Spectral Analysis of Acceleration Data for Detection of Generalized Tonic-Clonic Seizures

Joo, Hyo Sung; Han, Su‐Hyun; Lee, Jongshill; Jang, Dong Pyo; Kang, Joong Koo; Woo, Jeong-Soo

doi:10.3390/s17030481

Cited by 12 publications

(8 citation statements)

References 20 publications

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“…15,16 A correlation was also reported between PGES and the duration of the clonic phase in one study, 13 but not in others. 11,12,14 Most of the preceding PGES-associated variables can be readily measured by existing sensors incorporated into commercially available GTCS-detection devices, such as accelerometers [17][18][19] and electromyography (EMG) 20 for postictal immobility and duration of tonic or clonic phases, and skin conductance sensors for EDA. 21,22 The distinction of the various types of GTCS with such devices also appears feasible, although this remains to be demonstrated.…”

Section: And Mechanisms Of Sudepmentioning

confidence: 99%

Wearable devices for sudden unexpected death in epilepsy prevention

et al. 2018

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Sudden unexpected death in epilepsy (SUDEP) is most often associated with the occurrence of generalized tonic-clonic seizures (GTCS), a seizure type that can now be detected with high sensitivity and specificity by wearable or bed devices. The recent development in such devices and their performance offer multiple opportunities to tackle SUDEP and its prevention. Reliable GTCS detection might help physicians optimize antiepileptic treatment, which could in turn reduce the risk of SUDEP. GTCS-triggered alarms can lead to immediate intervention by caregivers that are also likely to decrease the odd of SUDEP. The biosignals used to detect GTCS might provide novel SUDEP biomarkers, in particular, by informing on several important characteristics of the ictal and postictal periods (type of GTCS, duration of tonic phase, rotation in the prone position, presence and duration of postictal immobility and bradycardia, rise in electrodermal activity). Other biosensors not yet used for detecting GTCS might provide complementary information, such as the presence and intensity of ictal/postictal hypoxemia. The above biomarkers, if strongly predictive, could help identify patients at very high risk of SUDEP, enabling better assessment of individual risk, as well as selection of appropriate patients for clinical studies aiming at preventing SUDEP. The same biosignals could also be used as ancillary biomarkers to test the impact of various interventions before moving to highly challenging randomized controlled trials with SUDEP as a primary outcome.

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Section: And Mechanisms Of Sudepmentioning

confidence: 99%

Wearable devices for sudden unexpected death in epilepsy prevention

et al. 2018

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“…Other types of sensors located elsewhere on the body have also been investigated for GTCS detection. Spectral analysis of accelerometry (ACM) from wrist-worn sensor was used to detect 10/10 GTCS from 5/12 patients monitored for 246 h with a mean false positive ratio of 2/24 h [23]. However, the location of the ear-EEG device has the advantage that it also samples EEG.…”

Section: Discussionmentioning

confidence: 99%

Detection of generalized tonic-clonic seizures from ear-EEG based on EMG analysis

Zibrandtsen

Kidmose

Kjaer

2018

Seizure

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“…In one of the studies, spectral analysis has been used to detect the generalized tonic-clonic seizures (GTCS) in daily life, using the ACM wrist band. 124 It could be helpful in providing early alerts to caregivers to prevent risks associated with GTCS. In Oman, spectral analysis was used as a non-invasive tool to distinguish the preeclamptic pregnant group from the normal.…”

Section: Singular Spectrum Analysismentioning

confidence: 99%

Utilization of Time Series Tools in Life-sciences and Neuroscience

2020

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Time series tools are part and parcel of modern day research. Their usage in the biomedical field; specifically, in neuroscience, has not been previously quantified. A quantification of trends can tell about lacunae in the current uses and point towards future uses. We evaluated the principles and applications of few classical time series tools, such as Principal Component Analysis, Neural Networks, common Auto-regression Models, Markov Models, Hidden Markov Models, Fourier Analysis, Spectral Analysis, in addition to diverse work, generically lumped under time series category. We quantified the usage from two perspectives, one, information technology professionals’, other, researchers utilizing these tools for biomedical and neuroscience research. For understanding trends from the information technology perspective, we evaluated two of the largest open source question and answer databases of Stack Overflow and Cross Validated. We quantified the trends in their application in the biomedical domain, and specifically neuroscience, by searching literature and application usage on PubMed. While the use of all the time series tools continues to gain popularity in general biomedical and life science research, and also neuroscience, and so have been the total number of questions asked on Stack overflow and Cross Validated, the total views to questions on these are on a decrease in recent years, indicating well established texts, algorithms, and libraries, resulting in engineers not looking for what used to be common questions a few years back. The use of these tools in neuroscience clearly leaves room for improvement.

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Spectral Analysis of Acceleration Data for Detection of Generalized Tonic-Clonic Seizures

Cited by 12 publications

References 20 publications

Wearable devices for sudden unexpected death in epilepsy prevention

Wearable devices for sudden unexpected death in epilepsy prevention

Detection of generalized tonic-clonic seizures from ear-EEG based on EMG analysis

Utilization of Time Series Tools in Life-sciences and Neuroscience

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