Adaptive Remote Sensing Paradigm for Real-Time Alerting of Convulsive Epileptic Seizures

Epilepsy is a debilitating neurological condition characterized by intermittent paroxysmal states called fits or seizures. Especially, the major motor seizures of a convulsive nature, such as tonic–clonic seizures, can cause aggravating consequences. Timely alerting for these convulsive epileptic states can therefore prevent numerous complications, during, or following the fit. Based on our previous research, a non-contact method using automated video camera observation and optical flow analysis underwent field trials in clinical settings. Here, we propose a novel adaptive learning paradigm for optimization of the seizure detection algorithm in each individual application. The main objective of the study was to minimize the false detection rate while avoiding undetected seizures. The system continuously updated detection parameters retrospectively using the data from the generated alerts. The system can be used under supervision or, alternatively, through autonomous validation of the alerts. In the latter case, the system achieved self-adaptive, unsupervised learning functionality. The method showed improvement of the detector performance due to the learning algorithm. This functionality provided a personalized seizure alerting device that adapted to the specific patient and environment. The system can operate in a fully automated mode, still allowing human observer to monitor and override the decision process while the algorithm provides suggestions as an expert system

Automated remote fall detection using impact features from video and audio

Elderly people and people with epilepsy may need assistance after falling, but may be unable to summon help due to injuries or impairment of consciousness. Several wearable fall detection devices have been developed, but these are not used by all people at risk. We present an automated analysis algorithm for remote detection of high impact falls, based on a physical model of a fall, aiming at universality and robustness. Candidate events are automatically detected and event features are used as classifier input. The algorithm uses vertical velocity and acceleration features from optical flow outputs, corrected for distance from the camera using moving object size estimation. A sound amplitude feature is used to increase detector specificity. We tested the performance and robustness of our trained algorithm using acted data from a public database and real life data with falls resulting from epilepsy and with daily life activities. Applying the trained algorithm to the acted dataset resulted in 90% sensitivity for detection of falls, with 92% specificity. In the real life data, six/nine falls were detected with a specificity of 99.7%; there is a plausible explanation for not detecting each of the falls missed. These results reflect the algorithm's robustness and confirms the feasibility of detecting falls using this algorithm

Invertible orientation bundles on 2d scalar images

Abstract. A general approach for multiscale orientation analysis of 2D scalar images is proposed. A scale-dependent orientation bundle (map of the visual space into function of two arguments: position and orientation) is constructed from the local Gaussian-derivatives jet of a scalar image in 2D. It is shown that there exists a class of orientation filters exhibiting an invertible relation between the orientation bundle and the original image in space domain. This invertible transformation is used to regain the original acuity in the spatial domain after analyzing orientation features at any given scale. The approach turns out to be highly effective for the detection of elongated structures

A Computational Method for Segmenting Topological Point Sets and Application to Image Analysis

We propose a new computational method for segmenting topological sub-dimensional point-sets in scalar images of arbitrary spatial dimensions. The technique is based on calculating the homotopy class defined by the gradient vector in a sub-dimensional neighborhood around every image point. This neighborhood is defined as the linear envelope spawned over a given sub-dimensional vector frame. In the simplest case where the rank of this frame is maximal, we obtain a technique for localizing the critical points, i.e. extrema and saddle points. We consider in particular the important case of frames formed by an arbitrary number of the first largest by absolute value principal directions of the Hessian. The method then segments positive and and negative ridges as well as other types of critical surfaces of different dimensionalities. The signs of the eigenvalues associated to the principal directions provide a natural labeling of the critical sub-sets

Automated video detection of epileptic convulsion slowing as a precursor for post-ictal generalized EEG suppression

Rationale. Automated monitoring and alerting for adverse events in patients with epilepsy can provide higher security and quality of life for those who suffer from this debilitating condition. Recently we explored the relation between clonic slowing at the end of a convulsive seizure and the occurrence and duration of a subsequent period of post-ictal generalized EEG suppression (PGES). We found that prolonged periods of PGES can be predicted by the amount of progressive increase of inter-clonic intervals (ICI) during the seizure. PGES was previously linked to SUDEP The purpose of the present study is to develop an automated, remote video sensing based algorithm for real-time detection of significant clonic slowing that can be used to alert for PGES and which may eventually help preventing sudden unexpected death in epilepsy (SUDEP). Methods. The technique is based on our earlier published optical flow video sequence processing paradigm that has been applied for automated detection of major motor seizures. Here we introduce an integral Radon-like transformation on the timefrequency wavelet spectrum in order to detect log-linear frequency changes during the seizure. We validate the automated detection and quantification of the ICI increase by comparison to the results from manually processed EEG traces as “gold standard”. We studied 48 cases of convulsive seizures for which synchronized EEG-video recording was available. Results. In most cases the spectral ridges obtained from Gabor-wavelet transformations of the optical flow group velocities were in close proximity to the ICI traces detected manually from EEG data during seizure (the gold standard). The quantification of the slowing-down effect measured by the dominant angle in the Radon transformed spectrum was significantly correlated with the exponential ICI increase factors obtained from manual detection

Automated video-based detection of nocturnal convulsive seizures in a residential care setting

\u3cp\u3ePeople with epilepsy need assistance and are at risk of sudden death when having convulsive seizures (CS). Automated real-time seizure detection systems can help alert caregivers, but wearable sensors are not always tolerated. We determined algorithm settings and investigated detection performance of a video algorithm to detect CS in a residential care setting. The algorithm calculates power in the 2-6 Hz range relative to 0.5-12.5 Hz range in group velocity signals derived from video-sequence optical flow. A detection threshold was found using a training set consisting of video-electroencephalogaphy (EEG) recordings of 72 CS. A test set consisting of 24 full nights of 12 new subjects in residential care and additional recordings of 50 CS selected randomly was used to estimate performance. All data were analyzed retrospectively. The start and end of CS (generalized clonic and tonic-clonic seizures) and other seizures considered desirable to detect (long generalized tonic, hyperkinetic, and other major seizures) were annotated. The detection threshold was set to the value that obtained 97% sensitivity in the training set. Sensitivity, latency, and false detection rate (FDR) per night were calculated in the test set. A seizure was detected when the algorithm output exceeded the threshold continuously for 2 seconds. With the detection threshold determined in the training set, all CS were detected in the test set (100% sensitivity). Latency was ≤10 seconds in 78% of detections. Three/five hyperkinetic and 6/9 other major seizures were detected. Median FDR was 0.78 per night and no false detections occurred in 9/24 nights. Our algorithm could improve safety unobtrusively by automated real-time detection of CS in video registrations, with an acceptable latency and FDR. The algorithm can also detect some other motor seizures requiring assistance.\u3c/p\u3