TY - JOUR
T1 - Beyond K-complex binary scoring during sleep
T2 - Probabilistic classification using deep learning
AU - Lechat, Bastien
AU - Hansen, Kristy
AU - Catcheside, Peter
AU - Zajamsek, Branko
PY - 2020/10/1
Y1 - 2020/10/1
N2 - Study Objectives: K-complexes (KCs) are a recognized electroencephalography marker of sensory processing and a defining feature of sleep stage 2. KC frequency and morphology may also be reflective of sleep quality, aging, and a range of sleep and sensory processing deficits. However, manual scoring of K-complexes is impractical, time-consuming, and thus costly and currently not well-standardized. Although automated KC detection methods have been developed, performance and uptake remain limited. Methods: The proposed algorithm is based on a deep neural network and Gaussian process, which gives the input waveform a probability of being a KC ranging from 0% to 100%. The algorithm was trained on half a million synthetic KCs derived from manually scored sleep stage 2 KCs from the Montreal Archive of Sleep Study containing 19 healthy young participants. Algorithm performance was subsequently assessed on 700 independent recordings from the Cleveland Family Study using sleep stages 2 and 3 data. Results: The developed algorithm showed an F1 score (a measure of binary classification accuracy) of 0.78 and thus outperforms currently available KC scoring algorithms with F1 = 0.2-0.6. The probabilistic approach also captured expected variability in KC shape and amplitude within individuals and across age groups. Conclusions: An automated probabilistic KC classification is well suited and effective for systematic KC detection for a more in-depth exploration of potential relationships between KCs during sleep and clinical outcomes such as health impacts and daytime symptomatology.
AB - Study Objectives: K-complexes (KCs) are a recognized electroencephalography marker of sensory processing and a defining feature of sleep stage 2. KC frequency and morphology may also be reflective of sleep quality, aging, and a range of sleep and sensory processing deficits. However, manual scoring of K-complexes is impractical, time-consuming, and thus costly and currently not well-standardized. Although automated KC detection methods have been developed, performance and uptake remain limited. Methods: The proposed algorithm is based on a deep neural network and Gaussian process, which gives the input waveform a probability of being a KC ranging from 0% to 100%. The algorithm was trained on half a million synthetic KCs derived from manually scored sleep stage 2 KCs from the Montreal Archive of Sleep Study containing 19 healthy young participants. Algorithm performance was subsequently assessed on 700 independent recordings from the Cleveland Family Study using sleep stages 2 and 3 data. Results: The developed algorithm showed an F1 score (a measure of binary classification accuracy) of 0.78 and thus outperforms currently available KC scoring algorithms with F1 = 0.2-0.6. The probabilistic approach also captured expected variability in KC shape and amplitude within individuals and across age groups. Conclusions: An automated probabilistic KC classification is well suited and effective for systematic KC detection for a more in-depth exploration of potential relationships between KCs during sleep and clinical outcomes such as health impacts and daytime symptomatology.
KW - Automated scoring
KW - Big data
KW - Deep learning
KW - K-complex
KW - Probabilistic output
UR - http://www.scopus.com/inward/record.url?scp=85096683697&partnerID=8YFLogxK
UR - http://purl.org/au-research/grants/NHMRC/1113571
UR - http://purl.org/au-research/grants/ARC/DP120102185
U2 - 10.1093/sleep/zsaa077
DO - 10.1093/sleep/zsaa077
M3 - Article
C2 - 32301485
AN - SCOPUS:85096683697
SN - 0161-8105
VL - 43
JO - SLEEP
JF - SLEEP
IS - 10
M1 - zsaa077
ER -