Biosignals - Science topic

Dear Eduard Babulak ,

The role of AI and computational algorithms in the processing of biosignals and medical images is critical for disease diagnosis and treatment planning. These technologies significantly enhance diagnostic accuracy by identifying subtle patterns and abnormalities that may be missed by human experts. AI-driven tools can automate the analysis of vast amounts of medical data, reducing the time required for diagnosis and enabling quick decision-making in critical clinical settings. Furthermore, AI algorithms support personalized treatment planning by analyzing patient-specific data, leading to more effective and targeted therapies. They also play a key role in early disease detection and prognosis, which are essential for successful treatment. By reducing the risk of human error and supporting healthcare professionals in interpreting complex medical data, AI enhances the consistency and reliability of diagnoses. Additionally, AI contributes to medical research, facilitating the development of new diagnostic tools and treatment options by analyzing large datasets. Overall, the integration of AI and computational algorithms into healthcare is transforming the field, leading to better patient outcomes and more efficient medical practices.

There are several classification methods commonly used for biomedical signals. Some of them include:

It's worth noting that the choice of classification method depends on the specific characteristics of the biomedical signals, the size of the dataset, and the objectives of the analysis.

The Minimum Energy Combination (MEC) spatial filter method can be combined with other techniques to improve SSVEP detection classification accuracy. One such technique is the Common Spatial Pattern (CSP), which is a well-known spatial filtering technique used for EEG signal processing.

The combination of MEC and CSP can be used to improve the classification accuracy of SSVEP detection. The MEC method is used to estimate the frequency components of the SSVEP signals, while the CSP method is used to improve the signal-to-noise ratio and separate the relevant signals from the irrelevant ones.

By combining the MEC and CSP methods, the resulting spatial filters can better extract the relevant SSVEP signals from the EEG data, leading to improved classification accuracy. This approach has been shown to be effective in several studies and is a promising direction for further improving SSVEP detection classification accuracy.

Dear Lokeshwaran .S ,

In fact you can use any impedance meter to measure the skin impedance All what you need is to stick to metallic probes on the skin and measure the impedance between the two electrodes by this impedance meter. There are many RLC meters that measures the impedance of the skin where it is equivalent to a parallel combination of a resistance and a capacitance. You have to press the electrodes against the skin to avoid the existence of air gaps between the electrode and the skin. You can use salty solutions to wet the electrodes. In this way you will get reproducible results.

You can also use the method in the paper at the given link:

I used this method to measure the skin impedance and it gave good results.

Best wishes

Thank you Aparna Sathya Murthy Fehmi Özel and Jose Risomar Sousa for your suggestions. Moving average method on AD8232 data works fine.

Please save the data in TXT or notepad format, then read that file in matlab. Another way is to save the data in XLX and use in matlab as the row and column. Fantini Carole

Hi, maybe this work would be helpful;

Good question

Dir Dobromir Dobrev

Welcome!

I would like to greet the respected colleagues Lutz and Nicolay.

I have some comments on the the circuit above!

What is the probe internal resistance.

In biomedical probes the internal impedance is very large may be much larger than the potential divider in the input. The divider in the input limits the input impedance of the instrumental amplifiers.

The instrumental amplifiers has the capability to cancel the common mode voltages. So, one can directly connect the electrodes to the input of the instrumental amplifiers. The active electrode and the reference electrode.

If frequency filtration is required it can be accomplished as the last step.

This circuit has components that do not add to the functions of the circuit:R3 and R4 together with the UA1,A.

Best wishes

ECG IMAGES DATASET OF CARDIAC AND COVID-19 PATIENTS

Dataset Link

http://dx.doi.org/10.17632/gwbz3fsgp8.1

This could interest you:

Kaggle:

https://www.kaggle.com/plameneduardo/sarscov2-ctscan-dataset Github: https://github.com/Plamen-Eduardo/xDNN-SARS-CoV-2-CT-Scan Paper: https://www.medrxiv.org/content/10.1101/2020.04.24.20078584v3 Original Video: ELLIS Against COVID19 https://www.youtube.com/watch?v=7RtDgGkxavc

Andrea Němcová please what are the most important human signals to take into consideration, related to COVID-19? my be i can help.

Dear Jacob,

Hope you are well,

I may came late to this question but i found it is related to the basic principles of signal processing.

The filters would output discrete frequency bands according to the frequency ranges of the filters used. Filter would take much computation time than the FFT.

In fact FFT is a sampling process of the signal in frequency domain. So, i would propose that you use the method which gives you the sufficient signal features that you increase your identification. The filter bank also is frequency analysis method. But the FFT is a systematic method to get the frequency components of the signal. I believe the FFT is an elegant filter bank.

However you also run an experiment to assess these method experimentally.

Best wishes

Have a look at https://otbioelettronica.it/en/

The team are very helpful and they hold an annual usergroup meeting to discuss advances and applications.

Oh, seems very nice and from an afidable source.

Hi,

first of all is important to preprocessing the EEG signals, for this reason you must improve your knowledge in EEG processing.

I recommend you to read:

^{Bigdely-Shamlo N, Mullen T, Kothe C, Su KM, Robbins KA. The PREP pipeline: standardized preprocessing for large-scale EEG analysis. Front Neuroinform. 2015 Jun 18;9:16. doi: 10.3389/fninf.2015.00016}

Moreover, for the first step of analysis i suggest to try to analyze resting state EEG with a matlab toolbox with GUI as eeglab.

Dear friend

I suggest you the following:

1- Use Arduino Due to collect the data because its cheap and fast.

2- Use Tera-term software to log the data from Arduino.

3- Save the data in text file then:

3- Use MATLAB to analyse the data. (import the text file from MATLAB).

Best regard

Hi Ranjeet,

The CYBHi is available for download as open data here:

A report of the dataset and protocol is available here:

Let me know in case you need any support with either.

Best regards,

Hugo Silva

I am afraid copper would start to electrochemically interact with the skin (not really dry - especially after cleaning it from fat to lower the impedance) very soon (minutes) which would change the impedance of your setup or even introduce a potential like a battery (skin pH<>0). Ag/AgCl in combination with a NaCl gel remains very stable during the period of EEG measurement.

Yes, summing amplifier is one of the right answers, An inverting summing amplifier should be implemented to develop the common body reference circuit. Then the inverting summing amplifier provides an average output results with the right ratio of resistors values. I've design 4 channel EMG circuits and I've used summing amplifier for for averaging the Body references

WBAN or WBASN is subclass of WSN. WBAN is specially for human body and biosignals. The protocol will take consideration of its effects in human.

RFID has two components , the rf tag and the rf tag reader. The communication between the two components is through the rf waves. The reader is able to instruct the tag to read and send its id information to the reader where it will be decoded and displayed.

For the vendors of the RFID products in India please refer to the link: https://dir.indiamart.com/impcat/rfid-tag.html?biz=10

You can choose the devices with the specifications and prices suitable to you.

You can use biometrics also for the identification by loading them in the TAGs

You can also connect the reader to a microelectronic to control the whole system because the reader has a micro controller.

Best wishes

you may consider this articl

Moujza,

There is an interaction between the electrode and the biological environment during recording and stimulation (These are essentially similar but different processes btw). Electrons are negatively charged particles that can freely move in conductive materials such as silver and copper. And it is a subject of electricity. However, in a conductive media, such as saline or biological tissue, the charge carrying particles are ions like Na and K. Now, this is subject of volume conduction. Ions can be negatively or positively charged.

During stimulation, very complex electrochemical processes occur. Some of these processes are reversible (capacitive mechanisms) and some of them are irreversible (faradaic mechanisms), meaning that the recording/stimulating electrode loses some of its material during the electrochemical reactions. As I said, very complex phenomena and needs a little bit of research to comprehend.

The same mechanisms, but mostly capacitive, work during recordings. When the ions move inside the tissue for some reason (for example the activation of excitable cells) they generate ionic currents. These ionic currents generate an electric field. If you place an electrode close enough to the electric field, you can pick up the changes in the potential around the electrode, and this appears as a varying voltage. I am not sure if it is called "conversion", but yes there is some electron trading happens between the electrode and ionic solution around it. But mostly, the electric field generated by the ionic currents "influence" the electrons inside the metal electrode. (Search for electrode-electrolyte interface for details).

Below is a paper talking about neural recording and stimulating electrodes authored by a highly respected person in the field. It is a bit long but I am sure that you will have a good understanding after reading it.  

Dear Carel Bron,

we recently used the system from Cometa (http://www.cometasystems.com),  the Gereonics miniturized electrodes (http://www.gereonics.com/biopotentialelectrodes.html) and the EMG Easy Report software (http://merlobioengineering.com/emg-easy-report/) to assess the motion pattern (onset/offset time and activity profile) of muscles that control the motion of the scapula (Picture attached) and to obtain normal reference data for shoulder and upper limb muscles during reaching movements.

Miniaturized electrode were used to prevent crosstalk and placed according to Basmajian & Blanc. Muscle onset was computed according to Merlo et al 2003, IEEE Trans. Biomed Eng.

I hope it can help.

I am the main author of the EMG Easy Report software, thus I have a conflict of interest to discolse.

Kind regards.

Andrea Merlo

 From the general machine learning perspective : You try to interpret the data that the network was not trained on. As the artifacts are far away from the normal signal input space, the network will identify artifacts incorectly (marginal receptive fields, extreme values of input space). 

From ICA perspective: The network (ICA matrix) will backproject the artifacts to all components, but the iterpretation of this backprojection is corrupted as it is insesitive to this type of signal.

From EEG perspective: There is problem that artifact is mostly presented in all channels (all dimensions of input space). I think that you cannot interpret projection of artifactual signal in component space. You should put the arifacts into the ICA and let them identify as the artifactual components. You know better than me that this procedure will distort good components.  

The only reason I can think of, where you might want to do this, would be if you knew there was a strong interfering sinusoid, at a frequency in between two DFT frequency bins. Without pre-filtering, the energy due to this interferer would spread over all nearby DFT bins, and might mask/hide weaker signals of interest there. You may gain something if you designed a notch filter to remove the interferer, before doing your spectrum estimation. But yes, the filter may also adversely affect signals of interest.  

But you could do the filtering in the frequency domain anyway, after you have computed the spectrum.

In general I would say that your intuition is correct - just look at the band of interest and don't bother filtering.      

There has been some discussion using Golden Mean as a combination method for basic frequency combinations recently.  Attached are some links that may be useful to you.

I work with Kubios Software. If you transfer your data in a .txt file it would be working.

Hi Pradeep,

The answer is...it depends. It depends on what you want to do with your rectified signal. The absolute (ABS) will  only put all your negative values positive. The ARV will also do that but will add another treatment as doing the mean over a time window (0.1 second by defaut) so your rectified signal will also be smoothed. Usually, we're doing some smoothing after rectifiying the EMG so the ARV might be a good approach in this case by choosing the appropriate time window. Anyway, if you choose either type of treatment, be sure to remove any DC offset from your EMG (even if there's not suppose to be one...ideally) by substracting the mean of your raw signal first or doing a highpass/bandpass filter on it before applying the rectification.

Hope this helps,

Michel

Maybe here:

If you do not have a digital oscilloscope, you can play with trigger function and you should be able to see the EMG signal.

Dear Archith

Your question has different answers in different field of processing, However, I recommend you in first step study previous works related to your field of research, then guess the frequency band which is important to you then filter rest of bands.

for instance, I can point out on my work, which was related to Parkinson patients tremor, based on previous works their frequency is about 3-12 Hz, so I passed these frequency band and filtered rest of bands then I implemented my processor based on features which had been extracted in this frequency band.

There's so many. Most of these are Matlab toolboxes, but you can either get access to that or get someone to compile them for you (not that hard):

This one is in R:

This is a standalone:

I have a recent research paper Alireza, which may be of your interest. Please see the attachment.

Maybe also interesting is that glucose -like most sugars- rotates the polarisation of electromagnetic waves passing through.

Guessing for "PSR ..Gd "

PSR = Power Spectrum Residue (?)

Gd = Good, as @Sandy Rihana said "Gd luck" (Good luck)

I am not sure I would say that Titan does not harbor life. First, if Titan has a deep ocean it could well have life there. http://science.nasa.gov/science-news/science-at-nasa/2012/28jun_titanocean/

Second, I would by no means regard the issue of surface life _not_ as we know it, possibly a very slow moving biology using methane or ethane as a working fluid.

On a higher level, for whatever reason biology has basically been ignored in space exploration since circa 1976. (Of course, a lot has been done in the lab and from Earth based telescopes.) I personally think that the low-hanging fruit here could be atmospheric biospheres.

Venus has a level in its atmosphere that could support Earth life and is not in chemical equilibrium. This has been known for decades, never followed up.

The Viking biology experiments had mixed results, but were ignored based on mass spectrometer data that has now been shown to possibly be in error. Known for decades, not followed up. At least there is an active Mars program, so I am willing to give this one a bye.

Jupiter has a plethora of regions not in chemical equilibria, and regions with stagnant flow (i.e., not quickly recycled), including the Great Red Spot. Jupiter (and Saturn) also have level with Earth like temperatures (~ at the 10 bar level).

Given that the Earth has a "high biology" http://ijs.sgmjournals.org/content/56/7/1465

https://www.technocrates.org/bacteria-in-the-clouds/36005/ there is in my opinion no reason not to search Venus, Jupiter and Saturn for similar biologies.

Hi,

both methods (orders of processing) might be of interest. The first - computing the ERP and then the spectral power per subject - is producing the evoked oscillation, in which you only see the frequency response that is locked to the stimulus onset (mostly early components of the ERP or big ones like the P300 if it is not 'jittering' too much). The second - computing the spectral power of your trial and then averaging over all these spectograms - produces the induced oscillations. They also reflect oscillatory activity that is not stimulus locked - often later responses that are not immediately caused by the stimulus but some higher processes/feedback from regions further down the processing stream. Have a look at Tallon-Baudry & Bertrand 1999 for a nice introduction to this topic: Oscillatory gamma activity in humans and its role in object representation.

C Tallon-Baudry, O Bertrand - Trends in cognitive sciences, 1999 - Elsevier

In the end your choice depends probably on the processes you are most interested in :)

cheers,

C.