A Bayesian approach for epileptic seizures detection with 3D accelerometers sensors

Assessing the Feasibility of Acoustic Based Seizure Detection

OBJECTIVE

Long-term monitoring of epilepsy patients outside of hospital settings is impractical due to the complexity and costs associated with electroencephalogram (EEG) systems. Alternative sensing modalities that can acquire, and automatically interpret signals through easy-to-use wearable devices, are needed to help with at-home management of the disease. In this paper, a novel machine learning algorithm is presented for detecting epileptic seizures using acoustic physiological signals acquired from the neck using a wearable device.

METHODS

Acoustic signals from an existing database, were processed, to extract their Mel-frequency Cepstral Coefficients (MFCCs) which were used to train RUSBoost classifiers to identify ictal and non-ictal acoustic segments. A postprocessing stage was then applied to the segment classification results to identify seizures episodes.

RESULTS

Tested on 667 hours of acoustic data acquired from 15 patients with at least one seizure, the algorithm achieved a detection sensitivity of 88.1% (95% CI: 79%-97%) from a total of 36 seizures, out of which 24 had no motor manifestations, with a FPR of 0.83/h, and a median detection latency of -42 s.

CONCLUSION

The results demonstrated for the first time the ability to identify seizures using acoustic internal body signals acquired on the neck.

SIGNIFICANCE

The results of this paper validate the feasibility of using internal physiological sounds for seizure detection, which could potentially be of use for the development of novel, wearable, very simple to use, long term monitoring, or seizure detection systems; circumventing the practical limitations of EEG monitoring outside hospital settings, or systems based on sensing modalities that work on convulsive seizures only.

[Mobile seizure monitoring in epilepsy patients]

Wearables are receiving much attention from both epilepsy patients and treating physicians, for monitoring of seizure frequency and warning of seizures. They are also of interest for the detection of seizure-associated risks of patients, for differential diagnosis of rare seizure types and prediction of seizure-prone periods. Accelerometry, electromyography (EMG), heart rate and further autonomic parameters are recorded to capture clinical seizure manifestations. Currently, a clinical use to document nocturnal motor seizures is feasible. In this review the available devices, data on the performance in the documentation of seizures, current options for clinical use and developments in data analysis are presented and critically discussed.

Neural stimulation systems for the control of refractory epilepsy: a review

Epilepsy affects nearly 1% of the world's population. A third of epilepsy patients suffer from a kind of epilepsy that cannot be controlled by current medications. For those where surgery is not an option, neurostimulation may be the only alternative to bring relief, improve quality of life, and avoid secondary injury to these patients. Until recently, open loop neurostimulation was the only alternative for these patients. However, for those whose epilepsy is applicable, the medical approval of the responsive neural stimulation and the closed loop vagal nerve stimulation systems have been a step forward in the battle against uncontrolled epilepsy. Nonetheless, improvements can be made to the existing systems and alternative systems can be developed to further improve the quality of life of sufferers of the debilitating condition. In this paper, we first present a brief overview of epilepsy as a disease. Next, we look at the current state of biomarker research in respect to sensing and predicting epileptic seizures. Then, we present the current state of open loop neural stimulation systems. We follow this by investigating the currently approved, and some of the recent experimental, closed loop systems documented in the literature. Finally, we provide discussions on the current state of neural stimulation systems for controlling epilepsy, and directions for future studies.

Seizure detection: do current devices work? And when can they be useful?

PURPOSE OF REVIEW

The unpredictability and apparent randomness of epileptic seizures is one of the most vexing aspects of epilepsy. Methods or devices capable of detecting seizures may help prevent injury or even death and significantly improve quality of life. Here, we summarize and evaluate currently available, unimodal, or polymodal detection systems for epileptic seizures, mainly in the ambulatory setting.

RECENT FINDINGS

There are two broad categories of detection devices: EEG-based and non-EEG-based systems. Wireless wearable EEG devices are now available both in research and commercial arenas. Neuro-stimulation devices are currently evolving and initial experiences of these show potential promise. As for non-EEG devices, different detecting systems show different sensitivity according to the different patient and seizure types. Regardless, when used in combination, these modalities may complement each other to increase positive predictive value. Although some devices with high sensitivity are promising, practical widespread use of such detection systems is still some way away. More research and experience are needed to evaluate the most efficient and integrated systems, to allow for better approaches to detection and prediction of seizures. The concept of closed-loop systems and prompt intervention may substantially improve quality of life for patients and carers.

Patient specific seizure prediction system using Hilbert spectrum and Bayesian networks classifiers

The aim of this paper is to develop an automated system for epileptic seizure prediction from intracranial EEG signals based on Hilbert-Huang transform (HHT) and Bayesian classifiers. Proposed system includes decomposition of the signals into intrinsic mode functions for obtaining features and use of Bayesian networks with correlation based feature selection for binary classification of preictal and interictal recordings. The system was trained and tested on Freiburg EEG database. 58 hours of preictal data, 40-minute data blocks prior to each of 87 seizures collected from 21 patients, and 503.1 hours of interictal data were examined resulting in 96.55% sensitivity with 0.21 false alarms per hour, 13.896% average proportion of time spent in warning, and 33.21 minutes of average detection latency using 30-second EEG segments with 50% overlap and a simple postprocessing technique resulting in a decision (a seizure is expected/not expected) every 5 minutes. High sensitivity and low false positive rate with reasonable detection latency show that HHT based features are acceptable for patient specific seizure prediction from intracranial EEG data. Time spent for testing an EEG segment was 4.1451 seconds on average, which makes the system viable for use in real-time seizure control systems.

Accelerometry-based home monitoring for detection of nocturnal hypermotor seizures based on novelty detection

Nocturnal home monitoring of epileptic children is often not feasible due to the cumbersome manner of seizure monitoring with the standard method of video/EEG-monitoring. We propose a method for hypermotor seizure detection based on accelerometers attached to the extremities. From the acceleration signals, multiple temporal, frequency, and wavelet-based features are extracted. After determining the features with the highest discriminative power, we classify movement events in epileptic and nonepileptic movements. This classification is only based on a nonparametric estimate of the probability density function of normal movements. Such approach allows us to build patient-specific models to classify movement data without the need for seizure data that are rarely available. If, in the test phase, the probability of a data point (event) is lower than a threshold, this event is considered to be an epileptic seizure; otherwise, it is considered as a normal nocturnal movement event. The mean performance over seven patients gives a sensitivity of 95.24% and a positive predictive value of 60.04%. However, there is a noticeable interpatient difference.

Using Spatio-Temporal Interest Points (STIP) for myoclonic jerk detection in nocturnal video

In this study we introduce a method for detecting myoclonic jerks during the night with video. Using video instead of the traditional method of using EEG-electrodes, permits patients to sleep without any attached sensors. This improves the comfort during sleep and it makes long term home monitoring possible. The algorithm for the detection of the seizures is based on spatio-temporal interest points (STIPs), proposed by Ivan Laptev, which is the state-of-the-art in action recognition.We applied this algorithm on a group of patients suffering from myoclonic jerks. With an optimal parameter setting this resulted in a sensitivity of over 75% and a PPV of over 85%, on the patients' combined data.

Ethernet Motion-Sensor Based Alarm System for Epilepsy Monitoring

In several biomedical domains, it would be interesting to monitor subjects over night time using wearable motion sensors and trigger an alarm if a specific movement has been detected by processing the accelerometer readings. In this paper, the authors describe an innovative architecture for such an alarm system in the context of epilepsy monitoring. The main ingredients of the proposed system are wireless motion sensors, a radio-frequency transceiver linked to an Ethernet gateway and an acquisition server that incorporates real-time detection method. This motion analysis system is further integrated in the dataflow of an existing medicalized alarm system and an event is sent to healthcare professionals every time a seizure is detected by the expert system. The EPIMOUV system has been evaluated, during a 6-month period, in a specialized institution with epilepsy pharmaco-resistant residents.