Accurate detection of spontaneous seizures using a generalized linear model with external validation.

OBJECTIVE: Seizure detection is a major facet of electroencephalography (EEG) analysis in neurocritical care, epilepsy diagnosis and management, and the instantiation of novel therapies such as closed-loop stimulation or optogenetic control of seizures. It is also of increased importance in high-throughput, robust, and reproducible pre-clinical research. However, seizure detectors are not widely relied upon in either clinical or research settings due to limited validation. In this study, we create a high-performance seizure-detection approach, validated in multiple data sets, with the intention that such a system could be available to users for multiple purposes. METHODS: We introduce a generalized linear model trained on 141 EEG signal features for classification of seizures in continuous EEG for two data sets. In the first (Focal Epilepsy) data set consisting of 16 rats with focal epilepsy, we collected 1012 spontaneous seizures over 3 months of 24/7 recording. We trained a generalized linear model on the 141 features representing 20 feature classes, including univariate and multivariate, linear and nonlinear, time, and frequency domains. We tested performance on multiple hold-out test data sets. We then used the trained model in a second (Multifocal Epilepsy) data set consisting of 96 rats with 2883 spontaneous multifocal seizures. RESULTS: From the Focal Epilepsy data set, we built a pooled classifier with an Area Under the Receiver Operating Characteristic (AUROC) of 0.995 and leave-one-out classifiers with an AUROC of 0.962. We validated our method within the independently constructed Multifocal Epilepsy data set, resulting in a pooled AUROC of 0.963. We separately validated a model trained exclusively on the Focal Epilepsy data set and tested on the held-out Multifocal Epilepsy data set with an AUROC of 0.890. Latency to detection was under 5 seconds for over 80% of seizures and under 12 seconds for over 99% of seizures. SIGNIFICANCE: This method achieves the highest performance published for seizure detection on multiple independent data sets. This method of seizure detection can be applied to automated EEG analysis pipelines as well as closed loop interventional approaches, and can be especially useful in the setting of research using animals in which there is an increased need for standardization and high-throughput analysis of large number of seizures.

Organic electrochemical transistors printed from degradable materials as disposable biochemical sensors.

Transient electronics hold promise in reducing electronic waste, especially in applications that require only a limited lifetime. While various degradable electronic and physical sensing devices have been proposed, there is growing interest in the development of degradable biochemical sensors. In this work, we present the development of an organic electrochemical transistor (OECT) with degradable electrodes, printed on an eco- and bioresorbable substrate. The influence of the design and materials for the contacts, channel and gate of the transducer, namely poly(3,4-ethylene dioxythiophene):polystyrene sulfonate (PEDOT:PSS) and carbon, is systematically evaluated for the development of OECT-based transient biosensors. The sensing capabilities of the electrochemical transistors are demonstrated with ionic solutions as well as for the enzyme-based detection of glucose. The disposable OECTs show comparable performance to their non-degradable counterparts. Their integration with highly conductive degradable and printable zinc tracks is studied for the realization of interconnects. These eco-friendly OECTs may find applications as disposable and sustainable biochemical sensors, and constitute a step towards bioresorbable biosensors.

A multidirectional gravity-assist algorithm that enhances locomotor control in patients with stroke or spinal cord injury.

Gait recovery after neurological disorders requires remastering the interplay between body mechanics and gravitational forces. Despite the importance of gravity-dependent gait interactions and active participation for promoting this learning, these essential components of gait rehabilitation have received comparatively little attention. To address these issues, we developed an adaptive algorithm that personalizes multidirectional forces applied to the trunk based on patient-specific motor deficits. Implementation of this algorithm in a robotic interface reestablished gait dynamics during highly participative locomotion within a large and safe environment. This multidirectional gravity-assist enabled natural walking in nonambulatory individuals with spinal cord injury or stroke and enhanced skilled locomotor control in the less-impaired subjects. A 1-hour training session with multidirectional gravity-assist improved locomotor performance tested without robotic assistance immediately after training, whereas walking the same distance on a treadmill did not ameliorate gait. These results highlight the importance of precise trunk support to deliver gait rehabilitation protocols and establish a practical framework to apply these concepts in clinical routine.

A neurorobotic platform for locomotor prosthetic development in rats and mice.

OBJECTIVES: We aimed to develop a robotic interface capable of providing finely-tuned, multidirectional trunk assistance adjusted in real-time during unconstrained locomotion in rats and mice. APPROACH: We interfaced a large-scale robotic structure actuated in four degrees of freedom to exchangeable attachment modules exhibiting selective compliance along distinct directions. This combination allowed high-precision force and torque control in multiple directions over a large workspace. We next designed a neurorobotic platform wherein real-time kinematics and physiological signals directly adjust robotic actuation and prosthetic actions. We tested the performance of this platform in both rats and mice with spinal cord injury. MAIN RESULTS: Kinematic analyses showed that the robotic interface did not impede locomotor movements of lightweight mice that walked freely along paths with changing directions and height profiles. Personalized trunk assistance instantly enabled coordinated locomotion in mice and rats with severe hindlimb motor deficits. Closed-loop control of robotic actuation based on ongoing movement features enabled real-time control of electromyographic activity in anti-gravity muscles during locomotion. SIGNIFICANCE: This neurorobotic platform will support the study of the mechanisms underlying the therapeutic effects of locomotor prosthetics and rehabilitation using high-resolution genetic tools in rodent models.

Neuroprosthetic technologies to augment the impact of neurorehabilitation after spinal cord injury.

Spinal cord injury leads to a range of disabilities, including limitations in locomotor activity, that seriously diminish the patients' autonomy and quality of life. Electrochemical neuromodulation therapies, robot-assisted rehabilitation and willpower-based training paradigms restored supraspinal control of locomotion in rodent models of severe spinal cord injury. This treatment promoted extensive and ubiquitous remodeling of spared circuits and residual neural pathways. In four chronic paraplegic individuals, electrical neuromodulation of the spinal cord resulted in the immediate recovery of voluntary leg movements, suggesting that the therapeutic concepts developed in rodent models may also apply to humans. Here, we briefly review previous work, summarize current developments, and highlight impediments to translate these interventions into medical practice to improve functional recovery of spinal-cord-injured individuals.