Real name: 
Yongfu Li

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First Name: 
Yongfu
Last Name: 
Li

Datasets & Competitions

IEEE BioCAS2024 Grand Challenge on Depression Detection

We're excited to present a unique challenge aimed at advancing automated depression diagnosis. Traditional methods using written speech or self-reported measures often fall short in real-world scenarios. To address this, we've curated a dataset of authentic depression clinical interviews from a psychiatric hospital.

 

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Biomedical and Health Sciences
Submitted On: 
Fri, 05/31/2024 - 10:32
Last Updated On: 
Fri, 05/31/2024 - 10:55
Citation Author(s): 
Kaining Mao, Deborah BaofengWang, Tiansheng Zheng, Rongqi Jiao, Yanhui Zhu, Bin Wu, Lei Qian, Wei Lyu, Jie Chen, MinjieYe

Respiratory Sound Track Grand Challenge 2024: Respiratory Sound Compression and Event Detection for SPRSound Dataset

Respiratory diseases are major global killers, demanding early diagnosis for effective management. Digital stethoscopes offer promise, but face limitations in storage and transmission. A compressive sensing-based compression algorithm is needed to address these constraints. Meanwhile, fast-reconstruction CS algorithms are sought to balance speed and fidelity. Sound event detection algorithms are crucial for identifying abnormal lung sounds and augmenting diagnostic accuracy. Integrating these technologies can revolutionize respiratory disease management, enhancing patient outcomes.

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Biophysiological Signals
Submitted On: 
Fri, 03/01/2024 - 03:03
Last Updated On: 
Fri, 05/31/2024 - 07:30

Respiratory Sound Track Grand Challenge 2023: Respiratory Sound Classification for SPRSound Dataset

Globally, respiratory diseases are the leading cause of death, making it essential to develop an automatic respiratory sounds software to speed up diagnosis and reduce physician workload. A recent line of attempts have been proposed to predict accurately, but they have yet been able to provide a satisfactory generalization performance. In this contest, we invited the community to develop more accurate and generalized respiratory sound algorithms. A starter code is provided to standardize the submissions and lower the barrier.

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Biophysiological Signals
Submitted On: 
Tue, 02/20/2024 - 01:21
Last Updated On: 
Mon, 04/01/2024 - 08:51

SPRSound: Open-Source SJTU Paediatric Respiratory Sound Database

It has proved that the auscultation of respiratory sound has advantage in early respiratory diagnosis. Various methods have been raised to perform automatic respiratory sound analysis to reduce subjective diagnosis and physicians’ workload. However, these methods highly rely on the quality of respiratory sound database. In this work, we have developed the first open-access paediatric respiratory sound database, SPRSound. The database consists of 2,683 records and 9,089 respiratory sound events from 292 participants.

Categories:
Biophysiological Signals
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