Clinical utility of a video/audio-based epilepsy monitoring system Nelli

Basnyat, Pabitra; Mäkinen, Jussi; Saarinen, Jukka T.; Peltola, Jukka

doi:10.1016/j.yebeh.2022.108804

Cited by 10 publications

(4 citation statements)

References 31 publications

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“…One of the major obstacles in the classification and differential diagnosis of suspected epileptic seizures is the patient's inability to accurately describe key features of these events (Mielke et al, 2020). Additionally, the ability to monitor seizure activity over time (Duun-Henriksen et al, 2020) also favors a better understanding of seizure types, frequency, and severity, helping clinicians to better understand the patient's condition and to assess the effect of medical interventions (Basnyat et al, 2022).…”

Section: Discussionmentioning

confidence: 99%

Automated analysis and detection of epileptic seizures in video recordings using artificial intelligence

Rai,

Knight,

Hiillos

et al. 2024

Front. Neuroinform.

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IntroductionAutomated seizure detection promises to aid in the prevention of SUDEP and improve the quality of care by assisting in epilepsy diagnosis and treatment adjustment.MethodsIn this phase 2 exploratory study, the performance of a contactless, marker-free, video-based motor seizure detection system is assessed, considering video recordings of patients (age 0–80 years), in terms of sensitivity, specificity, and Receiver Operating Characteristic (ROC) curves, with respect to video-electroencephalographic monitoring (VEM) as the medical gold standard. Detection performances of five categories of motor epileptic seizures (tonic–clonic, hyperkinetic, tonic, unclassified motor, automatisms) and psychogenic non-epileptic seizures (PNES) with a motor behavioral component lasting for >10 s were assessed independently at different detection thresholds (rather than as a categorical classification problem). A total of 230 patients were recruited in the study, of which 334 in-scope (>10 s) motor seizures (out of 1,114 total seizures) were identified by VEM reported from 81 patients. We analyzed both daytime and nocturnal recordings. The control threshold was evaluated at a range of values to compare the sensitivity (n = 81 subjects with seizures) and false detection rate (FDR) (n = all 230 subjects).ResultsAt optimal thresholds, the performance of seizure groups in terms of sensitivity (CI) and FDR/h (CI): tonic–clonic- 95.2% (82.4, 100%); 0.09 (0.077, 0.103), hyperkinetic- 92.9% (68.5, 98.7%); 0.64 (0.59, 0.69), tonic- 78.3% (64.4, 87.7%); 5.87 (5.51, 6.23), automatism- 86.7% (73.5, 97.7%); 3.34 (3.12, 3.58), unclassified motor seizures- 78% (65.4, 90.4%); 4.81 (4.50, 5.14), and PNES- 97.7% (97.7, 100%); 1.73 (1.61, 1.86). A generic threshold recommended for all motor seizures under study asserted 88% sensitivity and 6.48 FDR/h.DiscussionThese results indicate an achievable performance for major motor seizure detection that is clinically applicable for use as a seizure screening solution in diagnostic workflows.

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Section: Discussionmentioning

confidence: 99%

Automated analysis and detection of epileptic seizures in video recordings using artificial intelligence

Rai,

Knight,

Hiillos

et al. 2024

Front. Neuroinform.

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“…Additionally, the ability to monitor seizure activity over time 24 also favors a better understanding of seizure types, frequency, and severity, helping clinicians to better understand the patient's condition and to assess the effect of medical interventions 25 .…”

Section: Discussionmentioning

confidence: 99%

Automated analysis and detection of epileptic seizures in video recordings using artificial intelligence

Rai¹,

Knight²,

Hiillos³

et al. 2023

Preprint

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Automated seizure detection promises to aid in the prevention of SUDEP and improve the quality of care by assisting in epilepsy diagnosis and treatment adjustment. In this phase 2 validation, the algorithmic model performance of a contactless, marker-free video-based motor seizure detection system is evaluated against video-electroencephalographic monitoring (VEM) from two Danish epilepsy monitoring units. Detection performance of five categories of motor epileptic seizures (tonic-clonic, hyperkinetic, tonic, unclassified motor, automatisms) and psychogenic non-epileptic seizures (PNES) with a motor behavioral component lasting for >10 seconds were assessed. 230 patients were recruited in the validation set, with 340 motor seizures identified by VEM. The control threshold was evaluated at a range of values to compare the sensitivity and false detection rate (FDR). At optimal thresholds, the performance of seizure groups in terms of sensitivity (CI) and FDR/hr (CI): tonic-clonic- 95.2% (77.3%, 99.2%); 0.084 (0.081, 0.11), hyperkinetic- 92.9% (68.5%, 98.7%); 0.62 (0.57, 0.67), tonic- 78.3% (64.4%, 87.7%); 5.81 (5.46, 6.19), automatism- 87% (74.3%, 93.9%); 3.30 (3.08, 3.54), unclassified motor seizures- 77.7% (70.8%, 83.4%); 4.79 (4.48, 5.12), and PNES- 95.6% (85.2%, 98.8%); 1.70 (1.59, 1.83). A generic threshold recommended for all motor seizures under study asserted 87.8% sensitivity and 6.48 FDR/hr. These results indicate the system’s achievable performance at pre-specified thresholds that is clinically applicable for use as a seizure screening solution in diagnostic workflows.

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“…One study investigated the clinical utility of Nelli in a home environment, reporting that clinically relevant events were identified in 80% of participants. This figure was not cross‐referenced with events identified by epileptologists, so results are not directly comparable to other publications, emphasizing the need for standardized performance metrics 88 …”

Section: Clinical Application In Epilepsymentioning

confidence: 94%

“…This figure was not cross-referenced with events identified by epileptologists, so results are not directly comparable to other publications, emphasizing the need for standardized performance metrics. 88…”

Section: Available Devicesmentioning

confidence: 99%

Computer vision for automated seizure detection and classification: A systematic review

Brown,

Boyne,

Hassan

et al. 2024

Epilepsia

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Computer vision (CV) shows increasing promise as an efficient, low‐cost tool for video seizure detection and classification. Here, we provide an overview of the fundamental concepts needed to understand CV and summarize the structure and performance of various model architectures used in video seizure analysis. We conduct a systematic literature review of the PubMed, Embase, and Web of Science databases from January 1, 2000 to September 15, 2023, to identify the strengths and limitations of CV seizure analysis methods and discuss the utility of these models when applied to different clinical seizure phenotypes. Reviews, nonhuman studies, and those with insufficient or poor quality data are excluded from the review. Of the 1942 records identified, 45 meet inclusion criteria and are analyzed. We conclude that the field has shown tremendous growth over the past 2 decades, leading to several model architectures with impressive accuracy and efficiency. The rapid and scalable detection offered by CV models holds the potential to reduce sudden unexpected death in epilepsy and help alleviate resource limitations in epilepsy monitoring units. However, a lack of standardized, thorough validation measures and concerns about patient privacy remain important obstacles for widespread acceptance and adoption. Investigation into the performance of models across varied datasets from clinical and nonclinical environments is an essential area for further research.

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Clinical utility of a video/audio-based epilepsy monitoring system Nelli

Cited by 10 publications

References 31 publications

Automated analysis and detection of epileptic seizures in video recordings using artificial intelligence

Automated analysis and detection of epileptic seizures in video recordings using artificial intelligence

Automated analysis and detection of epileptic seizures in video recordings using artificial intelligence

Computer vision for automated seizure detection and classification: A systematic review

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