The dataset corresponding to the paper "A Time-Frequency Disentanglement Framework with Liquid Neural Networks for Biometric Identification and Physiological Interference Analysis from PPG Sensor Data"
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Abstract— Objective: Pulse oximetry is widely used to measure photoplethysmographic (PPG) signals and blood oxygen saturation but is susceptible to motion artifacts. This is a particular challenge for the growing field of wearable health devices. Independent component analysis (ICA) offers a solution for artifact removal without additional sensors. As there are different approaches for performing an ICA and because artifacts can take many forms, the optimal configuration is unknown.
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This research introduces the Open Seizure Database and Toolkit as a novel, publicly accessible resource designed to advance non-electroencephalogram seizure detection research. This paper highlights the scarcity of resources in the non-electroencephalogram domain and establishes the Open Seizure Database as the first openly accessible database containing multimodal sensor data from 49 participants in real-world, in-home environments.
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The MAUS dataset focused on collecting easy-acquired physiological signals under different mental demand conditions. We used the N-back task to stimuli different mental workload statuses. This dataset can help in developing a mental workload assessment system based on wearable device, especially for that PPG-based system. MAUS dataset provides ECG, Fingertip-PPG, Wrist-PPG, and GSR signal. User can make their own comparison between Fingertip-PPG and Wrist-PPG. Some study can be carried out in this dataset
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