![]() ![]() However, if you reduce your range too much, you can cut off peaks and troughs and lose important details of your input signal. When you reduce the range to fit your signal, you will get better resolution of your signal. The webinar will show you how to visualise your waveform as a series of individual points to check this AmplificationĪlso known as range, amplification is the selection of values between which your hardware will look for information and has a direct effect on resolution. To increase the detail recorded or the smoothness of the curve, you can go much higher - to 200 or even 400 Hz.Īs a more general rule, a sampling rate that gives you around 20 data points for the peaks of your waveform signal will give you a smooth curve with adequate detail. you are recording ECG in humans that has components which can reach up to 50 Hz as their highest expected frequency, so the minimum sampling rate should be 100 Hz. Nyquist frequency = 2 x highest expected frequencyĮ.g. The minimum rate at which digital sampling can accurately record an analog signal is known as the Nyquist Frequency, which is double the highest expected signal frequency. Here are two rules to keep in mind when you are choosing your sampling rate. While often your equipment will come with suggested sampling rates for different signals, it’s a good idea to check them rather than following the suggestions blindly. Two rules to help you choose your sampling rateĪppropriate sampling rates depend on the signal to be measured. As you will see, the space between the points has an effect on how the curve is represented when they are joined. Each sampled point is recorded by your analysis software and contributes to the resulting curve. For example, if your sampling rate is 10 Hz, your software will record a data value 10 times in every second. The regular interval at which the software ‘asks’ the DAQ unit for the voltage of the signal, is known as the sampling rate. The resulting digital signal is sampled (or recorded) at regular intervals by your analysis software which, in turn, will store and display the data on your computer. ![]() In this case, you will be using a transducer to collect an analog signal, which is converted to a digital signal by your DAQ unit. We’ll focus on biological signals recorded via a data acquisition unit (DAQ). Here, we’ll discuss the essentials of optimizing your sampling rate, amplification and filtering settings to get the best data quality for waveform signals. And while it can seem tricky to work out the optimal settings for your signal, it is well worth doing right early on - understanding whether your signal needs conditioning as you are recording is much more efficient than trying to manipulate your data retrospectively when some information may be lost. It is the first variable you want to eliminate, leaving you to concentrate on interpreting a result rather than worrying about lack of resolution or introduced artifacts. All scientists know the value of high quality and consistent data. ![]()
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