Automated Detection of Convulsive Seizures Using a Wearable Accelerometer Device.

Epileptic seizure detection requires specialized approaches such as video/electroencephalography monitoring. However, these approaches are restricted mainly to hospital setting and requires video/EEG analysis by experts, which makes these approaches resource- and labor-intensive. In contrast, we aim to develop a wireless remote monitoring system based on a single wrist-worn accelerometer device, which is sensitive to multiple types of convulsive seizures and is capable of detecting seizures with short duration. Simple time domain features including a new set of Poincar´e plot based features were extracted from the active movement events recorded using a wrist-worn accelerometer device. The best features were then selected using the area under the ROC curve analysis. Kernelized support vector data description (SVDD) was then used to classify non-seizure and seizure events. The proposed algorithm was evaluated on 5;576h of recordings from 79 patients and detected 40 (86:95%) of 46 convulsive seizures (generalized tonic-clonic (GTCS), psychogenic non-epileptic (PNES), and complex partial seizures (CPS)) from twenty patients with a total of 270 false alarms (1:16=24h). Furthermore, the algorithm showed a comparable performance (sensitivity 95:23% and false alarm rate 0:64=24h) with respect to existing unimodal and multi-modal methods for GTCS detection. The promising results shows the potential to build an ambulatory monitoring convulsive seizure detection system. A wearable accelerometer based seizure detection system would aid in continuous assessment of convulsive seizures in a timely and non-invasive manner.

Micro-CT and Histological Evaluation of an Neural Interface Implanted Within a Blood Vessel.

OBJECTIVE: Recently, we reported the development of a stent-mounted electrode array (Stentrode) capable of chronically recording neural signals from within a blood vessel with high fidelity. Preliminary data suggested incorporation of the Stentrode into the blood vessel wall was associated with improved recording sensitivity. We now investigate neointimal incorporation of the Stentrode, implanted in a cohort of sheep for up to 190 days. METHODS: Micro-CT, obtained from the Imaging and Medical Beamline at the Australian Synchrotron, and histomorphometic techniques developed specifically for evaluation of cerebral vasculature implanted with a stent-electrode array were compared as measures to assess device incorporation and vessel patency. RESULTS: Both micro-CT analysis and histomorphometry, revealed a strong correlation between implant duration and the number of incorporated stent struts. <10% (26/268) of stent struts were covered in neointima in sheep implanted for <2 weeks, increasing to >78% (191/243) between 2 and 4 weeks. Average strut-to-lumen thickness from animals implanted >12 weeks was comparable across both modalities, 339 ±15 µm measured using micro-CT and 331 ±19 µm ( n = 292) measured histologically. There was a strong correlation between lumen areas measured using the two modalities ( ), with no observation of vessel occlusion observed from any of the 12 animals implanted for up to 190 days. CONCLUSION: Micro-CT and the histomorphometric techniques we developed are comparable and can both be used to identify incorporation of a Stentrode implanted in cerebral vessels. SIGNIFICANCE: This study demonstrates preliminary safety of a stent-electrode array implanted in cerebral vasculature, which may facilitate technological advances in minimally invasive brain-computer interfaces.

Automatic Detection and Classification of Convulsive Psychogenic Nonepileptic Seizures Using a Wearable Device.

Epilepsy is one of the most common neurological disorders and patients suffer from unprovoked seizures. In contrast, psychogenic nonepileptic seizures (PNES) are another class of seizures that are involuntary events not caused by abnormal electrical discharges but are a manifestation of psychological distress. The similarity of these two types of seizures poses diagnostic challenges that often leads in delayed diagnosis of PNES. Further, the diagnosis of PNES involves high-cost hospital admission and monitoring using video-electroencephalogram machines. A wearable device that can monitor the patient in natural setting is a desired solution for diagnosis of convulsive PNES. A wearable device with an accelerometer sensor is proposed as a new solution in the detection and diagnosis of PNES. The seizure detection algorithm and PNES classification algorithm are developed. The developed algorithms are tested on data collected from convulsive epileptic patients. A very high seizure detection rate is achieved with 100% sensitivity and few false alarms. A leave-one-out error of 6.67% is achieved in PNES classification, demonstrating the usefulness of wearable device in the diagnosis of PNES.