Towards a wearable multi-modal seizure detection system in epilepsy: A pilot study

Nielsen, Joachim; Zibrandtsen, Ivan C; Masulli, Paolo; Sørensen, Torben Lykke; Andersen, T; Kjær, Troels W.

doi:10.1016/j.clinph.2022.01.005

Cited by 23 publications

(35 citation statements)

References 32 publications

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“…For focal TS the results were best for ACM, for generalized TS BVP. Nielsen et al assessed a multimodal device consisting of behind‐the‐ear EEG, ACM, and ECG 15 . One patient had TS.…”

Section: Discussionmentioning

confidence: 99%

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Wearable detection of tonic seizures in childhood epilepsy: An exploratory cohort study

Proost,

Macea,

Lagae

et al. 2023

Epilepsia

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ObjectiveTo investigate the performance of a multimodal wearable device for the offline detection of tonic seizures (TS) in a pediatric childhood epilepsy cohort, with a focus on patients with Lennox–Gastaut syndrome.MethodsParallel with prolonged video‐electroencephalography (EEG), the Plug 'n Patch system, a multimodal wearable device using the Sensor Dot and replaceable electrode adhesives, was used to detect TS. Multiple biosignals were recorded: behind‐the‐ear EEG, surface electromyography, electrocardiography, and accelerometer/gyroscope. Biosignals were annotated blindly by a neurologist. Seizure characteristics were described, and performance was assessed by sensitivity, positive predictive value (PPV), F1 score, and false alarm rate (FAR) per hour. Performance was compared to seizure diaries kept by the caretaker.ResultsNinety‐nine TS were detected in 13 patients. Seven patients (54%) had Lennox–Gastaut syndrome and six patients (46%) had other forms of (developmental) epileptic encephalopathies or drug‐resistant epilepsy. All but one patient had intellectual disability. Overall sensitivity was 41%, with a PPV of 9%, an F1 score of 14%, and a median FAR per hour of 0.75. Performance increased to an F1 score of 66% for nightly seizures lasting at least 10 s (sensitivity 66%, PPV 66%) and 71% for nightly seizures lasting at least 20 s (sensitivity 62%, PPV 82%). For these seizures there were no false alarms in 10 of 13 patients. Sensitivity of seizure diaries reached a maximum of 52% for prolonged (≥20 s) nightly seizures, even though caretakers slept in the same room.SignificanceWe showed that it is feasible to use a multimodal wearable device with multiple adhesive sites in children with epilepsy and intellectual disability. For prolonged nightly seizures, offline manual detection of TS outperformed seizure diaries. The recognition of seizure‐specific signatures using multiple modalities can help in the development of automated TS detection algorithms.

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“…For focal TS the results were best for ACM, for generalized TS BVP. Nielsen et al assessed a multimodal device consisting of behind‐the‐ear EEG, ACM, and ECG 15 . One patient had TS.…”

Section: Discussionmentioning

confidence: 99%

“…Nielsen et al assessed a multimodal device consisting of behind-the-ear EEG, ACM, and ECG. 15 One patient had TS. Their support vector machine classifier reached a sensitivity of 84% in seizures of at least 25 s, with a FAR of 0.33/24 h.…”

Section: Seizure-detection Performancementioning

confidence: 99%

Wearable detection of tonic seizures in childhood epilepsy: An exploratory cohort study

Proost,

Macea,

Lagae

et al. 2023

Epilepsia

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“…However, the epileptic seizure is a complex process with changes in multiple physiological parameters, and data from a single modality will lose information about seizure to a certain extent, limiting the performance of the algorithm. Compared with single‐modal signals, multimodal signals have certain advantages in the sensitivity and FDR 63,71 . In addition, the simple threshold method commonly used to classify single‐modal signal has basically not appeared in the research of multimodal signals, and replaced by various classic machine learning algorithms, especially deep learning.…”

Section: The Current State In the Field Of Seizure Detection Based On...mentioning

confidence: 99%

“…algorithm. Compared with single-modal signals, multimodal signals have certain advantages in the sensitivity and FDR 63,71. In addition, the simple threshold method commonly used to classify single-modal signal has basically not appeared in the research of multimodal signals, and replaced by various classic machine learning algorithms, especially deep learning.…”

mentioning

confidence: 99%

Seizure detection based on wearable devices: A review of device, mechanism, and algorithm

Wang

Lei

et al. 2022

Acta Neuro Scandinavica

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With sudden and unpredictable nature, seizures lead to great risk of the secondary damage, status epilepticus, and sudden unexpected death in epilepsy. Thus, it is essential to use a wearable device to detect seizure and inform patients' caregivers for assistant to prevent or relieve adverse consequence. In this review, we gave an account of the current state of the field of seizure detection based on wearable devices from three parts: devices, physiological activities, and algorithms. Firstly, seizure monitoring devices available in the market primarily involve wristband‐type devices, patch‐type devices, and armband‐type devices, which are able to detect motor seizures, focal autonomic seizures, or absence seizures. Secondly, seizure‐related physiological activities involve the discharge of brain neurons presented, autonomous nervous activities, and motor. Plenty of studies focus on features from one signal, while it is a lack of evidences about the change of signal coupling along with seizures. Thirdly, the seizure detection algorithms developed from simple threshold method to complicated machine learning and deep learning, aiming at distinguish seizures from normal events. After understanding of some preliminary studies, we will propose our own thought for future development in this field.

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“…Gaetano Zazzaro and Luigi Pavone evaluate the performance of a seizure detection system by studying its performance in correctly identifying seizures and in minimizing false alarms and to decide if it is generalizable to several patients [146]. In [74] explore the possibilities of wearable multimodal monitoring in epilepsy and identify effective strategies for seizure-detection.…”

Section: Introductionmentioning

confidence: 99%

EEG seizure detection: concepts, techniques, challenges, and future trends

Dessouky

El‐Sayed

Hemdan

2023

Multimed Tools Appl

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A central nervous system disorder is usually referred to as epilepsy. In epilepsy brain activity becomes abnormal, leading to times of abnormal behavior or seizures, and at times loss of awareness. Consequently, epilepsy patients face problems in daily life due to precautions they must take to adapt to this condition, particularly when they use heavy equipment, e.g., vehicle derivation. Epilepsy studies rely primarily on electroencephalography (EEG) signals to evaluate brain activity during seizures. It is troublesome and timeconsuming to manually decide the location of seizures in EEG signals. The automatic detection framework is one of the principal tools to help doctors and patients take appropriate precautions. This paper reviews the epilepsy mentality disorder and the types of seizure, preprocessing operations that are performed on EEG data, a generally extracted feature from the signal, and a detailed view on classification procedures used in this problem and provide insights on the difficulties and future research directions in this innovative theme. Therefore, this paper presents a review of work on recent methods for the epileptic seizure process along with providing perspectives and concepts to researchers to present an automated EEG-based epileptic seizure detection system using IoT and machine learning classifiers for remote patient monitoring in the context of smart Multimedia Tools and Applications

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Towards a wearable multi-modal seizure detection system in epilepsy: A pilot study

Cited by 23 publications

References 32 publications

Wearable detection of tonic seizures in childhood epilepsy: An exploratory cohort study

Wearable detection of tonic seizures in childhood epilepsy: An exploratory cohort study

Seizure detection based on wearable devices: A review of device, mechanism, and algorithm

EEG seizure detection: concepts, techniques, challenges, and future trends

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