This dataset contains 535 recordings of heart and lung sounds captured using a digital stethoscope from a clinical manikin, including both individual and mixed recordings of heart and lung sounds; 50 heart sounds, 50 lung sounds, and 145 mixed sounds. For each mixed sound, the corresponding source heart sound (145 recordings) and source lung sound (145 recordings) were also recorded. It includes recordings from different anatomical chest locations, with normal and abnormal sounds.
This study aims to create a robust hand grasp recognition system using surface electromyography (sEMG) data collected from four electrodes. The grasps to be utilized in this study include cylindrical grasp, spherical grasp, tripod grasp, lateral grasp, hook grasp, and pinch grasp. The proposed system seeks to address common challenges, such as electrode shift, inter-day difference, and individual difference, which have historically hindered the practicality and accuracy of sEMG-based systems.
Fourteen participants completed this experiment. All participants were university students, physically healthy, with no history of neurological or psychiatric disorders, and were all right-handed. Written informed consent was obtained from each participant after a detailed explanation of the study protocol. All procedures were conducted in accordance with the Declaration of Helsinki. Due to issues encountered during the data collection process, the data from Participant S001 is deemed unreliable and has been excluded from further analysis.
This dataset provides synchronized recordings of electroencephalography (EEG) and accelerometer (ACC) signals collected during controlled passive lower limb movements. The data were acquired to facilitate analysis of corticokinematic coherence (CKC), aiming to quantify cortical responses associated with proprioceptive input. EEG signals were recorded from healthy participants using a standardized electrode layout according to the international 10-20 system, while tri-axial accelerometers captured precise limb movement kinematics.
Attention Deficit Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental disorder affecting children and adolescents, characterized by inattention, hyperactivity, and impulsivity. Current diagnostic methods primarily rely on subjective clinical evaluations, which are prone to bias. Advances in neurophysiological assessment, particularly through electroencephalography (EEG), eye tracking, and electrodermal activity (EDA), offer promising avenues for objective diagnosis and monitoring of ADHD.
This competition aims to develop unique and innovative solutions using data to diagnose various health issues early. The participants must use the given dataset to develop innovative solutions to predict or early diagnose any disease or health condition. The details of the solutions need to be documented clearly, and a presentation on the same needs to be developed. Participants must describe the novelty, uniqueness, and impact of their given solution.
This dataset was collected to support research on the screening and diagnosis of Diabetic Peripheral Neuropathy (DPN) and Cardiac Autonomic Neuropathy (CAN) using wearable sensor technology. It includes synchronized data from gait analysis and physiological signals such as electrocardiogram (ECG), heart rate variability (HRV), and inertial measurement units (IMUs) obtained from individuals with and without DPN and CAN.
This study investigated neural mechanisms underlying working memory by employing a visual n-back task with graded cognitive load (0-back to 3-back). Ten healthy volunteers (6 males, 4 females; mean age 23.3 ± 0.9 years) participated, performing a spatial matching task where they judged whether the current position of a displayed square matched the position presented n trials earlier, responding via keypress ("V" for match, "N" for non-match).
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.
This database, collected at the Neural Engineering Laboratory, Iran University of Science and Technology, comprises iEEG recordings from Wistar rats during healthy and epileptic conditions. Recordings were collected from 5 rats (3 males, 2 females, weighing 260-378 g and aged 4-5 months). iEEG signals were recorded from 3 brain sites: motor cortex (left M1), thalamus (left ANT), and hippocampus (right CA1) of freely moving rats. As a result, for each rat, a matrix with 3 columns (representing the 3 signals) is available in this dataset.
