@InProceedings{Phan2023a,
   author    = {Phan, Huy and Heremans, Elisabeth and Chén, Oliver Y. 
and Koch, Philipp and Mertins, Alfred and De Vos, Maarten},
   booktitle = {ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
   title     = {Improving Automatic Sleep Staging Via Temporal 
Smoothness Regularization},
   year      = {2023},
   pages     = {1-5},
   doi       = {10.1109/ICASSP49357.2023.10095805},
}

@Article{Phan2023,
   author  = {Phan, Huy and Lorenzen, Kristian P. and Heremans, Elisabeth and Chén, Oliver Y. and Tran, Minh C. and Koch, Philipp and Mertins, Alfred and Baumert, Mathias and Mikkelsen, Kaare B. and De Vos, Maarten},
   journal = {IEEE Journal of Biomedical and Health Informatics},
   title   = {L-{SeqSleepNet}: {W}hole-cycle Long Sequence Modeling for 
Automatic Sleep Staging},
   year    = {2023},
   number  = {10},
   pages   = {4748-4757},
   volume  = {27},
   doi     = {10.1109/JBHI.2023.3303197},
}

@Article{Phan2022a,
   author   = {Phan, Huy and Mertins, Alfred and Baumert, Mathias},
   journal  = {IEEE Transactions on Biomedical Engineering},
   title    = {Pediatric Automatic Sleep Staging: A comparative study of 
state-of-the-art deep learning methods},
   year     = {2022},
   pages    = {1-1},
   abstract = {Despite the tremendous progress recently made towards automatic sleep staging in adults, it is currently unknown if the most advanced algorithms generalize to the pediatric population, which displays distinctive characteristics in overnight polysomnography (PSG). 
To answer the question, in this work, we conduct a large-scale comparative study on the state-of-the-art deep learning methods for pediatric automatic sleep staging. Six different deep neural networks with diverging features are adopted to evaluate a sample of more than
1,200 children across a wide spectrum of obstructive sleep apnea (OSA) severity. Our experimental results show that the individual performance of automated pediatric sleep stagers when evaluated on new subjects is equivalent to the expert-level one reported on adults. Combining the six stagers into ensemble models further boosts the staging accuracy, reaching an overall accuracy of 88.8%, a Cohens kappa of 0.852, and a macro F1-score of 85.8%. At the same time, the ensemble models lead to reduced predictive uncertainty. The results also show that the studied algorithms and their ensembles are robust to concept drift when the training and test data were recorded seven months apart and after clinical intervention. However, we show that the improvements in the staging performance are not necessarily clinically significant although the ensemble models lead to more favorable clinical measures than the six standalone models. Detailed analyses further demonstrate "almost perfect" agreement between the automatic stagers to one another and their similar patterns on the staging errors, suggesting little room for improvement.},
   doi      = {10.1109/TBME.2022.3174680},
}

@Article{Phan2022,
   author   = {Phan, Huy and Mikkelsen, Kaare and Chén, Oliver Y. and 
Koch, Philipp and Mertins, Alfred and De Vos, Maarten},
   journal  = {IEEE Transactions on Biomedical Engineering},
   title    = {SleepTransformer: Automatic Sleep Staging With 
Interpretability and Uncertainty Quantification},
   year     = {2022},
   number   = {8},
   pages    = {2456-2467},
   volume   = {69},
   abstract = {Background: Black-box skepticism is one of the main hindrances impeding deep-learning-based automatic sleep scoring from being used in clinical environments. Methods: Towards interpretability, this work proposes a sequence-to-sequence sleep-staging model, namely SleepTransformer. It is based on the transformer backbone and offers interpretability of the model's decisions at both the epoch and sequence level. We further propose a simple yet efficient method to quantify uncertainty in the model's decisions. The method, which is based on entropy, can serve as a metric for deferring low-confidence epochs to a human expert for further inspection. Results: Making sense of the transformer's self-attention scores for interpretability, at the epoch level, the attention scores are encoded as a heat map to highlight sleep-relevant features captured from the input EEG signal. At the sequence level, the attention scores are visualized as the influence of different neighboring epochs in an input sequence (i.e. the context) to recognition of a target epoch, mimicking the way manual scoring is done by human experts. Conclusion: Additionally, we demonstrate that SleepTransformer performs on par with existing methods on two databases of different sizes. Significance: Equipped with interpretability and the ability of uncertainty quantification, SleepTransformer holds promise for being integrated into clinical settings.},
   doi      = {10.1109/TBME.2022.3147187},
}

@InProceedings{Phan2021a,
  author    = {Huy Phan and Huy Le Nguyen and Oliver Y Chén and Philipp Koch and Ngoc QK Duong and Ian McLoughlin and Alfred Mertins},
  booktitle = {Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  title     = {Self-attention generative adversarial network for speech enhancement},
  year      = {2021},
  pages     = {7103--7107},
}

@Article{Phan2021b,
  author  = {Huy Phan and Oliver Y Chén and Minh C Tran and Philipp Koch and Alfred Mertins and Maarten De Vos},
  journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
  title   = {XSleepNet: Multi-view sequential model for automatic sleep staging},
  year    = {2021},
}

@InProceedings{Koch2021,
  author    = {Koch, Philipp and Mohammad-Zadeh, Kamran and Maass, Marco and Dreier, Mark and Thomsen, Ole and Parbs, Tim J. and Phan, Huy and Mertins, Alfred},
  title     = {sEMG-Based Hand Movement Regression by Prediction of Joint Angles With Recurrent Neural Networks},
  booktitle = {43nd Annual International Conference of the IEEE Engineering in Medicine \& Biology Society (EMBC)},
  year      = {2021},
  pages     = {6519--6523},
doi = {10.1109/EMBC46164.2021.9630042},
}

@InProceedings{Phan2021,
  author    = {Huy Phan and Huy Le Nguyen and Oliver Y Chén and Lam Pham and Philipp Koch and Ian McLoughlin and Alfred Mertins},
  booktitle = {Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  title     = {Multi-view Audio and Music Classification},
  year      = {2021},
  pages     = {611--615},
}

@InProceedings{Pham2021b,
   author    = {Pham, Lam and Phan, Huy and Schindler, Alexander and 
King, Ross and Mertins, Alfred and McLoughlin, Ian},
   booktitle = {2021 43rd Annual International Conference of the IEEE Engineering in Medicine Biology Society (EMBC)},
   title     = {Inception-Based Network and Multi-Spectrogram Ensemble 
Applied To Predict Respiratory Anomalies and Lung Diseases},
   year      = {2021},
   pages     = {253-256},
   doi       = {10.1109/EMBC46164.2021.9629857},
}

@Article{Pham2021,
  author  = {Lam Dang Pham and Huy Phan and Ramaswamy Palaniappan and Alfred Mertins and Ian McLoughlin},
  journal = {IEEE Journal of Biomedical and Health Informatics},
  title   = {C{NN-MoE} based framework for classification of respiratory anomalies and lung disease detection},
  year    = {2021},
}

@InProceedings{Koch2020a,
  author    = {Koch, Philipp and Dreier, Mark and Larsen, Anna and Parbs, Tim J. and Maass, Marco and Phan, Huy and Mertins, Alfred},
  title     = {Regression of Hand Movements from sEMG Data with Recurrent Neural Networks},
  booktitle = {42nd Annual International Conference of the IEEE Engineering in Medicine \& Biology Society (EMBC)},
  year      = {2020},
  pages     = {3783--3787},
}

@InProceedings{Koch2020b,
  author    = {Koch, Philipp and Dreier, Mark and Maass, Marco and Phan, Huy and Mertins, Alfred},
  title     = {RNN With Stacked Architecture for sEMG based Sequence-to-Sequence Hand Gesture Recognition},
  booktitle = {28th European Signal Processing Conference (EUSIPCO)},
  year      = {2020},
  pages     = {1600-1604},
}

@InProceedings{koch2018a,
  author    = {Philipp Koch and Huy Phan and Marco Maass and Fabrice Katzberg and Alfred Mertins},
  title     = {Recurrent Neural Network Based Early Prediction of Future Hand Movements},
  booktitle = {Proc. 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2018)},
  year      = {2018},
  address   = {Honolulu, Hawaii, United States},
  month     ={July}
}

@InProceedings{koch2018b,
  author    = {Philipp Koch and Huy Phan and Marco Maass and Fabrice Katzberg and  Radoslaw Mazur and Alfred Mertins},
  title     = {Recurrent Neural Networks with Weighting Loss for Early Prediction of Hand Movements},
  booktitle = {26th European Signal Processing Conference (EUSIPCO 2018)},
  year      = {2018},
  address   = {Rome, Italy},
  month     = {September} 
}

@INPROCEEDINGS{phan2017d,
  author = {Huy Phan and Philipp Koch and Fabrice Katzberg and Marco Maass and
	Radoslaw Mazur and Ian McLoughlin and Alfred Mertins},
  title = {What Makes Audio Event Detection Harder than Classification?},
  booktitle = {Proc. 25th European Signal Processing Conference (EUSIPCO 2017)},
  year = {2017},
  address = {Kos island, Greece},
  month = {September},
  owner = {Phan},
  timestamp = {2017.05.26}
}

@InProceedings{mazur2017a,
  author =    {Radoslaw Mazur and Fabrice Katzberg and Huy Phan, and Alfred Mertins},
  title =     {Room Equalization Based on Measurements with Moving Microphones},
  booktitle = {Proc. Workshop on Hands-free Speech Communication and Microphone Arrays (HSCMA)},
  year =      {2017},
  address =   {San Francisco, USA},
  month =     {March}
}

@ARTICLE{phan2017b,
  author = {Huy Phan and Lars Hertel and Marco Maass and Philipp Koch and Radoslaw
	Mazur and Alfred Mertins},
  title = {Improved Audio Scene Classification based on Label Tree Embeddings
	and Convolutional Neural Networks},
  journal = {IEEE/ACM Trans. on Audio, Speech and Language Processing (TASLP)},
  year = {2017},
  volume = {25},
  pages = {1278-1290},
  number = {6},
  month = {June},
  owner = {Phan},
  timestamp = {2017.02.25},
doi = {10.1109/TASLP.2017.2690564}
}

@INPROCEEDINGS{koch2017a,
  author = {Philipp Koch and Huy Phan and Marco Maass and Fabrice Katzberg and
	Alfred Mertins},
  title = {Early Prediction of Future Hand Movements Using s{EMG} Data},
  booktitle = {Proc. 39th Annual International Conference of the IEEE Engineering
	in Medicine and Biology Society (EMBC 2017)},
  year = {2017},
  address = {Jeju Island, South Korea},
  month = {July},
  owner = {Koch},
  timestamp = {2017.08.18}
}

@ARTICLE{phan2017e,
  author = {Ian McLoughlin and Haomin Zhang and Zhipeng Xie and Yan Song and
	Wei Xiao and Huy Phan},
  title = {Continuous Robust Sound Event Classification Using Time-Frequency
	Features and Deep Learning},
  journal = {PLoS ONE},
  year = {2017},
  volume = {12},
  number = {9},
  month = {September},
  eid = {e0182309},
  owner = {Phan},
  timestamp = {2017.09.14},
doi = {10.1371/journal.pone.0182309}
}

@INPROCEEDINGS{phan2017c,
  author = {Huy Phan and Philipp Koch and Fabrice Katzberg and Marco Maass and
	Radoslaw Mazur and Alfred Mertins},
  title = {Audio Scene Classification with Deep Recurrent Neural Networks},
  booktitle = {Proc. 18th Annual Conference of the International Speech Communication
	Association (INTERSPEECH 2017)},
  year = {2017},
  address = {Stockholm, Sweden},
  month = {August},
  owner = {Phan},
  timestamp = {2017.05.24}
}

@Article{maass2017a,
  author  = {Maass, Marco and Ahlborg, Mandy and Bakenecker, Anna and Katzberg, Fabrice and Phan, Huy and Buzug, Thorsten M. and Mertins, Alfred},
  title   = {A Trajectory Study for Obtaining MPI System Matrices in a Compressed-Sensing Framework},
  journal = {International Journal on Magnetic Particle Imaging},
  year    = {2017},
volume = {3},
number = {2},
doi ={10.18416/ijmpi.2017.1706005}
}

@InProceedings{Maass2016a,
  Title                    = {Compression of FFP System Matrix with a Special Sampling Rate on the Lissajous Trajectory},
  Author                   = {Maass, Marco and Bente, Klaas and Ahlborg, Mandy and Medimagh, Hanne and Phan, Huy and Buzug, Thorsten M. and Mertins, Alfred},
  Booktitle                = {International Workshop on Magnetic Particle Imaging},
  Year                     = {2016},

  Address                  = {Lübeck, Germany},
  Month                    = {March},
  Pages                    = {56},
  Publisher                = {Infinite Science Publishing}
}

@INPROCEEDINGS{phan2016a,
  author = {Huy Phan and Marco Maass and Lars Hertel and Radoslaw Mazur and Ian
	McLoughlin and Alfred Mertins},
  title = {Learning Compact Structural Representations for Audio Events Using
	Regressor Banks},
  booktitle = {Proc. 41st IEEE International Conference on Acoustics, Speech and
	Signal Processing (ICASSP 2016)},
  year = {2016},
  address = {Shanghai, China},
  month = {March},
  pages = {211--215},
  owner = {Phan},
  timestamp = {2016.01.22}
}

@Article{maass2016b,
  Title                    = {Optimized Compression of MPI System Matrices Using a Symmetry-Preserving Secondary Orthogonal Transform},
  Author                   = {Maass, Marco and Bente, Klaas and Ahlborg, Mandy and Medimagh, Hanne and Phan, Huy and Buzug, Thorsten M. and Mertins, Alfred},
volume = {2},
number = {1},
  Journal                  = {International Journal on Magnetic Particle Imaging},
  Year                     = {2016},
doi ={10.18416/ijmpi.2016.1607002}
}

@INPROCEEDINGS{phan2016c,
  author = {Huy Phan and Lars Hertel and Marco Maass and Alfred Mertins},
  title = {Robust Audio Event Recognition with 1-Max Pooling Convolutional Neural
	Networks},
  booktitle = {Proc. 17th Annual Conference of the International Speech Communication
	Association (INTERSPEECH 2016)},
  year = {2016},
  address = {San Francisco, USA},
  month = {September},
  owner = {Phan},
  timestamp = {2016.06.16}
}