Microscopic image based analysis plays an important role in histopathological computer based diagnostics. Identification of childhood medulloblastoma and its proper subtype from biopsy tissue specimen of childhood tumor is an integral part for prognosis.The dataset is of Childhood medulloblastoma (CMB) biopsy samples. The images are of 10x and 100x microscopic magnifications, uploaded in separate folders. The images consist of normal brain tissue cell samples and CMB cell samples of different WHO defined subtypes. An excel sheet is also uploaded for ease of data description.

Instructions: 

The dataset contains two folder of diffrent magnification images, i.e; 10x and 100x. The type of each image is described in the provided excel file. Each slide has a unique number and the number in bracket denotes that the corresponding image is of the single slide. 

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We present a novel, low-cost telerehabilitation system dedicated for bimanual training. The system captures the user’s movements with a Microsoft Kinect sensor and an inertial measurement unit (IMU). Herein, we deposit data we collected on a single, healthy subject who interacted with our system as described in our manuscript.

 

 

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27 Views

The dataset is used in "EXPACT: Explainable complex machine learning prediction of all-cause mortality in the U.S." 

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62 Views

The first bit of light is the gesture of being, on a massive screen of the black panorama. A small point of existence, a gesture of being. The universal appeal of gesture is far beyond the barriers of languages and planets. These are the microtransactions of symbols and patterns which have traces of the common ancestors of many civilizations.Gesture recognition is important to make communication between the computer system and humans, in the present era many studies are going on regarding the gesture recognition systems.

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41 Views

 

The Cross-sectional Diabetes Risk survey aims to assess the prevalence of diabetes and its risk factors at the same point in time and also provide a "snapshot" of diseases and risk factors simultaneously for individuals belonging to the western region of the Kingdom of Saudi Arabia (KSA).

 

Instructions: 

The survey is available at the following URL:

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https://www.munnamotorgarage.com/fcitrabet/saudi_diabetise_survey_2019-2....

 

The instructions for the use of the dataset and analysis tools are available in our submitted manuscript

(Machine Learning-Based Application for Predicting Risk of Type 2 Diabetes mellitus (T2DM) in Saudi Arabia: A Retrospective Cross-sectional Study) on IEEE access for review.

 

 

 

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121 Views

This dataset presents the measurements corresponding to the article "Validation of a Velostat-Based Pressure Sensitive Mat for Center of Pressure Measurements". You will find the data corresponding to an affordable commercial mat, a Velostat-based mat prototype, and a commercial force platform. The results obtained in the above-mentioned article can be reproduced with them.

Instructions: 

In the dataset, every user has a folder. In the folder of each user there are six  subfolders with the name of the balance exercises, and in each subfolder there are several files: the file of the force platform (pasco.txt), the file of the commercial mat (.json) and the files of the prototype (raw: matVelo_file.txt, post-processed: .npy). The force platform files have two headers. The second header has the name of the columns including ‘Yc (cm)’ and ‘Xc (cm)’. The commercial mat files present a dictionary with the data of the 16x16 arrays ordered sequentially. Data can be accessed by means of a sequence of keys: ‘pressureData’, ‘n’, ‘pressureData’, ’i’, ‘j’ for n = 0,1, … and i,j contained in [0,15]. The prototype post-processing files contains the data as a numpy array (time,16,16). Finally, the prototype raw files contain the sequence of 16x16 array as a comma separated vector (256 numbers per row).If the folder of the code and the folder of the dataset are at the same level and the requirements has been installed, the code can be executed showing the summary table.

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123 Views

The dataset contains rash images of 11 different disease states. Images of normal skin are also included in the dataset.

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422 Views

This dataset contains cardiovascular data recorded during progressive exsanguination in a porcine model of hemorrhage. Both wearable and catheter-based sensors were used to capture cardiovascular function; the wearable system contained a fusion of ECG, SCG, and PPG sensors while the catheter-based system was comprised of pressure catheters in the aortic arch, femoral artery, and right and left atria via a Swan-Ganz catheter.

Instructions: 

Experimental Protocol

This protocol included 6 Yorkshire swine (3 castrated male, 3 female, Age: 114–-150 days, Weight: 51.5-–71.4 kg), each of which passed a health assessment examination but were not subject to other exclusion criteria. Anesthesia was induced in the animal with xylazine and telazol and maintained with inhaled isoflurane during mechanical ventilation. Intravenous heparin was administered as needed to prevent coagulation of blood during the protocol. Before the induction of hypovolemia, a blood sample was taken to assess baseline plasma absorption. Following this baseline sample, Evans Blue dye was administered for blood volume estimation. After waiting several minutes to allow for even distribution of the dye, a second blood sample was taken to measure plasma volume. In this method, plasma volume is used along with hematocrit to estimate total blood volume. For one animal in the protocol (Pig 4), atropine was administered to raise the starting heart rate and blood pressure due to critically low values.

Hypovolemia was induced by draining blood through an arterial line at four levels of blood volume loss (7%, 14%, 21%, and 28%) as determined by the estimated total blood volume from the Evans Blue dye protocol. After draining passively through the arterial line, the blood was stored in a sterile container. Following each level of blood loss, exsanguination was paused for approximately 5-10 minutes to allow the cardiovascular system to stabilize. If cardiovascular collapse occurred once a level was reached, as defined by a 20% drop in mean aortic pressure from baseline after stabilization, exsanguination was terminated. Note that cardiovascular collapse was reached at different blood volume levels for each animal: Pigs 1, 3, and 4 reached 21% blood volume loss; Pigs 2 and 6 reached 28% blood volume loss; and Pig 5 reached 14% blood volume loss before the experimental protocol was terminated.

 

Signals from wearable sensors were continuously recorded using a BIOPAC MP160 data acquisition system (BIOPAC Systems, Inc., Goleta, California, USA) with a sampling frequency of 2 kHz. Electrocardiogram (ECG) signals were captured using a three-lead system of adhesive-backed Ag/AgCl electrodes placed in Einthoven Lead II configuration, which interfaced with a BIOPAC ECG100C amplifier. Reflectance-mode photoplethysmogram (PPG) was captured with a BIOPAC TSD270A transreflectance transducer, which interfaced with a BIOPAC OXY200 veterinary pulse oximeter. The transducer was placed over the femoral artery on either the right or left caudal limb, contralateral to inducer placement. Seismocardiogram (SCG) signals were captured using an ADXL354 accelerometer (Analog Devices, Inc., Norwood, Massachusetts, USA) placed on the mid-sternum, interfacing with a BIOPAC HLT100C transducer interface module.

Aortic root pressure was captured by inserting a fluid-filled catheter through a vascular introducer in the right carotid artery, fed through to the aortic root. Femoral artery pressure was obtained directly from an introducer placed on either the left or right femoral artery depending on accessibility. Right and left atrial pressures were captured with a Swan-Ganz catheter with proximal and distal monitoring ports inserted in either the right or left femoral vein. Left atrial pressure was inferred via PCWP captured using an Edwards 131F7 Swan-Ganz catheter (Edwards Lifesciences Corp, Irvine, California, USA). The vascular introducers were connected via pressure monitoring lines to ADInstruments MLT0670 pressure transducers (ADInstruments Inc., Colorado Springs, Colorado, USA). Data from the catheters were continuously recorded with an ADInstruments Powerlab 8/35 acquisition system sampling at 2 kHz.

 

Signal Pre-Processing

All signals were filtered with finite impulse response band-pass filters with Kaiser window, both in the forward and reverse directions to offset phase shift. Cutoff frequencies were 0.5–-40Hz for ECG and 1-40Hz for SCG. Only the dorso-ventral component of the SCG acceleration signal was used in this study. PPG signals, along with all four catheter-based pressure signals, were filtered with cutoffs at 0.5-10Hz. After filtering, data from all signals were heartbeat-separated using ECG R-peaks. The signal segments were then abbreviated to a length of 1,000 samples (500 ms) to enable more uniform analysis; however, due to the long left ventricular ejection time of Pig 3, a length of 1,500 samples (750 ms) is provided for this subject.

 

Using the Dataset

This dataset contains a separate .mat file for each of the 6 animal subjects in the protocol. The variables "scg" and "ppg" contain R-peak-separated signals from the SCG and PPG respectively during the protocol. The variables "aortic", "femoral", "rightAtrium", and "wedge" contain the R-peak-separated pressure waveforms from the catheters placed in the aortic root, femoral artery, right atrium, and left atrium (wedge pressure) respectivley. Each of these variables is a struct, with each of its fields representing a different level of blood volume loss. The field "B1" corresponds to the baseline level (pre-exsanguination); "L1", "L2", "L3", and "L4" correspond to the 7%, 14%, 21%, and 28% drop in blood volume respectively. Thus, the data in each field represents the heartbeat-separated signals collected during each blood volume level. The data has been selected such that periods of active draining of blood have been removed, such that the provided data reflects the heartbeat-separated signals during the resting period between blood-draws. The data is formatted in columnwise matrices, with the columns arranged in sequention order such that the first column is the first heartbeat and the last row is the last heartbeat.

 

The indices of ECG R-peaks are provided as a vector as well during each blood volume level, such that each element in the vector corresponds to its respective column in the provided column matrices. The unit of these values is in miliseconds, staring from t = 0 (onset of baseline recording).

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133 Views

This repository introduces a novel dataset for the classification of Chronic Obstructive Pulmonary Disease (COPD) patients and Healthy Controls. The Exasens dataset includes demographic information on 4 groups of saliva samples (COPD-HC-Asthma-Infected) collected in the frame of a joint research project, Exasens (https://www.leibniz-healthtech.de/en/research/projects/bmbf-project-exasens/), at the Research Center Borstel, BioMaterialBank Nord (Borstel, Germany).

Instructions: 

 

Definition of 4 sample groups included within the Exasens dataset:

(I) Outpatients and hospitalized patients with COPD without acute respiratory infection (COPD).

(II) Outpatients and hospitalized patients with asthma without acute respiratory infections (Asthma).

(III) Patients with respiratory infections, but without COPD or asthma (Infected).

(IV) Healthy controls without COPD, asthma, or any respiratory infection (HC).

Attribute Information:

1- Diagnosis (COPD-HC-Asthma-Infected)

2- ID

3- Age

4- Gender (1=male, 0=female)

5- Smoking Status (1=Non-smoker, 2=Ex-smoker, 3=Active-smoker)

6- Saliva Permittivity:

a) Imaginary part (Min(Δ)=Absolute minimum value, Avg.(Δ)=Average)

b) Real part (Min(Δ)=Absolute minimum value, Avg.(Δ)=Average)

In case of using the introduced Exasens dataset or the proposed classification methods, please cite the following papers:

  • P. S. Zarrin, N. Roeckendorf and C. Wenger., "In-vitro Classification of Saliva Samples of COPD Patients and Healthy Controls Using Machine Learning Tools," in IEEE Access, doi: 10.1109/ACCESS.2020.3023971.

  • Soltani Zarrin, P.; Ibne Jamal, F.; Roeckendorf, N.; Wenger, C. Development of a Portable Dielectric Biosensor for Rapid Detection of Viscosity Variations and Its In Vitro Evaluations Using Saliva Samples of COPD Patients and Healthy Control. Healthcare 2019, 7, 11.

  • Soltani Zarrin, P.; Jamal, F.I.; Guha, S.; Wessel, J.; Kissinger, D.; Wenger, C. Design and Fabrication of a BiCMOS Dielectric Sensor for Viscosity Measurements: A Possible Solution for Early Detection of COPD. Biosensors 2018, 8, 78.

  • P.S. Zarrin and C. Wenger. Pattern Recognition for COPD Diagnostics Using an Artificial Neural Network and Its Potential Integration on Hardware-based Neuromorphic Platforms. Springer Lecture Notes in Computer Science (LNCS), Vol. 11731, pp. 284-288, 2019.

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518 Views

The data set is collected from MyNeuroHealth Application developed for the detection of Seizures and Falls. Data is gathered using tri-axial accelerometer placed at the upper left arm of an individual in an unconstraint environment.

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280 Views

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