Search Results for author: Shijia Geng

Found 8 papers, 4 papers with code

A Review of Deep Learning Methods for Photoplethysmography Data

1 code implementation23 Jan 2024 Guangkun Nie, Jiabao Zhu, Gongzheng Tang, Deyun Zhang, Shijia Geng, Qinghao Zhao, Shenda Hong

In this review, we systematically reviewed papers that applied deep learning models to process PPG data between January 1st of 2017 and July 31st of 2023 from Google Scholar, PubMed and Dimensions.

Human Activity Recognition Photoplethysmography (PPG)

Self-Supervised Time Series Representation Learning via Cross Reconstruction Transformer

1 code implementation20 May 2022 Wenrui Zhang, Ling Yang, Shijia Geng, Shenda Hong

In this paper, we aim at learning representations for time series from a new perspective and propose Cross Reconstruction Transformer (CRT) to solve the aforementioned problems in a unified way.

Contrastive Learning Representation Learning +2

Defending Against Adversarial Attack in ECG Classification with Adversarial Distillation Training

no code implementations14 Mar 2022 Jiahao Shao, Shijia Geng, Zhaoji Fu, Weilun Xu, Tong Liu, Shenda Hong

The results show that our method performed more effectively against adversarial attacks targeting on ECG classification than the other baseline methods, namely, adversarial training, defensive distillation, Jacob regularization, and noise-to-signal ratio regularization.

Adversarial Attack Classification +1

A Deep Bayesian Neural Network for Cardiac Arrhythmia Classification with Rejection from ECG Recordings

1 code implementation26 Feb 2022 Wenrui Zhang, Xinxin Di, Guodong Wei, Shijia Geng, Zhaoji Fu, Shenda Hong

Finally, with the help of a clinician, we conduct case studies to explain the results of large uncertainties and incorrect predictions with small uncertainties.

MetaVA: Curriculum Meta-learning and Pre-fine-tuning of Deep Neural Networks for Detecting Ventricular Arrhythmias based on ECGs

no code implementations25 Feb 2022 Wenrui Zhang, Shijia Geng, Zhaoji Fu, Linlin Zheng, Chenyang Jiang, Shenda Hong

MAML is expected to better transfer the knowledge from a large dataset and use only a few recordings to quickly adapt the model to a new person.

Meta-Learning

A Simple Self-Supervised ECG Representation Learning Method via Manipulated Temporal-Spatial Reverse Detection

no code implementations25 Feb 2022 Wenrui Zhang, Shijia Geng, Shenda Hong

To verify the effectiveness of the proposed method, we perform a downstream task to detect atrial fibrillation (AF) which is one of the most common ECG tasks.

Representation Learning Self-Supervised Learning

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