The Role of Wearable Devices in Chronic Disease Monitoring and Patient Care: A Comprehensive Review

Cureus, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 8, 2024

Wearable health devices are becoming vital in chronic disease management because they offer real-time monitoring and personalized care. This review explores their effectiveness challenges across medical fields, including cardiology, respiratory health, neurology, endocrinology, orthopedics, oncology, mental health. A thorough literature search identified studies focusing on wearable devices' impact patient outcomes. In wearables have proven effective for hypertension, detecting arrhythmias, aiding cardiac rehabilitation. these enhance asthma continuous of critical parameters. Neurological applications include seizure detection Parkinson's management, with showing promising results improving technology advances thyroid dysfunction monitoring, fertility tracking, diabetes management. Orthopedic improved postsurgical recovery rehabilitation, while help early complication oncology. Mental benefits anxiety detection, post-traumatic stress disorder reduction through biofeedback. conclusion, transformative potential managing illnesses by enhancing engagement. Despite significant improvements adherence outcomes, data accuracy privacy persist. However, ongoing innovation collaboration, we can all be part the solution to maximize technologies healthcare.

Language: Английский

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Seizure occurrence is linked to multiday cycles in diverse physiological signals

Epilepsia, Journal Year: 2023, Volume and Issue: 64(6), P. 1627 - 1639

Published: April 15, 2023

The factors that influence seizure timing are poorly understood, and unpredictability remains a major cause of disability. Work in chronobiology has shown cyclical physiological phenomena ubiquitous, with daily multiday cycles evident immune, endocrine, metabolic, neurological, cardiovascular function. Additionally, work chronic brain recordings identified risk is linked to activity. Here, we provide the first characterization relationships between modulation diverse set signals, activity, timing.In this cohort study, 14 subjects underwent ambulatory monitoring multimodal wrist-worn sensor (recording heart rate, accelerometry, electrodermal temperature) an implanted responsive neurostimulation system interictal epileptiform abnormalities electrographic seizures). Wavelet filter-Hilbert spectral analyses characterized circadian wearable recordings. Circular statistics assessed physiology.Ten met inclusion criteria. mean recording duration was 232 days. Seven had reliable electroencephalographic detections (mean = 76 Multiday were present all device signals across subjects. Seizure phase locked five (temperature), four (heart phasic activity), three (accelerometry, rate variability, tonic activity) Notably, after regression behavioral covariates from six seven locking residual signal.Seizure associated multiple processes. Chronic can situate rare paroxysmal events, like seizures, within broader context individual. Wearable devices may advance understanding enable personalized time-varying approaches epilepsy care.

Language: Английский

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Prediction and detection of terminal diseases using Internet of Medical Things: A review

Computers in Biology and Medicine, Journal Year: 2025, Volume and Issue: 188, P. 109835 - 109835

Published: Feb. 24, 2025

Language: Английский

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Machine Learning and Artificial Intelligence Applications to Epilepsy: a Review for the Practicing Epileptologist

Current Neurology and Neuroscience Reports, Journal Year: 2023, Volume and Issue: 23(12), P. 869 - 879

Published: Dec. 1, 2023

Language: Английский

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Neural signal data collection and analysis of Percept™ PC BrainSense recordings for thalamic stimulation in epilepsy

Journal of Neural Engineering, Journal Year: 2024, Volume and Issue: 21(1), P. 012001 - 012001

Published: Jan. 11, 2024

Abstract Deep brain stimulation (DBS) using Medtronic’s Percept™ PC implantable pulse generator is FDA-approved for treating Parkinson’s disease (PD), essential tremor, dystonia, obsessive compulsive disorder, and epilepsy. enables simultaneous recording of neural signals from the same lead used stimulation. Many sensing features were built with PD patients in mind, but these are potentially useful to refine therapies many different processes. When starting our ongoing epilepsy research study, we found it difficult find detailed descriptions about have compiled information multiple sources understand as a tool, particularly use other than those PD. Here provide tutorial scientists physicians interested PC’s examples how time series data often represented saved. We address characteristics recorded discuss hardware software capabilities pre-processing, signal filtering, DBS performance. explain power spectrum shaped by filter response well aliasing due digitally sampling data. present ability extract biomarkers that may be optimize therapy. show differences type affects noise implanted leads seven enrolled clinical trial. has sufficient signal-to-noise ratio, capabilities, stimulus artifact rejection activity recording. Limitations rate, potential artifacts during stimulation, shortening battery life when monitoring at home observed. Despite limitations, demonstrates tool order personalize treatment.

Language: Английский

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The present and future of seizure detection, prediction, and forecasting with machine learning, including the future impact on clinical trials

Frontiers in Neurology, Journal Year: 2024, Volume and Issue: 15

Published: July 11, 2024

Seizures have a profound impact on quality of life and mortality, in part because they can be challenging both to detect forecast. Seizure detection relies upon accurately differentiating transient neurological symptoms caused by abnormal epileptiform activity from similar with different causes. forecasting aims identify when person has high or low likelihood seizure, which is related seizure prediction. Machine learning artificial intelligence are data-driven techniques integrated neurodiagnostic monitoring technologies that attempt accomplish those tasks. In this narrative review, we describe the existing software hardware approaches for forecasting, as well concepts how evaluate performance new future application clinical practice. These include long-term without electroencephalography (EEG) report very sensitivity reduced false positive detections. addition, implications evaluation novel treatments seizures within trials. Based these data, machine could fundamentally change care people seizures, but there multiple validation steps necessary rigorously demonstrate their benefits costs, relative current standard.

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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Seizure forecasting: Bifurcations in the long and winding road

Epilepsia, Journal Year: 2022, Volume and Issue: 64(S4)

Published: May 23, 2022

To date, the unpredictability of seizures remains a source suffering for people with epilepsy, motivating decades research into methods to forecast seizures. Originally, only few scientists and neurologists ventured this niche endeavor, which, given difficulty task, soon turned long winding road. Over past decade, however, our narrow field has seen major acceleration, trials chronic electroencephalographic devices subsequent discovery cyclical patterns in occurrence Now, burgeoning science seizure timing is emerging, which turn informs best forecasting strategies upcoming clinical trials. Although finish line might be view, many challenges remain make reality. This review covers most recent scientific, technical, medical developments, discusses methodology detail, sets number goals future studies.

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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Resting-state background features demonstrate multidien cycles in long-term EEG device recordings

Brain stimulation, Journal Year: 2023, Volume and Issue: 16(6), P. 1709 - 1718

Published: Nov. 1, 2023

Longitudinal EEG recorded by implanted devices is critical for understanding and managing epilepsy. Recent research reports patient-specific, multi-day cycles in device-detected epileptiform events that coincide with increased likelihood of clinical seizures. Understanding these could elucidate mechanisms generating seizures advance drug neurostimulation therapies.

Language: Английский

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