A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Automatic sleep scoring : a deep learning architecture for multi-modality time series (2021)
Yan, R., Li, F., Zhou, D.D., Ristaniemi, T., & Cong, F. (2021). Automatic sleep scoring : a deep learning architecture for multi-modality time series. Journal of Neuroscience Methods, 348, Article 108971. https://doi.org/10.1016/j.jneumeth.2020.108971
JYU-tekijät tai -toimittajat
Julkaisun tiedot
Julkaisun kaikki tekijät tai toimittajat: Yan, Rui; Li, Fan; Zhou, Dong Dong; Ristaniemi, Tapani; Cong, Fengyu
Lehti tai sarja: Journal of Neuroscience Methods
ISSN: 0165-0270
eISSN: 1872-678X
Julkaisuvuosi: 2021
Volyymi: 348
Artikkelinumero: 108971
Kustantaja: Elsevier
Julkaisumaa: Alankomaat
Julkaisun kieli: englanti
DOI: https://doi.org/10.1016/j.jneumeth.2020.108971
Julkaisun avoin saatavuus: Ei avoin
Julkaisukanavan avoin saatavuus:
Julkaisu on rinnakkaistallennettu (JYX): https://jyx.jyu.fi/handle/123456789/73831
Tiivistelmä
Method: The model adopts a linear function to address different numbers of inputs, thereby extending model applications. Two-dimensional convolution neural networks are used to learn features from multi-modality polysomnographic signals, a “squeeze and excitation” block to recalibrate channel-wise features, together with a long short-term memory module to exploit long-range contextual relation. The learnt features are finally fed to the decision layer to generate predictions for sleep stages.
Result: Model performance is evaluated on three public datasets. For all tasks with different available channels, our model achieves outstanding performance not only on healthy subjects but even on patients with sleep disorders (SHHS: Acc-0.87, K-0.81; ISRUC: Acc-0.86, K-0.82; Sleep-EDF: Acc-0.86, K-0.81). The highest classification accuracy is achieved by a fusion of multiple polysomnographic signals.
Comparison: Compared to state-of-the-art methods that use the same dataset, the proposed model achieves a comparable or better performance, and exhibits low computational cost.
Conclusions: The model demonstrates its transferability among different datasets, without changing model architecture or hyper-parameters across tasks. Good model transferability promotes the application of transfer learning on small group studies with mismatched channels. Due to demonstrated availability and versatility, the proposed method can be integrated with diverse polysomnography systems, thereby facilitating sleep monitoring in clinical or routine care.
YSO-asiasanat: uni (lepotila); unitutkimus; signaalianalyysi; signaalinkäsittely; koneoppiminen
Vapaat asiasanat: polysomnography; automatic sleep scoring; multi-modality analysis; deep learning
Liittyvät organisaatiot
OKM-raportointi: Kyllä
Raportointivuosi: 2021
JUFO-taso: 1
