Biomedical Signal Processing and Control

The file contains three CNN 5-CH models in Matlab Deep Learning Toolbox format. The trained networks correspond to Experiments #1, #2, and #3b as described in the paper.

Algorithm for robust and accurate estimation of QRS-T angle metrics

built a ventricular parasystole dataset. Two electrocardiologists added manual annotations to the open-access St.-Petersburg Institute of Cardiological Technics 12-lead arrhythmia database as gold standards.

All datasets were divided into three parts of raw data, analyzed data, and the related codes. The raw EEG and EMG data were obtained from the experiment, which were mentioned in the manuscript. Five subjects repeated the movements of wrist flexion, wrist extension, and fist twice as described in the experiment. We compressed all datasets as a folder named “Related Data.zip” and all datasets were divided into three parts of raw data, analyzed data, and the related codes. As a result, there were ten sets of raw data. We have given the raw data in a file called “subject1-5_EEG_EMG_data.xlsx” and also a folder “Raw_ data” that contains the files in the matlab mat format. The analyzed data were MSC coefficients and presented in a file folder “Coherence_data(new and old)” that contains all MSC coefficients using the mat file format. We have shown the related codes in the file folder “data_file.m”, which were saved in the matlab m-file format.

research details: measurement and analysis

New novel paper for work of research

The shared data consists of Ground Reaction Forces (GRF) and Ankle Angles used in this work. GRFs are in Newtons (N) and Ankle angles are in degrees (°). The GRFs are shared as GRF left and GRF right denoting the left and right gait cycles (GRF left, GRF right). Similarly, ankle angles are shared as ankle angle left and ankle angle right (ankle angle left, ankle angle right). There are 10 samples of data for each data type (ankle angle left, ankle angle right, GRF left, GRF right). Each sample denote data from stance gait cycle. In the shared excel file, data is organised in 4 sheets. Each sheet denotes the below; Sheet 1 (ankle_angle_left)- consists of 10 samples of ankle angle extracted from left leg Sheet 2 (ankle_angle_right)- consists of 10 samples of ankle angle extracted from right leg Sheet 3 (GRF_left)- consists of 10 samples of GRFs extracted from left leg Sheet 4 (GRF_right)- consists of 10 samples of GRFs extracted from right leg Within each sheet, column 1 to 10 denotes data from Sample 1 to Sample 10 respectively.

MANUSCRIPT TITLE Analyzing the Optimization Process Underlying the Stabilization of the Upright Body Posture MANUSCRIPT AUTHORS Carla Porto, Thiago Lemos, Arthur de Sá Ferreira CODE IMPLEMENTATION BY Arthur S. Ferreira LAST UPDATE August 22, 2018 PLEASE FOWARD QUESTIONS, COMMENTS, REMARKS, AND BUGS TO arthur_sf@icloud.com SESSION INFO (sessionInfo()) R version 3.5.1 (2018-07-02) Platform: x86_64-apple-darwin15.6.0 (64-bit) Running under: macOS High Sierra 10.13.6 Matrix products: default BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib locale: [1] pt_BR.UTF-8/pt_BR.UTF-8/pt_BR.UTF-8/C/pt_BR.UTF-8/pt_BR.UTF-8 attached base packages: [1] stats graphics grDevices utils datasets methods base other attached packages: [1] TestFunctions_0.2.0 loaded via a namespace (and not attached): [1] compiler_3.5.1 README INFO The following R scripts are provided: 'Albertsen.R' for generating reading and analyzing data from literature. 'cop.processing.R' for digital signal processing. ‘optimization.R’ for analyzing data using the optimization variables. ‘test.func.optim.R’ for simulating and analyzing test functions for mathematical optimization procedures. There is no particular order to execute each script, although they follow the sequence of the structured manuscript. Just make sure all required R packages ('TestFunctions') are installed before running the scripts.

EEG data for 8 male and 8 female individuals

This dataset contains images of thin blood smear with normal red blood cell morphology and rouleaux red blood cell morphology. Ethical approval with approval number: NHREC/17/03//2018 was obtained from Kano state ministry of health. Blood samples from 100 malaria infected patients were collected from Asiya Bayero pediatric hospital, kano state, Nigeria. Thick and thin blood smear slides were prepared using field stain. To ensure there was no bias in slide preparation, slides used for hospital diagnosis prepared under limited and constrained conditions were used as such types of slides represent the true reality of malaria diagnosis in less developed countries.Thin blood smear microscopy was performed by an expert microscopist and each slide was labeled according to the presence of Rouleaux formation or not among others. Out of 100 samples collected, 28 samples had rouleaux formation morphology. A 12MP iPhone 10 camera was attached to a microscope’s eyepiece. Pictures of different field of views for each slide were captured using the iPhone’s camera. For each slide, a minimum of 10 different field of views were captured. 616 images were captured for slides with rouleaux formation. To create a balanced dataset an equal number, 616 images were also captured for slides with normal morphology. To increase the size and variation of the dataset. 312 Digital images of thin blood smear slides with Giemsa staining collected from Murtala Muhammad specialist hospital were added. out of the 312 images, 156 had rouleaux RBC morphology and 156 had normal RBC morphology. Image capture was conducted in the morning, afternoon and evening and in different rooms with different lighting conditions to introduce diverse levels of illumination in the images The captured images from both hospitals had a size of 4032x3024 pixels. The background of the images were cropped to give a size 2500x2500 which were then sliced to give a final size of 750x750 pixels. The final data set consists of 12,356 thin blood smear images with rouleaux formation morphology and 12,356 thin blood smear images with normal red blood cell morphology. Different CNN architectures were trained for the binary classification of the dataset.