11.3 Artifact removal and baseline correction
2 min read•july 18, 2024
Biosignals often contain unwanted artifacts that can distort the data. , , and are common culprits. Removing these artifacts is crucial for accurate analysis and interpretation of biological signals.
Digital filtering techniques like low-pass, high-pass, and can effectively remove specific types of artifacts. Baseline correction methods, such as and , further improve signal quality. Evaluating these techniques ensures optimal artifact removal and signal preservation.
Artifact Removal in Biosignals
Types of biosignal artifacts
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- Motion artifacts introduce low-frequency noise in the signal caused by patient movement or sensor displacement
- Power line interference introduces a 50 Hz or 60 Hz sinusoidal component in the signal, depending on the power line frequency, caused by electromagnetic interference from nearby electrical devices or power lines (power outlets, electrical wiring)
- Baseline drift introduces a low-frequency trend in the signal, causing the baseline to shift over time, caused by changes in electrode-skin impedance, respiration, or perspiration (sweating, deep breathing)
Digital filtering for artifact removal
- remove high-frequency noise and artifacts while preserving the low-frequency components of the signal, implemented using (FIR) or (IIR) filters (Butterworth, Chebyshev)
- remove low-frequency noise and artifacts, such as baseline drift, while preserving the high-frequency components of the signal, implemented using FIR or IIR filters (Butterworth, Chebyshev)
- Notch filters remove a specific frequency component, such as power line interference, implemented using a narrow band-stop filter centered at the interference frequency, can be realized using second-order IIR filters or FIR filters with a high order (50 Hz, 60 Hz)
Baseline Correction Techniques
Methods of baseline correction
- Polynomial fitting estimates the baseline drift using a low-order polynomial function (linear, quadratic), subtracts the estimated baseline from the original signal to obtain the corrected signal, the order of the polynomial should be chosen carefully to avoid overfitting or underfitting
- Wavelet-based approaches decompose the signal into different frequency scales using a (Daubechies, Symlet), identify the scales corresponding to the baseline drift, set the coefficients of the identified scales to zero and reconstruct the signal using the inverse
Evaluation of signal processing techniques
- measures the ratio of the signal power to the noise power, higher indicates better artifact removal and baseline correction
- Root mean square error measures the difference between the original and the corrected signal, lower error indicates better artifact removal and baseline correction
- Visual inspection qualitatively assesses the corrected signal for any remaining artifacts or distortions, compares the corrected signal with the original signal to ensure that important features are preserved ( in ECG, in EEG)
Key Terms to Review (19)
Adaptive Filtering: Adaptive filtering is a signal processing technique that automatically adjusts its parameters in response to changes in the input signal or environmental conditions. This method is particularly useful in biomedical applications for enhancing signal quality and removing noise, making it vital for analyzing and interpreting complex biomedical signals.
Alpha waves: Alpha waves are brainwave patterns in the frequency range of 8-12 Hz, predominantly observed during relaxed, calm states of mind while awake. These waves play a significant role in various aspects of cognitive function and are crucial for understanding brain activity during different mental states and their applications in biomedical fields.
Baseline Drift: Baseline drift refers to the gradual shift in the baseline level of a signal over time, often resulting in inaccurate measurements and interpretations. This phenomenon is common in biomedical signals, where it can obscure the actual physiological information being measured. Understanding baseline drift is crucial for properly analyzing signals like ECG, EEG, and EMG, and for implementing effective artifact removal and correction techniques.
Finite Impulse Response: Finite impulse response (FIR) refers to a type of digital filter that responds to an input signal for a finite duration, characterized by a finite number of coefficients. FIR filters are commonly used in signal processing for tasks such as artifact removal and baseline correction, as they can precisely control the frequency response and introduce no phase distortion. These filters are implemented using a fixed number of past input values, making them stable and straightforward to design.
High-pass filters: High-pass filters are electronic circuits that allow signals with a frequency higher than a certain cutoff frequency to pass through while attenuating frequencies lower than that threshold. They are widely used in various applications, including biomedical signal processing, to enhance the quality of signals by removing low-frequency noise and interference, making the desired high-frequency components more detectable. This filtering process is particularly valuable in contexts like artifact removal and baseline correction.
High-pass filters: High-pass filters are electronic circuits or algorithms that allow signals with a frequency higher than a certain cutoff frequency to pass through while attenuating signals with frequencies lower than that threshold. This filtering technique is crucial in enhancing the quality of biomedical signals by removing low-frequency noise and unwanted baseline drift, making it vital in various applications such as signal amplification, artifact removal, and improving the clarity of electroencephalogram (EEG) signals.
Infinite Impulse Response: Infinite Impulse Response (IIR) refers to a type of digital filter that produces an output that continues indefinitely in response to an impulse input. Unlike finite impulse response filters, IIR filters use feedback mechanisms, allowing them to create an output that can theoretically persist forever, resulting in complex behavior in signal processing applications. This characteristic makes IIR filters efficient for implementing certain types of frequency responses and is particularly useful in applications like artifact removal and baseline correction where ongoing adjustments are necessary.
Low-Pass Filters: Low-pass filters are signal processing tools that allow low-frequency signals to pass through while attenuating or blocking higher-frequency signals. They are essential in various applications, helping to smooth data by removing unwanted high-frequency noise, which is particularly useful in analyzing biomedical signals. These filters play a significant role in cleaning up signals for better interpretation and are crucial in techniques like artifact removal and baseline correction.
Low-pass filters: Low-pass filters are electronic circuits or algorithms that allow signals with a frequency lower than a certain cutoff frequency to pass through while attenuating higher frequencies. These filters are essential for reducing noise and preserving useful signal components, making them invaluable in many applications where clean signal processing is critical.
Motion artifacts: Motion artifacts are unwanted variations in biosignal recordings caused by movement of the subject, equipment, or both, which can distort the true representation of the physiological signals being measured. These artifacts can significantly hinder the accuracy of data interpretation and may obscure important features in the signal, making it crucial to identify and manage them effectively to ensure reliable results.
Notch Filters: Notch filters are specialized signal processing tools designed to eliminate or significantly attenuate specific frequency components from a signal while allowing others to pass through unaffected. They are particularly useful in biomedical applications where unwanted noise or interference can obscure important signals, making them essential for enhancing the clarity and quality of data collected from various physiological signals.
Polynomial Fitting: Polynomial fitting is a mathematical technique used to find a polynomial equation that best approximates a set of data points. By adjusting the coefficients of the polynomial, this method helps in capturing trends and patterns within the data, making it easier to analyze and interpret. In contexts involving noise and fluctuations, polynomial fitting is particularly useful for removing artifacts and correcting baseline shifts, thereby enhancing the quality of the data analysis.
Power Line Interference: Power line interference refers to the unwanted noise that can be introduced into biosignal recordings due to electromagnetic fields generated by electrical power lines and devices. This type of interference can obscure the true signals being measured, leading to inaccuracies in data analysis and interpretation. Understanding and mitigating this interference is crucial for improving signal quality and ensuring accurate biosignal processing.
QRS Complexes: QRS complexes are the graphical representation of the electrical depolarization of the ventricles in the heart, seen in an electrocardiogram (ECG). This component of the ECG signifies the moment when the ventricles contract, sending blood to the lungs and the rest of the body. Understanding QRS complexes is crucial for interpreting heart rhythms, diagnosing various cardiac conditions, and ensuring accurate artifact removal and baseline correction in ECG analysis.
Root Mean Square Error: Root Mean Square Error (RMSE) is a measure used to quantify the difference between values predicted by a model and the values observed. It provides a way to gauge how well a model performs in predicting outcomes, with lower values indicating better fit. In practical applications, RMSE is vital for assessing the accuracy of models, especially when removing artifacts from signals or identifying system dynamics.
Signal-to-Noise Ratio: Signal-to-noise ratio (SNR) is a measure used to quantify how much a signal stands out from the background noise, typically expressed in decibels (dB). A higher SNR indicates a clearer and more distinguishable signal, which is crucial for accurate data interpretation and analysis in various applications, especially in the biomedical field.
Wavelet Transform: Wavelet transform is a mathematical technique that decomposes signals into components at various scales, allowing for both time and frequency analysis. This method is particularly useful in extracting features from signals, detecting anomalies, and processing biomedical data, making it a powerful tool in fields such as signal enhancement, artifact removal, and rhythm analysis.
Wavelet transform: Wavelet transform is a mathematical technique used to decompose signals into different frequency components, allowing for both time and frequency analysis simultaneously. This powerful tool provides a flexible way to analyze non-stationary signals by breaking them down into localized wavelets, enabling more effective feature extraction and noise reduction.
Wavelet-based approaches: Wavelet-based approaches are mathematical techniques that utilize wavelets to analyze and represent signals in a way that captures both frequency and temporal information. These methods allow for effective decomposition of signals into various frequency components, making them particularly useful for tasks such as artifact removal and baseline correction in biomedical signals.