Personalized strategies of neurostimulation: from static biomarkers to dynamic closed-loop assessment of neural function

Frontiers in Neuroscience, Journal Year: 2024, Volume and Issue: 18

Published: March 7, 2024

Despite considerable advancement of first choice treatment (pharmacological, physical therapy, etc.) over many decades, neurological disorders still represent a major portion the worldwide disease burden. Particularly concerning, trend is that this scenario will worsen given an ever expanding and aging population. The different methods brain stimulation (electrical, magnetic, are, on other hand, one most promising alternatives to mitigate suffering patients families when conventional fall short delivering efficacious treatment. With applications in virtually all conditions, neurostimulation has seen success providing relief symptoms. On large variability therapeutic outcomes also been observed, particularly usage non-invasive (NIBS) modalities. Borrowing inspiration concepts from its pharmacological counterpart empowered by unprecedented neurotechnological advancement, field recent years widespread aimed at personalization parameters, based biomarkers individuals being treated. rationale that, taking into account important factors influencing outcome, personalized can yield much-improved therapy. Here, we review literature delineate state-of-the-art stimulation, while considering aspects type informing parameter (anatomy, function, hybrid), invasiveness, level development (pre-clinical experimentation versus clinical trials). Moreover, reviewing relevant closed loop neuroengineering solutions general activity dependent method particular, put forward idea improved may be achieved able track real time dynamics adjust parameters accordingly. We conclude such approaches have great potential promoting recovery lost functions enhance quality life for patients.

Language: Английский

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Removing artefacts and periodically retraining improve performance of neural network-based seizure prediction models

Scientific Reports, Journal Year: 2023, Volume and Issue: 13(1)

Published: April 11, 2023

Abstract The development of seizure prediction models is often based on long-term scalp electroencephalograms (EEGs) since they capture brain electrical activity, are non-invasive, and come at a relatively low-cost. However, suffer from major shortcomings. First, EEG usually highly contaminated with artefacts. Second, changes in the signal over long intervals, known as concept drift, neglected. We evaluate influence these problems deep neural networks using time series shallow widely-used features. Our patient-specific were tested 1577 hours continuous EEG, containing 91 seizures 41 patients temporal lobe epilepsy who undergoing pre-surgical monitoring. results showed that cleaning data, previously developed artefact removal method convolutional networks, improved performance. also found retraining reduced false predictions. Furthermore, show although processing less susceptible to alarms, may need more data surpass feature-based methods. These findings highlight importance robust denoising periodic adaptation models.

Language: Английский

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Prospective validation of a seizure diary forecasting falls short

Epilepsia, Journal Year: 2024, Volume and Issue: 65(6), P. 1730 - 1736

Published: April 12, 2024

Recently, a deep learning artificial intelligence (AI) model forecasted seizure risk using retrospective diaries with higher accuracy than random forecasts. The present study sought to prospectively evaluate the same algorithm.

Language: Английский

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Forecasting seizure likelihood from cycles of self-reported events and heart rate: a prospective pilot study

EBioMedicine, Journal Year: 2023, Volume and Issue: 93, P. 104656 - 104656

Published: June 16, 2023

BackgroundSeizure risk forecasting could reduce injuries and even deaths in people with epilepsy. There is great interest using non-invasive wearable devices to generate forecasts of seizure risk. Forecasts based on cycles epileptic activity, times or heart rate have provided promising results. This study validates a method multimodal recorded from devices.MethodSeizure were extracted 13 participants. The mean period data smartwatch was 562 days, 125 self-reported seizures smartphone app. relationship between onset time phases investigated. An additive regression model used project cycles. results cycles, combination both compared. Forecasting performance evaluated 6 participants prospective setting, long-term collected after algorithms developed.FindingsThe showed that the best achieved area under receiver-operating characteristic curve (AUC) 0.73 for 9/13 showing above chance during retrospective validation. Subject-specific AUC 0.77 4/6 chance.InterpretationThe this demonstrate detected can be combined within single, scalable algorithm provide robust performance. presented enabled estimated an arbitrary future generalised across range types. In contrast earlier work, current prospectively, subjects blinded their outputs, representing critical step towards clinical applications.FundingThis funded by Australian Government National Health & Medical Research Council BioMedTech Horizons grant. also received support Epilepsy Foundation America's 'My Seizure Gauge'

Language: Английский

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Comparison between epileptic seizure prediction and forecasting based on machine learning

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: March 7, 2024

Epilepsy affects around 1% of the population worldwide. Anti-epileptic drugs are an excellent option for controlling seizure occurrence but do not work one-third patients. Warning devices employing prediction or forecasting algorithms could bring patients new-found comfort and quality life. These would attempt to detect a seizure's preictal period, transitional moment between regular brain activity seizure, relay this information user. Over years, many studies using Electroencephalogram-based methodologies have been developed, triggering alarm when detecting period. Recent suggested shift in view from forecasting. Seizure takes probabilistic approach problem question instead crisp prediction. In field study, triggered symbolize detection period is substituted by constant risk assessment analysis. The present aims explore capable establish comparison with results. Using 40 EPILEPSIAE database, we developed several patient-specific different classifiers (a Logistic Regression, 15 Support Vector Machines ensemble, Shallow Neural Networks ensemble). Results show increase sensitivity relative up 146% number that displayed improvement over chance 300%. results suggest methodology may be more suitable warning than one.

Language: Английский

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Seizure forecasting: Where do we stand?

Epilepsia, Journal Year: 2023, Volume and Issue: 64(S3)

Published: Feb. 13, 2023

A lot of mileage has been made recently on the long and winding road toward seizure forecasting. Here we briefly review some selected milestones passed along way, which were discussed at International Conference for Technology Analysis Seizures-ICTALS 2022-convened University Bern, Switzerland. Major impetus was gained from wearable implantable devices that record not only electroencephalography, but also data motor behavior, acoustic signals, various signals autonomic nervous system. This multimodal monitoring can be performed ultralong timescales covering months or years. Accordingly, features metrics extracted these now assess dynamics with a greater degree completeness. Most prominently, this allowed confirmation long-suspected cyclical nature interictal epileptiform activity, risk, seizures. The cover daily, multi-day, yearly cycles. Progress fueled by approaches originating interdisciplinary field network science. Considering epilepsy as large-scale disorder yielded novel perspectives pre-ictal evolving epileptic brain. In addition to discrete predictions will take place in specified prediction horizon, community broadened scope probabilistic forecasts risk continuously time. shift gears triggered incorporation additional quantify performance forecasting algorithms, should compared chance constrained stochastic null models. An imminent task utmost importance is find optimal ways communicate output seizure-forecasting algorithms patients, caretakers, clinicians, so they have socioeconomic impact improve patients' well-being.

Language: Английский

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User experience of a seizure risk forecasting app: A mixed methods investigation

Epilepsy & Behavior, Journal Year: 2024, Volume and Issue: 157, P. 109876 - 109876

Published: June 7, 2024

Over recent years, there has been a growing interest in exploring the utility of seizure risk forecasting, particularly how it could improve quality life for people living with epilepsy. This study reports on user experiences and perspectives forecaster app, as well potential impact mood adjustment to

Language: Английский

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Seizure prediction and forecasting: a scoping review

Current Opinion in Neurology, Journal Year: 2025, Volume and Issue: 38(2), P. 135 - 139

Published: Jan. 20, 2025

This scoping review summarizes key developments in the field of seizure forecasting. Developments have been made along several modalities forecasting, including long term intracranial and subcutaneous encephalogram, wearable physiologic monitoring, diaries. However, clinical translation these tools is limited by various factors. One lack validation on an external dataset. Moreover, widespread practice comparing models to a chance forecaster may be inadequate. Instead, model should able at least surpass moving average forecaster, which serves as 'napkin test' (i.e., can computed back napkin). The impact frequency performance also accounted for when across studies. Surprisingly, despite potential poor quality forecasts, some individuals with epilepsy still want access imprecise forecasts even alter their behavior based upon them. Promising advances development but current not yet overcome hurdles. Future studies will need address potentially dangerous patient behaviors well account validation, napkin test, dependent metrics.

Language: Английский

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The spectrum of indications for ultralong-term EEG monitoring

Seizure, Journal Year: 2024, Volume and Issue: 121, P. 262 - 270

Published: Aug. 23, 2024

We assessed clinical cases to investigate the spectrum of indications for ultra-longterm EEG monitoring using a subcutaneous implantable device in adult patients with focal epilepsy.

Language: Английский

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Real-world epilepsy monitoring with ultra long-term subcutaneous EEG: a 15-month prospective study

medRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 18, 2024

Novel subcutaneous electroencephalography (sqEEG) systems enable prolonged, near-continuous cerebral monitoring in real-world conditions. Nevertheless, the feasibility, acceptability and overall clinical utility of these remains unclear. We report on longest observational study using ultra long-term sqEEG to date.

Language: Английский

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