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Research Resources

Data

If you would like access to particular data, please contact me via email.

Biomedical Signal Quality Analysis

The Matlab files will enable people researching biomedical signal quality analysis to have a common methodology to compare against.

Keywords: biological signal, biosignal, electrocardiogram (EMG), Matlab, signal quality index (SQI), signal processing, signal quality analysis

Usage

If you are using these files (or a modification of these files) provide an acknowledgment (e.g. in publications) for their usage. Usage of these files (or a modification of these files) should reference:

 (20-07-16) Example code of SQI that contaminates ECG with motion artifact at different levels of SNR and shows how the SQI varies with SNR.

 signal quality index for ECG

Motion Artifact Signal Generation Toolkit

These files include simulated motion artifact, pretrained models to simulate motion artifact, and the ability to train new models. The models include an autoregressive (AR) model, a Markov chain model, and a recurrent neural network (RNN). The simulated motion artifact can be added to bioelectric signal recordings (e.g., ECG, EMG) for biomedical signal quality analysis research.

Keywords: biological signal, biosignal, electrocardiogram (EMG), motion artifact, python, signal processing, signal quality analysis

Usage

If you are using these files (or a modification of these files) provide an acknowledgment (e.g. in publications) for their usage. Usage of these files (or a modification of these files) should reference:

 (22-08-08) Simulated motion artifact, pretrained models to simulate motion artifact, and software to train and simulate motion artifact.

Motion Artifact Signal Database

These files include motion artifact signals obtained by taking 84 ambulatory ECG recordings from the  and removing the ECG. Each recording is about 24-hours in length.

Keywords: biological signal, biosignal, motion artifact, signal quality analysis

Usage

If you are using these files (or a modification of these files) provide an acknowledgment (e.g. in publications) for their usage. Usage of these files (or a modification of these files) should reference:

 (23-04-22) A sample of 10 motion artifact signals, each around 24-hours in length.

Myoelectric Control (MECLab)

The Matlab files will enable people researching MES/EMG classification methods to have a common methodology to compare against. The methodology used is a relatively simple and direct approach using ULDA feature reduction and an LDA classifier; however, it has shown to be quite effective.

Keywords: biological signal, electromyography (EMG), feature reduction, Matlab, myoelectric control, myoelectric signals (MES), pattern classification, prosthetic control, prosthesis, signal processing

Usage

If you are using these files (or a modification of these files) provide an acknowledgment (e.g. in publications) for their usage. Usage of these files (or a modification of these files) should reference:

Myoelectric Control Example

 (10-02-08) Example code and data to classify eight channels of myoelectric data to predict seven upper arm motions (i.e. using electromyography (EMG) signals for control of upper limb prostheses).

Feature Extraction

 root mean square feature

 mean absolute value feature

 integrated absolute value feature

 autoregressive feature

 zero crossing feature

 slope sign change feature

 waveform length feature

 creates a scatter plot of the feature vector (likely want to use pca or ulda feature reduction first)

Feature Reduction

 principal component analysis feature reduction

 uncorrelated linear discriminant analysis feature reduction

Classification

 converts columns of scores into classification outputs (numbers)

 generates a confusion matrix

 plots a confusion matrix

 plots a confusion matrix in text format

 find the rank of a particular class given columns of scores

 converts columns of scores into ranks

 performs majority vote post processing on classification decisions

 plots classification results as a function of time

 classification performed by linear discriminant analysis

 classification performed by k-nearest neighbors

Myoelectric signal processing

 computes the mean frequency

 (09-10-07) computes the median frequency

 computes the signal-to-motion artifact ratio

 computes the maximum-to-minimum drop in power density

 computes the signal-to-noise ratio

 computes the spectral deformation

Miscellaneous

 (10-02-08) will remove transitional data (e.g. from a time series of feature vectors)

ECG Person Identification

Keywords: biological signal, electrcardiography (ECG), electrcardiogram, wavelet, Matlab, biometric, person identification

Usage

If you are using these files (or a modification of these files) provide an acknowledgment (e.g. in publications) for their usage. Usage of these files (or a modification of these files) should reference:

 ECG from 10 subjects from three sessions on separate days.

 wavelet distance measure

Adaptive Signal Processing

 adaptive filter using the LMS algorithm

 adaptive filter using the RLS algorithm

Miscellaneous

 finds the delay (in samples) between two signals

 logical function to compare numbers to see if they are within a certain tolerance of each other

 removes the mean from signals that are arranged in columns

 computes the Gaussian probability distribution function

 computes the log Gaussian probability distribution function

 computes the percent residual difference

 computes fft with corresponding frequencies (fftshift is optional)

 software to configure the Grass-Telefactor Model 15 Neurodata Amplifier System

 this function is an implementation of the MOBD algorithm for QRS detection

 this function loads data from Axon file (generated from AxoScope)

Disclaimer

The files provided are distributed “AS IS” and “WITH ALL FAULTS”. We do not offer a warranty for the content or use of these files, nor do we guarantee their quality, accuracy, fitness for a particular purpose, or safety, either expressed or implied. All questions, complaints, issues, and claims related to files should be directed to the contributing author.

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