In biological signal measurement, “noise” refers to unwanted interference that can obscure or distort true signals. Myogenic noise is a common type, arising from the electrical activity of muscles. It presents a significant challenge in accurately capturing and interpreting biological signals, particularly when measurements are taken near muscle tissue.
How Muscle Activity Generates Myogenic Noise
Muscles generate electrical signals through a process called muscle contraction. This process involves the depolarization of muscle cell membranes, which creates transient electrical impulses known as action potentials. These action potentials are the fundamental units of electrical activity in muscle fibers. They propagate along the muscle fibers, leading to muscle contraction.
When electrodes are placed on the skin to record biological signals, they can inadvertently pick up these electrical signals from nearby muscles. The electrical fields generated by muscle action potentials extend beyond the muscle fibers. If recording electrodes are positioned over or adjacent to active muscles, these electrical muscle signals are detected as unwanted interference alongside the intended biological signal.
Impact on Biological Signal Measurement
Myogenic noise impacts the clarity and accuracy of biological signal measurements. It contaminates physiological data, making the actual signal of interest difficult to discern. This contamination can lead to misinterpretations and inaccurate conclusions drawn from the recorded data.
For instance, in electroencephalography (EEG), which measures brain activity, muscle artifacts from facial or scalp muscles can obscure subtle electrical patterns originating from the brain. Similarly, in electrocardiography (ECG), used to assess heart activity, muscle movements from the chest or limbs can introduce interference that distorts the heart’s electrical waveform. Electrooculography (EOG), which records eye movements, also frequently encounters myogenic noise from the muscles surrounding the eyes, complicating the analysis of eye-related signals. Myogenic noise can mask genuine physiological events or be mistakenly identified as part of the signal, compromising diagnostic accuracy or research findings.
Recognizing Myogenic Noise
Myogenic noise exhibits distinct characteristics in recorded biological signals, aiding identification. It typically appears as irregular, high-frequency fluctuations or sharp, spike-like artifacts in the signal trace. The frequency content of myogenic noise often spans a higher range, extending from tens to several hundred hertz, which can differentiate it from many physiological signals of interest like brain waves.
Visual inspection of the raw data often reveals these characteristic patterns, especially when they correlate with observable muscle tension or movement by the subject. For example, clenching the jaw or tensing the neck muscles will typically produce a noticeable increase in these high-frequency components. Recognizing these visual and frequency signatures helps distinguish muscle artifacts from genuine biological signals.
Strategies for Minimizing Myogenic Noise
Minimizing myogenic noise involves subject preparation, hardware optimization, and advanced signal processing techniques. Proper subject preparation includes instructing them to relax and avoid unnecessary movements or muscle tension during recording. Strategic electrode placement away from major muscle groups also reduces muscle electrical activity pickup. Ensuring good skin contact and proper electrode impedance further minimizes noise.
Hardware solutions contribute to noise reduction using specialized electrodes and optimized amplification systems. High-quality electrodes with low impedance can improve the signal-to-noise ratio. Differential amplifiers are often employed to amplify the difference between two input signals while rejecting common-mode noise, including muscle artifacts. Shielding the recording environment and cables also prevents external electrical interference.
Signal processing techniques mitigate residual myogenic noise after data acquisition. Digital filtering, such as band-pass filtering, can selectively remove high-frequency components characteristic of muscle activity while preserving lower-frequency physiological signals. Advanced algorithms like Independent Component Analysis (ICA) can mathematically separate independent sources within a mixed signal, isolating and removing muscle artifacts. These approaches enhance the purity and interpretability of recorded signals.
References
1. Myogenic Artifact in EEG. https://neuro.unlv.edu/Myogenic%20Artifact%20in%20EEG.pdf
2. EEG Artifacts – an overview | ScienceDirect Topics. https://www.sciencedirect.com/topics/neuroscience/eeg-artifacts
3. Artifact Removal Methods – an overview | ScienceDirect Topics. https://www.sciencedirect.com/topics/neuroscience/artifact-removal-methods
4. ICA-Based Artifact Removal – an overview | ScienceDirect Topics. https://www.sciencedirect.com/topics/neuroscience/ica-based-artifact-removal