Real-time automated detection of clonic seizures in newborns

Pisani, Francesco; Spagnoli, Carlotta; Pavlidis, Elena; Facini, Carlotta; Ntonfo, Guy Mathurin Kouamou; Ferrari, Gianluigi; Raheli, R.

doi:10.1016/j.clinph.2013.12.119

Cited by 29 publications

(30 citation statements)

References 21 publications

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“…In another study, colored pyjamas were used to facilitate movement quantification for CS detection . Compared to other algorithms targeted on periodicity, the detection delay we used (2 seconds) is shorter than generally applied (10 seconds), while maintaining a low false detection rate. This can be attributed to the application of spectral contrast (opposed to power) and the output‐smoothening effect of the 4‐second calculation window.…”

Section: Discussionmentioning

confidence: 99%

“…Most remote CS detection methods reported in literature are, like ours, targeted on movement periodicity (the exception being methods targeted on seizure sounds 23,24 and muscle activity [25][26][27] ). CS have been detected in video recordings by calculating periodicity in the luminance signal [10][11][12] and with neural networks trained on optical flow motion tracking output. 8,9 In another study, colored pyjamas were used to facilitate movement quantification for CS detection.…”

Section: Discussionmentioning

confidence: 99%

“…A number of studies have been performed on detecting CS using video recordings. [8][9][10][11][12][13][14] These studies were, however, proofs of principle, showing detection feasibility in small datasets recorded in controlled clinical settings. There is currently no working system available that has been shown to have good performance in real life settings.…”

mentioning

confidence: 94%

“…Such a system would be privacy‐friendly as there is no need for video storage or for someone to monitor output. A number of studies have been performed on detecting CS using video recordings . These studies were, however, proofs of principle, showing detection feasibility in small datasets recorded in controlled clinical settings.…”

Section: Introductionmentioning

confidence: 99%

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Automated video‐based detection of nocturnal convulsive seizures in a residential care setting

et al. 2018

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People with epilepsy need assistance and are at risk of sudden death when having convulsive seizures (CS). Automated real-time seizure detection systems can help alert caregivers, but wearable sensors are not always tolerated. We determined algorithm settings and investigated detection performance of a video algorithm to detect CS in a residential care setting. The algorithm calculates power in the 2-6 Hz range relative to 0.5-12.5 Hz range in group velocity signals derived from video-sequence optical flow. A detection threshold was found using a training set consisting of video-electroencephalogaphy (EEG) recordings of 72 CS. A test set consisting of 24 full nights of 12 new subjects in residential care and additional recordings of 50 CS selected randomly was used to estimate performance. All data were analyzed retrospectively. The start and end of CS (generalized clonic and tonic-clonic seizures) and other seizures considered desirable to detect (long generalized tonic, hyperkinetic, and other major seizures) were annotated. The detection threshold was set to the value that obtained 97% sensitivity in the training set. Sensitivity, latency, and false detection rate (FDR) per night were calculated in the test set. A seizure was detected when the algorithm output exceeded the threshold continuously for 2 seconds. With the detection threshold determined in the training set, all CS were detected in the test set (100% sensitivity). Latency was ≤10 seconds in 78% of detections. Three/five hyperkinetic and 6/9 other major seizures were detected. Median FDR was 0.78 per night and no false detections occurred in 9/24 nights. Our algorithm could improve safety unobtrusively by automated real-time detection of CS in video registrations, with an acceptable latency and FDR. The algorithm can also detect some other motor seizures requiring assistance.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

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Automated video‐based detection of nocturnal convulsive seizures in a residential care setting

et al. 2018

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“…In addition to detection of such ictal episodes and Alzheimer's patient monitoring, algorithms have been proposed that utilizes analytics from phase synchrony of various brain lobes [96][97][98][99][100][101]. Recent findings of early detection of neurological disorders such as epilepsy, autism, and Alzheimer's disease as well as real-time monitoring of cognitive loads and collaborative learning show promise of BCI technologies as a viable medical tool of next generation [12,46,[102][103][104].…”

Section: Processing Of Brain Signalsmentioning

confidence: 99%

A Brief Review of Brain Signal Monitoring Technologies for BCI Applications: Challenges and Prospects

Morshed¹

2014

J Bioeng Biomed Sci

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Significant strides have been made since 1940s for monitoring brain activities and utilizing the information for diagnosis, therapy, and control of robotic instruments including prosthetics. Monitoring brain activities with brain computer interfacing (BCI) technologies are of recent interest to due to the immense potential for various medical applications, particularly for many neurological disorder patients, and the emergence of technologies suitable for long duration BCI applications. Recent initiatives are geared towards transforming these clinic centric technologies to patient centric technologies by monitoring brain activities in practical settings. This paper briefly reviews current status of these technologies and relevant challenges. The technologies can be broadly classified into non-invasive (EEG, MEG, MRI) and invasive (Microelectrode, ECoG, MEA). Challenges to resolve include neuronal damage, neurotrophicity, usability and comfort.

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Bedside EEG Monitoring in the Neonatal Intensive Care Unit

Hochberg

Berger

2022

Curr Treat Options Peds

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Real-time automated detection of clonic seizures in newborns

Cited by 29 publications

References 21 publications

Automated video‐based detection of nocturnal convulsive seizures in a residential care setting

Automated video‐based detection of nocturnal convulsive seizures in a residential care setting

A Brief Review of Brain Signal Monitoring Technologies for BCI Applications: Challenges and Prospects

Bedside EEG Monitoring in the Neonatal Intensive Care Unit

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