Biomedical and Health Sciences

Data related to the publication "Real-Time Optimization of Multi-Target Focused Ultrasound using Phased Array Transducers"

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.

Brain-Computer Interface (BCI) technology makes possible a direct interface between the brain and external devices through the interpretation of neural signals. It is essential to have patient's native language-containing datasets when designing BCI-based solutions for neurological disorders. Current BCI research, though, lacks language-specific datasets, notably for languages like Telugu, which has over 90 million speakers in India. We developed an Electroencephalograph (EEG)-based Brain-Computer Interface (BCI) dataset consisting of EEG signal samples for Telugu Vowels and Consonants.

No Iamge

This dataset includes conjunctival and retinal images collected from both diabetic and healthy individuals to support research on diabetes-related vascular changes. For each subject, eight conjunctival images (four per eye: looking left, right, up, and down) are provided. Subjects with diabetes additionally have corresponding left and right retinal fundus images. Metadata for diabetic participants includes classification into subgroups: diabetes only, diabetes with retinopathy, or diabetes with related complications such as hypertension.

<p>Electrocardiogram (ECG) interpretation is critical for diagnosing a wide range of cardiovascular conditions. To streamline and accelerate the development of deep learning models in this domain, we present a novel, image-based version of the PTB Diagnostic ECG Database tailored for use with convolutional neural networks (CNNs), vision transformers (ViTs), and other image classification architectures. This enhanced dataset consists of 516 grayscale .png images, each representing a 12-lead ECG signal arranged as a 2D matrix (12 × T, where T is the number of time steps).

Paper : Assessment of Inference Improvements for Facial Micronutrient Deficiency Detection using Attention-Enhanced YOLOv5

Authors : Amey Agarwal, Shreya Rathod, Riva Rodrigues, Nirmitee Sarode, Dhananjay R. Kalbande

Desciption

This is a dataset of 7 classes : 6 facial skin problems and 1 null class.

A facial skin problem may be identified in an image and marked using Bounding Box Annotation.

Acne Class indicates deficiency of Vitamin D

Blackhead and Nodules are types of acne 

This dataset is a curated and processed version of the ISIC2019 skin lesion dataset, specifically prepared for research on lightweight skin disease classification and knowledge distillation. The dataset includes:

A subset of dermoscopic images from ISIC2019, formatted and resized for training and evaluation.

Corresponding metadata tables containing patient information (e.g., age, sex, lesion location).

Pre-processed CSV files that map image names to diagnostic labels.

Split files (train/val/test) for reproducibility.

Attached Image data set from combined OCT-SLO is used to train AI models and identify features to maximize quality of data set to adjust MZI reference arm, PMT Voltage of Liquid Lens and location of object. Why adjustment is needed is explained below: 

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.