Noisy signals giving you a headache? Looking for ways to improve the quality of your data?
Brandon Bucher, Head of Research at ADInstruments provides expert advice on avoiding common data acquisition and analysis mistakes in order to produce higher quality results.
Basics of Data Acquisition
Optimizing your data acquisition (DAQ) settings to suit your signal of interest is one of the first things you can do to improve the quality of your data. The diagram below gives a simplified explanation of the DAQ process. Initially, the biological signal of interest must be transitioned from a continuous analog signal into a digital signal, via a PowerLab data acquisition system. In order to make this transition, the PowerLab must do three things: Amplification, filtering and sampling.
Amplification, also known as range or resolution, is the action of taking out small signals as they exists in the transducer and making them larger, so that it's easier for the DAQ system to record them.
Sampling is the action of taking samples per time from a continuous signal to make a representative digital signal. Selecting the appropriate sampling rate for a particular biological signal is very important. If the sampling rate is too low you will lose information and resolution over time and your signal will not be represented correctly. Alternatively, if the sampling rate is too high, you run the risk of capturing excessive noise and artifacts in your signal.
Filtering, is removing any unwanted signal from the signal of interest. This can be done either before or after the amplification step. Most filters will fall under a few categories; a low-pass or high-pass filter, and then various combinations of the two. Below we have an example of a signal (in red) that is comprised of both a low frequency signal (slow and large amplitude) and a high frequency signal (faster and smaller amplitude).
Applying a low-pass filter will allow the low frequency waveform to pass through (in blue) where as a high-pass filter will allow the high frequency waveform to pass through (in green).
Dealing with Noise
A common question customers ask is how to mitigate the effects of noise during DAQ. Noise 'contamination' can come from anywhere - whether it be the equipment you are using in your preparation or a friend carrying out an experiment in the room next door. Identifying the source of noise may require some detective skills, however it is very important that you spend time finding and removing potential sources of noise as they can be extremely detrimental to your data. Here are Brandon’s top tips for dealing with noise:
One of the easiest ways to reduce noise, is to address the area surrounding your preparation. Make sure that it is clean and tidy by reducing clutter and the amount of power cabling that's in the immediate area.
Faraday cages are commonly used in electrophysiology applications to reduce noise around instrumentation. By grounding your preparation with a meshed metal cage, you can shield it from any surrounding electromagnetic interference.
Grounding Instrumentation (but avoid loops)
Similar to Faraday cages, grounding posts or rods can be used to ground instrumentation and reduce noise. It is important to be efficient or judicious with your grounding wires to avoid creating any grounding loops - as these will act like a large antenna, giving you the opposite effect!
Headstages/Near Subject Amplification
The addition of a head stage will allow you to amplify your signal closer to your preparation, reducing the time or distance noise has to be introduced into your signal before it is amplified.
Shielded cabling/Insulating cables
Shielding acts like a Faraday cage by protecting the wires inside from any interference. Another option is to insulate your cables with a mesh sleeve to reduce the amount of noise in your preparation.
The addition of a hardware or software filter can also be an effective way to mitigate persistent noise. One thing to be aware of is that hardware filters MUST be set correctly before you start recording data as they can not be removed post DAQ.
On the other hand, software filters are editable and can be enabled at any time, but are potentially less effective. See our Essentials to Data Acquisition guide for more information on hardware and software filters.
Smoothing has a similar action to filtering, but is advantageous in situations where it is difficult to tell the difference between your signal and your noise using frequency, or if your noise is intermittent or artifact like.
Data qualification, artifact rejection and automated analysis
In this section we will cover how to be prepared for data qualification, artifact rejection, repetition and automated analysis. Most of Brandon’s tips and ticks come down to being prepared and creating a set of rules/guidelines in your protocols to ensure your experiments are consistent and repeatable.
Using annotations and calculations for artifact rejection and data qualification
Our data analysis software LabChart, allows you to add annotations to your data both during and after DAQ. Being liberal with your comments and annotations can be an effective way to qualify data and determine which data should be kept and which should be rejected. Adding comments as you move through the experiment will allow you to easily navigate the data and know what is happening precisely when and where.
Brandon uses an example of creating a health check within LabChart by using the systolic pressure, mean arterial pressure, and changes in heart rate to measure the health of an animal after undergoing surgery. This provides a reliable way to determine whether the animal is healthy enough to continue on with the experiment. In research, your data must be reproducible in order for you to draw conclusions from it. By creating checkpoints at critical steps during your protocol, you can keep tabs on how the experiment is going and whether the results are 'real' or valid.
Identifying and removing artifacts from your signals can be a headache inducing thought for any researcher. Fortunately, LabChart can provide you with some low-level and high-level software tools to automate artifact rejection. Make sure you check out Brandon's demonstration in the video above (Chapter 11) for some simple, but effective ways to remove artifacts from your data. For a more detailed example of artifact rejection, check out our LabChart Mastery video on how to remove ECG Artifacts from EMG Data.
Take home messages
Go back to the basics - make sure your basic data acquisition settings like range, sampling rate, and any filters you might be using are optimized for your signal of interest.
Play by your own rules - have a set of rules in place before carrying out an experiment so you can have confidence in the validity of your data as well as a framework in place for any additional experiments you might carry out down the track.
Consistency is key - Apply these rules/guidelines across all you experiments, that way you can accurately and confidently compare data across multiple experiments!
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