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Self-Supervised Electroencephalogram Representation Learning for Automatic Sleep Staging: Model Development and Evaluation Study

Self-Supervised Electroencephalogram Representation Learning for Automatic Sleep Staging: Model Development and Evaluation Study

Deep learning models have shown great success in automating tasks in sleep medicine by learning from high-quality labeled electroencephalogram (EEG) data [1]. EEG data are collected from patients wearing clinical sensors, which generate real-time multimodal signal data. A common challenge in classifying physiological signals, including EEG signals, is the lack of enough high-quality labels.

Chaoqi Yang, Cao Xiao, M Brandon Westover, Jimeng Sun

JMIR AI 2023;2:e46769

Accuracy of Machine Learning in Detecting Pediatric Epileptic Seizures: Systematic Review and Meta-Analysis

Accuracy of Machine Learning in Detecting Pediatric Epileptic Seizures: Systematic Review and Meta-Analysis

Reference 12: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram Reference 33: Leveraging electronic medical record-embedded standardised electroencephalogram reporting 46: An evaluation of quantum neural networks in the detection of epileptic seizures in the neonatal electroencephalogramelectroencephalogram

Zhuan Zou, Bin Chen, Dongqiong Xiao, Fajuan Tang, Xihong Li

J Med Internet Res 2024;26:e55986

Automated Sleep Stages Classification Using Convolutional Neural Network From Raw and Time-Frequency Electroencephalogram Signals: Systematic Evaluation Study

Automated Sleep Stages Classification Using Convolutional Neural Network From Raw and Time-Frequency Electroencephalogram Signals: Systematic Evaluation Study

PSG sleep staging requires visual inspection of electroencephalogram (EEG), electromyogram, and electrooculogram data, which is time-consuming and labor-intensive, that is, trained technicians may spend hours manually scoring a single night of sleep [1,2]. Thus, the resultant high cost of PSG makes it an unappealing method for longitudinal or population-based sleep studies. The inter- and intrarater variability of PSG scoring is also a concern in manual sleep stage classification [3-5].

Shahab Haghayegh, Kun Hu, Katie Stone, Susan Redline, Eva Schernhammer

J Med Internet Res 2023;25:e40211

Comparison of Polysomnography, Single-Channel Electroencephalogram, Fitbit, and Sleep Logs in Patients With Psychiatric Disorders: Cross-Sectional Study

Comparison of Polysomnography, Single-Channel Electroencephalogram, Fitbit, and Sleep Logs in Patients With Psychiatric Disorders: Cross-Sectional Study

For instance, home-based single-channel electroencephalogram (EEG)—the Zmachine Insight Plus—provides algorithm-based sleep staging [9]. Validation studies have indicated the high validity for the sleep parameters and stages, as compared with PSG, not only in people with insomnia but also in patients with psychiatric disorders [10,11]. A consumer wearable device, Fitbit, has also been released, and it is increasingly used in various settings.

Keita Kawai, Kunihiro Iwamoto, Seiko Miyata, Ippei Okada, Hiroshige Fujishiro, Akiko Noda, Kazuyuki Nakagome, Norio Ozaki, Masashi Ikeda

J Med Internet Res 2023;25:e51336

A Transcranial Magnetic Stimulation Trigger System for Suppressing Motor-Evoked Potential Fluctuation Using Electroencephalogram Coherence Analysis: Algorithm Development and Validation Study

A Transcranial Magnetic Stimulation Trigger System for Suppressing Motor-Evoked Potential Fluctuation Using Electroencephalogram Coherence Analysis: Algorithm Development and Validation Study

Cortical excitability can be measured using an electroencephalogram (EEG) [11]. The similarity of the measured EEG is calculated using coherence analysis, which is a method for calculating the correlation between 2 EEG signals in the frequency domain. We hypothesized that the fluctuation of MEP amplitude must be suppressed when TMS is delivered to the M1 at a time when the electroencephalograms of 2 channels measured on the scalp are highly similar in the frequency domain.

Keisuke Sasaki, Yuki Fujishige, Yutaka Kikuchi, Masato Odagaki

JMIR Biomed Eng 2021;6(2):e28902

Advancing Posttraumatic Stress Disorder Diagnosis and the Treatment of Trauma in Humanitarian Emergencies via Mobile Health: Protocol for a Proof-of-Concept Nonrandomized Controlled Trial

Advancing Posttraumatic Stress Disorder Diagnosis and the Treatment of Trauma in Humanitarian Emergencies via Mobile Health: Protocol for a Proof-of-Concept Nonrandomized Controlled Trial

CANTAB: Cambridge Neuropsychological Test Automated Battery; CAPS-5: Clinician-Administered Posttraumatic Stress Disorder Scale for the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition; EEG: electroencephalogram; HTQ-4: Harvard Trauma Questionnaire-4; PTSD: posttraumatic stress disorder; SCID-5-RV: Structured Clinical Interview for the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, Research Version; SUDS: Subjective Units of Distress Scale; TBI: traumatic brain injury

Janaina V Pinto, Caroline Hunt, Brian O'Toole

JMIR Res Protoc 2022;11(6):e38223

Development of Cognitive and Physical Exercise Systems, Clinical Recordings, Large-Scale Data Analytics, and Virtual Coaching for Heart Failure Patients: Protocol for the BioTechCOACH-ForALL Project

Development of Cognitive and Physical Exercise Systems, Clinical Recordings, Large-Scale Data Analytics, and Virtual Coaching for Heart Failure Patients: Protocol for the BioTechCOACH-ForALL Project

activity level regarding the doctor’s recommendation and readjust e-coaching system parameters (home study phases 1 and 2) Development of an e-coaching system (home study phase 3) based on neuroscience evidence (lab study), incorporating exergaming [30] and remote health monitoring [29] techniques Patient engagement with different user interface interaction means, such as virtual projected coaches with different characteristics (presence/absence of medical uniform, gender, age) will be explored by means of electroencephalogram

Antonis Billis, Niki Pandria, Sophia-Anastasia Mouratoglou, Evdokimos Konstantinidis, Panagiotis Bamidis

JMIR Res Protoc 2020;9(5):e17714

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