Could Fast Fourier transform (FFT) be used in medical Electromyography (EMG)

tests to minimize the on-time for electrical nerve stimulation?

Yes, the Fast Fourier Transform (FFT)—and frequency-domain signal processing in general—can absolutely be used to help minimize the "on-time" (both the duration of individual electrical pulses and the cumulative number of shocks) during medical Electromyography (EMG) and Nerve Conduction Studies (NCS).

Because electrical nerve stimulation is notoriously uncomfortable for patients, minimizing the duration and intensity of the shocks is a major goal in biomedical engineering. While FFT is a mathematical data-processing tool and cannot change the underlying biology of a nerve, it enables advanced signal extraction. By fundamentally changing how well a machine can "hear" a nerve's response, the hardware doesn't have to "shout" as loudly with electrical current.

Here is exactly how FFT is applied to minimize stimulation on-time and reduce patient discomfort:

1. Enabling Ultra-Short Pulse Widths (Less Painful Shocks)

• The Problem: The pain a patient feels during an EMG is largely dictated by the "Strength-Duration curve." To depolarize a nerve, a shock must have a certain amplitude (intensity) and a certain "on-time" (pulse width, usually 0.1 to 1.0 milliseconds). Longer pulse widths give slower, unmyelinated pain fibers (C-fibers) time to depolarize, causing lingering pain.
• The FFT Solution: Clinicians can reduce the pulse width to a fraction of a millisecond to selectively trigger fast motor fibers while bypassing pain fibers. However, this yields a much weaker, noisier biological signal (a submaximal response) that is hard to see on a standard time-domain screen. By applying an FFT, the software translates the noisy recording into the frequency domain, isolating the specific frequency bands of the muscle response (typically 20 Hz to 500 Hz) and mathematically filtering out the noise. Because the software can now extract a tiny signal flawlessly, the clinician can use a much shorter stimulation pulse width.

2. Eliminating the Need for Repetitive Signal Averaging

• The Problem: Sensory nerve responses (SNAPs) are incredibly small. Because they are easily drowned out by ambient room noise and baseline wander, standard time-domain EMG machines require the technician to shock the patient 10 to 30 times in rapid succession. The machine averages these shocks together to cancel out random noise.
• The FFT Solution: Because FFT-based spectral analysis separates the true physiological signal from ambient noise (like 50/60 Hz powerline interference) with surgical precision, a high Signal-to-Noise Ratio (SNR) can be achieved from just a single pulse or a very small cluster of pulses. By extracting a clean diagnostic reading from 2 shocks instead of 20, the total cumulative "on-time" of the testing session is heavily minimized.

3. Rapid Stimulus Artifact Rejection

• The Problem: The electrical shock delivered to the skin creates a massive "stimulus artifact"—a spike of electrical interference that can blind the recording electrodes for several milliseconds, often masking the actual biological response.
• The FFT Solution: Advanced artifact-adaptive filtering uses FFT to identify the exact spectral footprint of the stimulator's electrical artifact. Because the artificial shock has a different frequency signature than biological muscle tissue, the FFT can map the artifact in the frequency domain, subtract it, and use an inverse-FFT to rebuild the clean signal. This prevents the clinician from having to repeatedly increase the stimulus duration or intensity to "outpace" or overpower the artifact.

4. "System Identification" (Bypassing Painful Shocks Entirely)

• The Problem: To map a nerve's health, standard NCS requires a sudden, high-voltage "supramaximal" shock to fire all nerve fibers simultaneously.
• The FFT Solution: In modern neuro-engineering research, FFT allows for a "transfer function" approach called system identification. Instead of a series of escalating, painful square-wave pulses, the machine delivers a continuous, ultra-low-voltage (sub-sensory or barely perceptible) "pseudorandom" electrical noise into the nerve. The system uses FFT to calculate the cross-correlation between this tiny input signal and the raw EMG output. This mathematically reconstructs the exact same "Impulse Response" (the diagnostic waveform) as a high-voltage shock, but it spreads the electrical energy out over time at a level the patient can barely feel, entirely eliminating the painful high-energy "on-time" spike.
  

The Biological Limitation

It is important to note that while FFT is mathematically powerful, its ability to minimize on-time is strictly bound by human biology. To force a nerve to fire an action potential, a minimum electrical charge must be delivered. If the pulse "on-time" is reduced too much, the current (amplitude) must be inversely increased to trigger the nerve, which can also stimulate pain receptors.

Therefore, FFT cannot alter the fundamental physics of nerve depolarization, but it optimizes the diagnostic trade-off. By maximizing the amount of data extracted from weak or noisy signals, FFT ensures that the absolute shortest, weakest, and least painful electrical charge possible can still yield a highly accurate medical diagnosis.

Is Fast Fourier transform (FFT) currently used in medical Electromyography (EMG)

tests to minimize the on-time for electrical nerve stimulation?

No. In standard, everyday clinical practice, the Fast Fourier Transform (FFT) is not currently used to minimize the on-time (pulse width) or intensity of electrical nerve stimulation during medical Electromyography (EMG) and Nerve Conduction Studies (NCS).

While the mathematics and biomedical engineering concepts theoretically allow for advanced signal extraction from ultra-short, painless shocks (as is currently being explored in research labs), modern medical diagnostic standards strictly prevent this from being used in hospitals and clinics today.

Here is why FFT is not used to minimize the shock in standard clinical settings, how clinics actually handle the data, and what FFT is actually used for on modern machines instead:

The Clinical Roadblock: The "Supramaximal" Standard

To accurately diagnose how sick or injured a nerve is, a neurologist relies heavily on a measurement called Amplitude (the height of the electrical wave produced by the muscle or nerve). Amplitude tells the doctor exactly how many nerve fibers (axons) are alive and functioning.

To get an accurate amplitude, global medical guidelines require Supramaximal Stimulation.

• The machine must deliver an electrical shock with an on-time (pulse width) and intensity high enough to force 100% of the axons in the nerve bundle to fire at the exact same time.
• If a machine used FFT to extract a mathematically clean signal from a shorter, less painful "submaximal" shock, it would only be firing a fraction of the nerve fibers.
• You cannot mathematically "upscale" or guess what the full nerve response would look like based on a partial response. If a machine did this, an artificially low amplitude would be recorded, and the doctor would mistakenly diagnose a healthy patient with severe nerve damage (axonal loss).

Because doctors need to guarantee that every single nerve fiber has been forced to fire, the technician will actually find the electrical threshold where the muscle response stops growing, and then intentionally increase the shock intensity by another 10% to 20%. This guarantees supramaximal firing, but it completely negates any attempt to use FFT to minimize the shock's on-time or save the patient from discomfort.

How Clinics Currently Handle Noise

Instead of using FFT to extract a tiny signal from a single painless shock, modern commercial machines (like those from Natus, Cadwell, or Nihon Kohden) rely on brute-force, time-domain techniques:

• Time-Domain Averaging: To see tiny sensory nerve responses (which are easily drowned out by room noise), current machines do not use FFT to isolate the signal. Instead, the technician shocks the patient 10 to 30 times in a rapid cluster. The software overlays the recordings and averages them together. Because random room noise fluctuates but your nerve response is identical every time, the noise averages out to zero, leaving only the clean nerve waveform.

What is FFT actually used for on modern EMG machines?

Every modern clinical EMG machine has FFT capabilities built into the software, but the doctor uses them for analyzing the recorded muscle data, not for altering the input stimulation:

• Tremor Analysis: If a patient has a visible tremor, the doctor can place surface electrodes on the muscle and use FFT to instantly calculate the exact frequency of the shaking. This helps differentiate a Parkinson's disease tremor (typically 4–6 Hz) from an Essential tremor (typically 8–12 Hz).
• Quantitative Needle EMG (qEMG): During the needle portion of the EMG (where a pin is inserted into the muscle, but no shocks are given), FFT is used to analyze the frequency spectrum of Motor Unit Action Potentials (MUAPs) to help distinguish between nerve diseases (neuropathies) and muscle diseases (myopathies).
• Muscle Fatigue Tracking: In kinesiology and physical therapy, surface EMGs use FFT to monitor muscle fatigue. As a muscle gets tired, the frequency of its electrical firing reliably shifts to the lower end of the spectrum (the "median frequency shift"), which FFT maps in real-time.
  
• Digital Filtering: FFT principles run constantly in the background to identify and strip out 50 Hz or 60 Hz AC powerline noise generated by the lights and wall outlets in the exam room, ensuring the doctor has a clean waveform to look at.

Summary:

While university research labs and biomedical engineers are actively exploring ways to use FFT and submaximal continuous stimulation (like "pseudorandom noise") to replace painful shocks entirely, clinical neurology today still requires the traditional method. Until diagnostic criteria change globally to accept mathematically reconstructed waveforms, the painful, supramaximal shock remains the only medically accepted way to measure total nerve health.