SleepKD

Teacher Assistant-Based Knowledge Distillation Extracting Multi-level Features on Single Channel Sleep EEG

Accepted by IJCAI 2023 [Paper] [Webpage]

Datasets

ISRUC-III

ISRUC-III collects the PSG data samples from 10 subjects (1 for males and 9 for females) for a whole night in 8 hours. The annotations of this dataset are scored by two professional experts.

Sleep-EDF

Sleep-EDF is a very famous public dataset that contains the PSG data samples from 20 subjects (10 for males and 10 for females) in 2 days. The ages of the subjects range from 25 to 34 years old. These recordings were manually classi- fied into one of the eight classes (W, N1, N2, N3, N4, REM, Movement, Unknown) by sleep experts according to the R&K standard. For a fair comparison, we remove the Movement and Unknown stage, and merge the N3 and N4 stage into a single N3 stage according to the AASM manual.

Build With

• TensorFlow 2.5.0
• Python 3.7

Implementation

We implement all our knowledge distillation experiments, including SleepKD and baselines, with tensorflow/keras.

In the training process, the input of the student model is [data, teacher_features] while the output is the output of the SleepKD layer we published.

In the inference process, the SleepKD layer should be removed from the student model. As a result, the input of the student model is [data] while the output is the prediction of the model.

Usage

We provide the distillation file SleepKD.py. You need to extract the intermediate features as the inputs of the SleepKD distillation layer.

Reference

@inproceedings{DBLP:conf/ijcai/LiangLWJ23,
    author       = {Heng Liang and
                    Yucheng Liu and
                    Haichao Wang and
                    Ziyu Jia},
    title        = {Teacher Assistant-Based Knowledge Distillation Extracting Multi-level
                    Features on Single Channel Sleep {EEG}},
    booktitle    = {Proceedings of the Thirty-Second International Joint Conference on
                    Artificial Intelligence, {IJCAI} 2023, 19th-25th August 2023, Macao,
                    SAR, China},
    pages        = {3948--3956},
    publisher    = {ijcai.org},
    year         = {2023},
    url          = {https://doi.org/10.24963/ijcai.2023/439},
    doi          = {10.24963/ijcai.2023/439},
    timestamp    = {Mon, 14 Aug 2023 16:05:12 +0200},
    biburl       = {https://dblp.org/rec/conf/ijcai/LiangLWJ23.bib},
    bibsource    = {dblp computer science bibliography, https://dblp.org}
    }