Automated seizure detection systems and their effectiveness for each type of seizure

Ulate‐Campos, Adriana; Coughlin, Francesca; Gaínza‐Lein, Marina; Fernández, Iván Sánchez; Pearl, Phillip L.; Loddenkemper, Tobias

doi:10.1016/j.seizure.2016.06.008

Cited by 189 publications

(179 citation statements)

References 132 publications

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“…The EDA might also help detect and characterize seizures with subtle or no motor movement. A recent review suggests that the EDA signal is one of the most promising alternatives for a widespread variety of epileptic episodes 24 . A study with an Empatica E3 wristband reported that 97% of 34 predominantly non-motor seizures could be detected with a hierarchical classifier based on EDA 36 .…”

Section: Limits and Future Workmentioning

confidence: 99%

Multicenter clinical assessment of improved wearable multimodal convulsive seizure detectors

Onorati¹,

Regalia²,

Caborni³

et al. 2017

Epilepsia

170

169

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Running title: Wearable multimodal motor seizure detectors Onorati et al. 2 Summary Objective:New devices are needed for monitoring seizures, especially those associated with sudden unexpected death in epilepsy (SUDEP). They must be unobtrusive, automated and provide false alarm rates bearable in everyday life. This study quantifies the performance of new multimodal wrist-worn convulsive seizure detectors. Methods:Hand-annotated video-electroencephalography seizure events were collected from 69 patients at 6 clinical sites. Three different wristbands were used to record electrodermal activity (EDA) and accelerometer (ACM) signals, obtaining 5,928 hours of data, including 55 convulsive epileptic seizures (6 focal tonic-clonic seizures and 49 focal-to-bilateral-tonicclonic seizures) from 22 patients. Recordings were analyzed off-line to train and test two new machine learning classifiers and a published EDA and ACM-based classifier. Moreover, wristband data were analyzed to estimate seizure-motion duration and autonomic responses. Results:The two novel classifiers consistently outperformed the previous detector. The most efficient (Classifier III) yielded sensitivity of 94.55%, and false alarm rate (FAR) of 0.2 events/day.No nocturnal seizures were missed. Most patients had less than 1 false alarm every 4 days with FAR below their seizure frequency. When increasing the sensitivity to 100% (no missed seizures) the FAR is up to 13 times lower than the previous detector. Furthermore, all detections occurred before the seizure ended, providing reasonable latency (median: 29.3 s, range: 14.8-151 s). Automatically estimated seizure durations were correlated with true durations, enabling reliable annotations. Finally, EDA measurements confirmed the presence of post-ictal autonomic dysfunction, exhibiting a significant rise in 73% of the convulsive seizures. Onorati et al. 3 Significance:The proposed multimodal wrist-worn convulsive seizure detectors provide seizure counts that are more accurate than previous automated detectors and typical patient self-reports, while maintaining a tolerable FAR for ambulatory monitoring. Furthermore, the multimodal system provides an objective description of motor behaviour and autonomic dysfunction, aimed at enriching seizure characterization, with potential utility for SUDEP warning.

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Section: Limits and Future Workmentioning

confidence: 99%

Multicenter clinical assessment of improved wearable multimodal convulsive seizure detectors

Onorati¹,

Regalia²,

Caborni³

et al. 2017

Epilepsia

170

169

View full text Add to dashboard Cite

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“…18,28,29 To evaluate semiology, movements can be analyzed using accelerometry, surface electromyography, video detection systems, mattress sensor, and seizure-alert dogs; physiological signals can be evaluated by electrocardiography, skin temperature, audio classification, and respiratory rate changes techniques. The evaluation of the manifestation of epilepsy is vital to defining the accurate monitoring and treatment procedure.…”

Section: Traditional Approaches To Automatic Epilepsy Evaluationmentioning

confidence: 99%

“…The evaluation of the manifestation of epilepsy is vital to defining the accurate monitoring and treatment procedure. 18 The success of epilepsy evaluation relies especially on evaluating the electroclinical correlation, which allows the SEEG technique, by analyzing the clinical signs recorded by video monitoring and the electrical activity registered by depth electrodes. 18,28,29 To evaluate semiology, movements can be analyzed using accelerometry, surface electromyography, video detection systems, mattress sensor, and seizure-alert dogs; physiological signals can be evaluated by electrocardiography, skin temperature, audio classification, and respiratory rate changes techniques.…”

Section: Traditional Approaches To Automatic Epilepsy Evaluationmentioning

confidence: 99%

Automated analysis of seizure semiology and brain electrical activity in presurgery evaluation of epilepsy: A focused survey

et al. 2017

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Epilepsy being one of the most prevalent neurological disorders, affecting approximately 50 million people worldwide, and with almost 30-40% of patients experiencing partial epilepsy being nonresponsive to medication, epilepsy surgery is widely accepted as an effective therapeutic option. Presurgical evaluation has advanced significantly using noninvasive techniques based on video monitoring, neuroimaging, and electrophysiological and neuropsychological tests; however, certain clinical settings call for invasive intracranial recordings such as stereoelectroencephalography (SEEG), aiming to accurately map the eloquent brain networks involved during a seizure. Most of the current presurgical evaluation procedures focus on semiautomatic techniques, where surgery diagnosis relies immensely on neurologists' experience and their time-consuming subjective interpretation of semiology or the manifestations of epilepsy and their correlation with the brain's electrical activity. Because surgery misdiagnosis reaches a rate of 30%, and more than one-third of all epilepsies are poorly understood, there is an evident keen interest in improving diagnostic precision using computer-based methodologies that in the past few years have shown near-human performance. Among them, deep learning has excelled in many biological and medical applications, but has advanced insufficiently in epilepsy evaluation and automated understanding of neural bases of semiology. In this paper, we systematically review the automatic applications in epilepsy for human motion analysis, brain electrical activity, and the anatomoelectroclinical correlation to attribute anatomical localization of the epileptogenic network to distinctive epilepsy patterns. Notably, recent advances in deep learning techniques will be investigated in the contexts of epilepsy to address the challenges exhibited by traditional machine learning techniques. Finally, we discuss and propose future research on epilepsy surgery assessment that can jointly learn across visually observed semiologic patterns and recorded brain electrical activity.

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“…EEG aside, different biometric signals have been proposed for detecting the epileptic seizures [10,33]: gyroscopes, magnetometers, implanted advisory system, electromyography, ECG, ACM, video detection systems, mattress sensor, and audio classification. BSN placed on the body together with MCC has been proposed for epilepsy detection in [8]; more specifically, the BSN includes an ACM cap, a wrist band including a temperature sensor and an ACM, a moisture sensor, and microphone.…”

Section: Introductionmentioning

confidence: 99%

An IoT Platform for Epilepsy Monitoring and Supervising

et al. 2017

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Epilepsy is a chronic neurological disorder with several different types of seizures, some of them characterized by involuntary recurrent convulsions, which have a great impact on the everyday life of the patients. Several solutions have been proposed in the literature to detect this type of seizures and to monitor the patient; however, these approaches lack in ergonomic issues and in the suitable integration with the health system. This research makes an in-depth analysis of the main factors that an epileptic detection and monitoring tool should accomplish. Furthermore, we introduce the architecture for a specific epilepsy detection and monitoring platform, fulfilling these factors. Special attention has been given to the part of the system the patient should wear, providing details of this part of the platform. Finally, a partial implementation has been deployed and several tests have been proposed and carried out in order to make some design decisions.

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Automated seizure detection systems and their effectiveness for each type of seizure

Cited by 189 publications

References 132 publications

Multicenter clinical assessment of improved wearable multimodal convulsive seizure detectors

Multicenter clinical assessment of improved wearable multimodal convulsive seizure detectors

Automated analysis of seizure semiology and brain electrical activity in presurgery evaluation of epilepsy: A focused survey

An IoT Platform for Epilepsy Monitoring and Supervising

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