Patient-centered design criteria for wearable seizure detection devices

Patel, Anup D.; Moss, Robert; Rust, Steven W.; Patterson, Jeremy; Strouse, Robert; Gedela, Satyanarayana; Haines, Jesse; Lin, Simon

doi:10.1016/j.yebeh.2016.09.012

Cited by 59 publications

(66 citation statements)

References 49 publications

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“…Several large-scale surveys of patients and caregivers have clearly demonstrated the need for reliable automated seizure detection using wearable devices. [1][2][3][4] Recently, there has been a considerable increase in the number of publications on seizure detection. 5,6 However, the way studies are designed and reported is very heterogeneous and often confusing.…”

Section: Discussionmentioning

confidence: 99%

“…Recent advances in technology and in signal analysis have made seizure detection a realistic goal. Several large‐scale surveys of patients and caregivers have clearly demonstrated the need for reliable automated seizure detection using wearable devices . Recently, there has been a considerable increase in the number of publications on seizure detection .…”

Section: Introductionmentioning

confidence: 99%

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Standards for testing and clinical validation of seizure detection devices

2018

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To increase the quality of studies on seizure detection devices, we propose standards for testing and clinical validation of such devices. We identified 4 key features that are important for studies on seizure detection devices: subjects, recordings, data analysis and alarms, and reference standard. For each of these features, we list the specific aspects that need to be addressed in the studies, and depending on these, studies are classified into 5 phases (0-4). We propose a set of outcome measures that need to be reported, and we propose standards for reporting the results. These standards will help in designing and reporting studies on seizure detection devices, they will give readers clear information on the level of evidence provided by the studies, and they will help regulatory bodies in assessing the quality of the validation studies. These standards are flexible, allowing classification of the studies into one of the 5 phases. We propose actions that can facilitate development of novel methods and devices.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Standards for testing and clinical validation of seizure detection devices

2018

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“…Wavelet analysis showed specific dynamic evolution ( Figure 1) of the TCS, characterized by changes in the low-frequency (LF) wavelet component (2)(3)(4)(5)(6)(7)(8) and the high-frequency (HF) wavelet component (64-256 Hz). 18 These changes determined the following phases: toniconset, tonic-maintenance and clonic.…”

Section: Dynamic Changes Of Quantitative Emg During Tcsmentioning

confidence: 99%

“…This contributes to social isolation and decreased quality of life. There is a well‐documented need for wearable seizure detection devices . Bilateral or generalized tonic–clonic seizures (TCS) may lead to injuries, and they constitute a primary risk factor for sudden unexpected death in epilepsy (SUDEP), especially when patients are unattended .…”

Section: Introductionmentioning

confidence: 99%

“…There is a well-documented need for wearable seizure detection devices. [2][3][4][5][6][7][8] Bilateral or generalized tonic-clonic seizures (TCS) may lead to injuries, and they constitute a primary risk factor for sudden unexpected death in epilepsy (SUDEP), especially when patients are unattended. 9,10 Wearable seizure detection devices have been suggested as a tool to prevent SUDEP, 1,9,10 and a statement of research need on epilepsy deaths from UK research teams in 2014 encouraged development of seizure detection devices that may prevent SUDEP.…”

Section: Introductionmentioning

confidence: 99%

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Detection of convulsive seizures using surface electromyography

2018

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Bilateral (generalized) tonic-clonic seizures (TCS) increase the risk of sudden unexpected death in epilepsy (SUDEP), especially when patients are unattended. In sleep, TCS often remain unnoticed, which can result in suboptimal treatment decisions. There is a need for automated detection of these major epileptic seizures, using wearable devices. Quantitative surface electromyography (EMG) changes are specific for TCS and characterized by a dynamic evolution of low- and high-frequency signal components. Algorithms targeting increase in high-frequency EMG signals constitute biomarkers of TCS; they can be used both for seizure detection and for differentiating TCS from convulsive nonepileptic seizures. Two large-scale, blinded, prospective studies demonstrated the accuracy of wearable EMG devices for detecting TCS with high sensitivity (76%-100%). The rate of false alarms (0.7-2.5/24 h) needs further improvement. This article summarizes the pathophysiology of muscle activation during convulsive seizures and reviews the published evidence on the accuracy of EMG-based seizure detection.

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Enablers for IoT Regarding Wearable Medical Devices to Support Healthy Living: The Five Facets

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Yıldırım

2021

IoT in Healthcare and Ambient Assisted Living

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Patient-centered design criteria for wearable seizure detection devices

Cited by 59 publications

References 49 publications

Standards for testing and clinical validation of seizure detection devices

Standards for testing and clinical validation of seizure detection devices

Detection of convulsive seizures using surface electromyography

Enablers for IoT Regarding Wearable Medical Devices to Support Healthy Living: The Five Facets

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