Biomedical and Health Sciences

This dataset was prepared to aid in the creation of a machine learning algorithm that would classify the white blood cells in thin blood smears of juvenile Visayan warty pigs. The creation of this dataset was deemed imperative because of the limited availability of blood smear images collected from the critically endangered species on the internet. The dataset contains 3,457 images of various types of white blood cells (JPEG) with accompanying cell type labels (XLSX).


EEG consists of collecting information from brain activity in the form of electrical voltage. Epileptic Seizure prediction and detection is a major sought after research nowadays. This dataset contains data from 11 patients of whom seizures are observed in EEG for 2 patients.


The total duration of seizures is 170 seconds. The number of channels is 16 and data is collected at 256Hz sampling rate.


The final dataset files in .csv format contain 87040 rows x 17 columns,



one hundred fifty patients were followed up  every year for 28 years, and at each visit the characteristics of the patients were recoreded ( these are the predictors)  like:  sex( 0= female, 1= male) , age, BMI, LDL-chol, HOMA2-IR, systolic blood pressure and diastolic blood pressure. For each partipant, the recorded value is the mean of the follow up measurements. The age is the median value.  The response variables are the transition counts among the states of the process of the disease ( fibrosis in NAFLD ) evolving over time .


This is the augmentation dataset used in the paper named "LSTformer: Long Short-term Transformer for Real Time RespiratoryPrediction".  We made an augmentation dataset utilizing an RGB-D camera to collect motion signals in a breathing simulator phantom device. It is worth noticing that the movement of the simulator is driven by the clinical patient’s respiration, which is from a public dataset ( Other details can be seen in our previous work : H. Peng, L. Deng, Z. Xia, Y.


Screening tools play a vital role in sensory processing disorder detection. The automatic collection of features related to behavioral parameters and the response to given stimuli is possible with the recent technology. Real time stress related health parameters are collected as response to visual stimuli created with experts’ suggestions based on visual sensory processing related questionnaire. Body temperature and heart rate are obtained by smart watch.


Today, Mental health problems are getting grave and need technological solutions. Irrational anticipated fear is Anxiety Disorder. Specific Phobia disorders are a type of Anxiety disorder; these phobias are rarely detected in clinical settings and are presence indicators of other serious mental problems. VR is considered a potent tool for treatment and diagnosis.


Eight participants, sat on a stable chair with no arm rests and a high backrest, with his/her right arm strapped to the passive manipulator. The participant’s motion was simultaneously recorded using a Kinect sensor, an electronic goniometer (Biopac Systems, USA), and a passive marker motion capture system, V120:Trio (OptiTrack, USA). The Kinect sensor was placed 2 m in front and slightly above the participant. The goniometer was attached, using double-sided tape, to the participant’s arm above the elbow. Three reflective markers were used for the V120:Trio recording.


Anemia is a condition in which the oxygen-carrying capacity of red blood cells is insufficient to meet the body's physiological needs and affects billions of people worldwide. An early diagnosis of this disease could prevent the advancement of other disorders. Currently, traditional methods used to detect anemia consist of venipuncture, which requires a patient to frequently visit laboratories. Therefore, anemia diagnosis using noninvasive and cost effective methods is an open challenge.


These datasets are used for epidemilogical modeling using artifical neural network.


This dataset is the Cardiopulmonary Exercise Test(CPET) processed before using machine learning algorithms. The CPET cases went to a diverse feature engineering process that gives over 100 features and 4 labels. The labels are in binary and define if the patient has one of the following conditions, healthy, primary cardiac, pulmonary or other limitation.