[content] Avoid footnotes as they are not well supported by theme

Author: anjos

content/category/theses/samuel-michel.bib

@@ -0,0 +1,52 @@
+@article{chambon-2018,
+    title = {A {Deep} {Learning} {Architecture} for {Temporal} {Sleep} {Stage} {
+             Classification} {Using} {Multivariate} and {Multimodal} {Time} {
+             Series}},
+    volume = {26},
+    issn = {1534-4320, 1558-0210},
+    url = {https://ieeexplore.ieee.org/document/8307462/},
+    doi = {10.1109/TNSRE.2018.2813138},
+    abstract = {Sleep stage classification constitutes an important preliminary
+                exam in the diagnosis of sleep disorders. It is traditionally
+                performed by a sleep expert who assigns to each 30 s of signal a
+                sleep stage, based on the visual inspection of signals such as
+                electroencephalograms (EEG), electrooculograms (EOG),
+                electrocardiograms (ECG) and electromyograms (EMG). We introduce
+                here the first deep learning approach for sleep stage
+                classification that learns end-to-end without computing
+                spectrograms or extracting hand-crafted features, that exploits
+                all multivariate and multimodal Polysomnography (PSG) signals
+                (EEG, EMG and EOG), and that can exploit the temporal context of
+                each 30 s window of data. For each modality the first layer learns
+                linear spatial filters that exploit the array of sensors to
+                increase the signal-to-noise ratio, and the last layer feeds the
+                learnt representation to a softmax classifier. Our model is
+                compared to alternative automatic approaches based on
+                convolutional networks or decisions trees. Results obtained on 61
+                publicly available PSG records with up to 20 EEG channels
+                demonstrate that our network architecture yields state-of-the-art
+                performance. Our study reveals a number of insights on the
+                spatio-temporal distribution of the signal of interest: a good
+                trade-off for optimal classification performance measured with
+                balanced accuracy is to use 6 EEG with 2 EOG (left and right) and
+                3 EMG chin channels. Also exploiting one minute of data before
+                and after each data segment offers the strongest improvement when
+                a limited number of channels is available. As sleep experts, our
+                system exploits the multivariate and multimodal nature of PSG
+                signals in order to deliver state-of-the-art classification
+                performance with a small computational cost.},
+    language = {en},
+    number = {4},
+    urldate = {2022-05-19},
+    journal = {IEEE Transactions on Neural Systems and Rehabilitation
+               Engineering},
+    author = {Chambon, Stanislas and Galtier, Mathieu N. and Arnal, Pierrick J.
+              and Wainrib, Gilles and Gramfort, Alexandre},
+    month = apr,
+    year = {2018},
+    pages = {758--769},
+    file = {Chambon et al. - 2018 - A Deep Learning Architecture for Temporal
+            Sleep St.pdf:/Users/andre/Documents/Zotero/storage/XMNG4AJM/Chambon
+            et al. - 2018 - A Deep Learning Architecture for Temporal Sleep
+            St.pdf:application/pdf},
+}

content/category/theses/samuel-michel.md

@@ -7,6 +7,7 @@ summary: |-
   This thesis develops stateless methods for sleep-phase detection from
   polysomnographs (PSG), while exploring techniques to improve
   cross-database generalisation.
+pybtex_sources: samuel-michel.bib
 ---
 
 :::{figure} {static}/images/covers/samuel-michel.png
@@ -45,7 +46,7 @@ data coming from different sleep centers. We scoped this study to stateless
 models that do not take into account temporal context. We investigated both
 hand-crafted and learnable feature extractors. In terms of intra-database
 performance, our best model was the CNN Chambon model proposed by Chambon et
-al. in their paper [^cite_chambon-2018]. However, when evaluating generalization
+al. in their paper [@@chambon-2018]. However, when evaluating generalization
 across different setups, the random forest model with manually chosen features
 described in the same paper emerged as the best model.
 
@@ -61,12 +62,5 @@ reproduce published findings.
 :::
 
 % Place your references here
-
-[^cite_chambon-2018]: *S. Chambon, M. N. Galtier, P. J. Arnal, G. Wainrib, et A.
-    Gramfort*. **A Deep Learning Architecture for Temporal Sleep Stage
-    Classification Using Multivariate and Multimodal Time Series**. IEEE Trans.
-    Neural Syst. Rehabil. Eng., vol. 26, nᵒ 4, p. 758–769, avr. 2018,
-    <https://doi.org/10.1109/TNSRE.2018.2813138>.
-
 [software]: {filename}../software/sleepless.md
-[thesis report]: https://publications.idiap.ch/publications/show/5127
+[thesis report]: <https://publications.idiap.ch/publications/show/5127>