A personalized semi-automatic sleep spindle detection (PSASD) framework.
J Neurosci Methods
; 407: 110064, 2024 Jul.
Article
em En
| MEDLINE
| ID: mdl-38301832
ABSTRACT
BACKGROUND:
Sleep spindles are distinct electroencephalogram (EEG) patterns of brain activity that have been posited to play a critical role in development, learning, and neurological disorders. Manual scoring for sleep spindles is labor-intensive and tedious but could supplement automated algorithms to resolve challenges posed with either approaches alone. NEWMETHODS:
A Personalized Semi-Automatic Sleep Spindle Detection (PSASD) framework was developed to combine the strength of automated detection algorithms and visual expertise of human scorers. The underlying model in the PSASD framework assumes a generative model for EEG sleep spindles as oscillatory components, optimized to EEG amplitude, with remaining signals distributed into transient and low-frequency components.RESULTS:
A single graphical user interface (GUI) allows both manual scoring of sleep spindles (model training data) and verification of automatically detected spindles. A grid search approach allows optimization of parameters to balance tradeoffs between precision and recall measures. COMPARISON WITH EXISTINGMETHODS:
PSASD outperformed DETOKS in F1-score by 19% and 4% on the DREAMS and P-DROWS-E datasets, respectively. It also outperformed YASA in F1-score by 25% in the P-DROWS-E dataset. Further benchmarking analysis showed that PSASD outperformed four additional widely used sleep spindle detectors in F1-score in the P-DROWS-E dataset. Titration analysis revealed that four 30-second epochs are sufficient to fine-tune the model parameters of PSASD. Associations of frequency, duration, and amplitude of detected sleep spindles matched those previously reported with automated approaches.CONCLUSIONS:
Overall, PSASD improves detection of sleep spindles in EEG data acquired from both younger healthy and older adult patient populations.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Fases do Sono
/
Eletroencefalografia
Tipo de estudo:
Diagnostic_studies
/
Prognostic_studies
Limite:
Adult
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Aged
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Female
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Humans
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Male
/
Middle aged
Idioma:
En
Revista:
J Neurosci Methods
Ano de publicação:
2024
Tipo de documento:
Article