Automated real-time detection of tonic-clonic seizures using a wearable EMG device

ObjectiveTo determine the accuracy of automated detection of generalized tonic-clonic seizures (GTCS) using a wearable surface EMG device.MethodsWe prospectively tested the technical performance and diagnostic accuracy of real-time seizure detection using a wearable surface EMG device. The seizure detection algorithm and the cutoff values were prespecified. A total of 71 patients, referred to long-term video-EEG monitoring, on suspicion of GTCS, were recruited in 3 centers. Seizure detection was real-time and fully automated. The reference standard was the evaluation of video-EEG recordings by trained experts, who were blinded to data from the device. Reading the seizure logs from the device was done blinded to all other data.ResultsThe mean recording time per patient was 53.18 hours. Total recording time was 3735.5 hours, and device deficiency time was 193 hours (4.9% of the total time the device was turned on). No adverse events occurred. The sensitivity of the wearable device was 93.8% (30 out of 32 GTCS were detected). Median seizure detection latency was 9 seconds (range −4 to 48 seconds). False alarm rate was 0.67/d.ConclusionsThe performance of the wearable EMG device fulfilled the requirements of patients: it detected GTCS with a sensitivity exceeding 90% and detection latency within 30 seconds.Classification of evidenceThis study provides Class II evidence that for people with a history of GTCS, a wearable EMG device accurately detects GTCS (sensitivity 93.8%, false alarm rate 0.67/d).

An efficient complex event detection model for high proportion disordered RFID event stream

With the aim of solving the detection problems for current complex event detection models in detecting a related event for a complex event from the high proportion disordered RFID event stream due to its big uncertainty arrival, an efficient complex event detection model based on Extended Nondeterministic Finite Automaton (ENFA) is proposed in this paper. The achievement of the paper rests on the fact that an efficient complex event detection model based on ENFA is presented to successfully realize the detection of a related event for a complex event from the high proportion disordered RFID event stream. Specially, in our model, we successfully use a new ENFA-based complex event detection model instead of an NFA-based complex event detection model to realize the detection of the related events for a complex event from the high proportion disordered RFID event stream by expanding the traditional NFA-based detection model, which can effectively address the problems above. The experimental results show that the proposed model in this paper outperforms some general models in saving detection time, memory consumption, detection latency and improving detection throughput for detecting a related event of a complex event from the high proportion out-of-order RFID event stream.

Early Seizure Detection Using Neuronal Potential Similarity: A Generalized Low-Complexity and Robust Measure

A novel approach using neuronal potential similarity (NPS) of two intracranial electroencephalogram (iEEG) electrodes placed over the foci is proposed for automated early seizure detection in patients with refractory partial epilepsy. The NPS measure is obtained from the spectral analysis of space-differential iEEG signals. Ratio between the NPS values obtained from two specific frequency bands is then investigated as a robust generalized measure, and reveals invaluable information about seizure initiation trends. A threshold-based classifier is subsequently applied on the proposed measure to generate alarms. The performance of the method was evaluated using cross-validation on a large clinical dataset, involving 183 seizure onsets in 1785 h of long-term continuous iEEG recordings of 11 patients. On average, the results show a high sensitivity of 86.9% (159 out of 183), a very low false detection rate of 1.4 per day, and a mean detection latency of 13.1 s from electrographic seizure onsets, while in average preceding clinical onsets by 6.3 s. These high performance results, specifically the short detection latency, coupled with the very low computational cost of the proposed method make it adequate for using in implantable closed-loop seizure suppression systems.

A Bank of Finite Memory Filters for Fault Detection and Adjusting Detection Latency

This paper proposes a new fault detection scheme using a bank of finite memory filters for discrete-time dynamic systems with multiple sensors. In the proposed scheme, fault detection is carried out by testing the consistency of two filtered estimates, which are obtained from the primary estimation filter and the auxiliary estimation filter using a bank of finite memory filters, respectively. Detection latency is considered as one of important performance criteria and focus on the improvement of detection latency even for high threshold value. Through extensive computer simulations for the F-404 engine system, it is shown that detection latency can be adjusted by the window length. Simulation results show that the trade-off between the fast detection performance and the noise-suppressing estimation performance should be needed for the proposed fault detection scheme in real applications.

Online fault diagnosis of wireless sensor networks

This paper proposes an adaptive online distributed solution for fault diagnosis in wireless sensor networks (WSNs). Fault diagnosis is achieved by comparing the heartbeat message generated by neighboring nodes and dissemination of decision made at each node. Time redundancy is used to detect the intermittent faults since an intermittent fault will not occur consistently. The diagnosis performance degradation due to intermittent faults in sensing and transient faults in communication is analyzed. A near optimal trade-off between detection latency and number of tests required to detect intermittent faults is obtained. Simulation results are provided and they show that this work performs better, from both time and energy complexity viewpoint.