update

Author: daiyang07

.idea/.gitignore

@@ -1,11 +1,11 @@
 # MultiChannelSleepNet
 ### MultiChannelSleepNet: A Transformer-based Model for Automatic Sleep Stage Classification with PSG 
 #### *by: Yang Dai, Xiuli li, Shanshan Liang, Lukang Wang, Qingtian Duan, Hui Yang, Chunqing Zhang, Xiaowei Chen, Longhui Li\*, Xingyi Li\*, and Xiang Liao\* 
-
+This work has been accepted for publication in [IEEE Journal of Biomedical and Health Informatics (JBHI).](https://ieeexplore.ieee.org/document/10146380)
 
 ## Abstract
 ![AttnSleep Architecture](imgs/MultiChannelSleepNet.png)
-Automatic sleep stage classification plays an essential role in sleep quality measurement and sleep disorder diagnosis. Although many approaches have been developed, most use only single-channel electroencephalogram signals for classification. Polysomnography (PSG) provides multiple channels of signal recording, enabling the use of the appropriate method to extract and integrate the information from different channels to achieve higher sleep staging performance. We present a transformer encoder-based model, MultiChannelSleepNet, for automatic sleep stage classification with multichannel PSG data, whose architecture is implemented based on the transformer encoder for single-channel feature extraction and multichannel feature fusion. In a single-channel feature extraction block, transformer encoders extract features from time-frequency images of each channel independently. Based on our integration strategy, the feature maps extracted from each channel are fused in the multichannel feature fusion block. Another set of transformer encoders further capture joint features, and a residual connection preserves the original information from each channel in this block. Experimental results on three publicly available datasets demonstrate that our method achieves higher classification performance than state-of-the-art techniques. MultiChannelSleepNet is an efficient method to fully use the information from multichannel PSG data, which facilitates precision sleep staging in clinical applications.
+Automatic sleep stage classification plays an essential role in sleep quality measurement and sleep disorder diagnosis. Although many approaches have been developed, most use only single-channel electroencephalogram signals for classification. Polysomnography (PSG) provides multiple channels of signal recording, enabling the use of the appropriate method to extract and integrate the information from different channels to achieve higher sleep staging performance. We present a transformer encoder-based model, MultiChannelSleepNet, for automatic sleep stage classification with multichannel PSG data, whose architecture is implemented based on the transformer encoder for single-channel feature extraction and multichannel feature fusion. In a single-channel feature extraction block, transformer encoders extract features from time-frequency images of each channel independently. Based on our integration strategy, the feature maps extracted from each channel are fused in the multichannel feature fusion block. Another set of transformer encoders further capture joint features, and a residual connection preserves the original information from each channel in this block. Experimental results on three publicly available datasets demonstrate that our method achieves higher classification performance than state-of-the-art techniques. MultiChannelSleepNet is an efficient method to extract and integrate the information from multichannel PSG data, which facilitates precision sleep staging in clinical applications.
 
 
 ## Requirmenets:
@@ -24,7 +24,7 @@ We used three public datasets in this study:
 - [SleepEDF-78](https://physionet.org/content/sleep-edfx/1.0.0/)
 - [SHHS](https://sleepdata.org/datasets/shhs)
 
-This project currently only provides pre-processing code for SleepEDF-20 and SleepEDF-78. We will update the code of SHHS later.  
+This project currently only provides pre-processing code for SleepEDF-20 and SleepEDF-78, and only provides code for sample-wise k-fold cross-validation. We will update the code in the future.  
 After downloading the datasets, please place them in the folder with the corresponding name in the directory `dataset`.  
 You can run the `dataset_prepare.py` to extract events from the original record (.edf)
 
@@ -45,6 +45,7 @@ To easily reproduce the results you can follow the next steps:
 4. Run `result_evaluate.py` to get the evaluation report. It concludes the various valuation metrics we described in paper.  
 
 
+
 ## Contact
 Yang Dai  
 Center for Neurointelligence, School of Medicine  

.idea/MultiChannelSleepNet.iml

@@ -65,8 +65,7 @@ def forward(self, x):
         x2 = self.transformer_encoder_2(x2)
         x3 = self.transformer_encoder_3(x3)
 
-        x = torch.cat((x1, x2), dim=2)
-        x = torch.cat((x, x3), dim=2)
+        x = torch.cat([x1, x2, x3], dim=2)
 
         x = self.drop(x)
         x = self.layer_norm(x)