received Food and Drug Administration investigational device exemption approval. Trial results will be disseminated through peer-reviewed publications, conference presentations and seminars.Trial registration numberNCT03026816.

We propose a patient-specific algorithm for seizure prediction using multiple features of spectral power from electroencephalogram (EEG) and support vector machine (SVM) classification.The proposed patient-specific algorithm consists of preprocessing, feature extraction, SVM classification, and postprocessing. Preprocessing removes artifacts of intracranial EEG recordings and they are further preprocessed in bipolar and/or time-differential methods. Features of spectral power of raw, or bipolar and/or time-differential intracranial EEG (iEEG) recordings in nine bands are extracted from a sliding 20-s-long and half-overlapped window. Nine bands are selected based on standard EEG frequency bands, but the wide gamma bands are split into four. Cost-sensitive SVMs are used for classification of preictal and interictal samples, and double cross-validation is used to achieve in-sample optimization and out-of-sample testing. We postprocess SVM classification outputs using the Kalman Filter and it removes sporadic and isolated false alarms. The algorithm has been tested on iEEG of 18 patients of 20 available in the Freiburg EEG database who had three or more seizure events. To investigate the discriminability of the features between preictal and interictal, we use the Kernel Fisher Discriminant analysis.The proposed patient-specific algorithm for seizure prediction has achieved high sensitivity of 97.5% with total 80 seizure events and a low false alarm rate of 0.27 per hour and total false prediction times of 13.0% over a total of 433.2 interictal hours by bipolar preprocessing (92.5% sensitivity, a false positive rate of 0.20 per hour, and false prediction times of 9.5% by time-differential preprocessing). This high prediction rate demonstrates that seizures can be predicted by the patient-specific approach using linear features of spectral power and nonlinear classifiers. Bipolar and/or time-differential preprocessing significantly improves sensitivity and specificity. Spectral powers in high gamma bands are the most discriminating features between preictal and interictal.High sensitivity and specificity are achieved by nonlinear classification of linear features of spectral power. Power changes in certain frequency bands already demonstrated their possibilities for seizure prediction indicators, but we have demonstrated that combining those spectral power features and classifying them in a multivariate approach led to much higher prediction rates. Employing only linear features is advantageous, especially when it comes to an implantable device, because they can be computed rapidly with low power consumption.