What are the most common signal enhancement techniques in biomedical signal processing?
Biomedical signals are data that reflect the physiological activity of living organisms, such as electrocardiograms (ECG), electroencephalograms (EEG), or blood pressure. Biomedical signal processing is the application of mathematical and computational methods to analyze, enhance, and interpret these signals for various purposes, such as diagnosis, monitoring, or treatment. In this article, you will learn about some of the most common signal enhancement techniques in biomedical signal processing, and how they can improve the quality and reliability of biomedical signals.
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Nathália NevesEngenheira de Produção | SENAI | Educação e tecnologia
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Shreya K.Doctoral Researcher @ FAU Erlangen-Nürnberg | Computer Vision | Cognitive Science | Machine Learning
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João OliveiraBiomedical Engineer | Clinical Engineer | Healthcare Technology Management | Occupational Health and Safety Engineer
One of the main challenges in biomedical signal processing is to reduce the noise that can corrupt the signals and affect their accuracy and readability. Noise can originate from various sources, such as external interference, measurement errors, or biological variability. Noise reduction techniques aim to remove or minimize the noise components from the signals, while preserving the relevant information and features. Some of the most common noise reduction techniques are filtering, which applies a frequency-based criterion to eliminate unwanted components; wavelet denoising, which decomposes the signal into different scales and thresholds the noise coefficients; and adaptive filtering, which adjusts the filter parameters according to the signal characteristics and the noise statistics.
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A medição adequada na calibração de equipamentos é crucial para assegurar valores confiáveis. Isso é essencial para identificar origens de ruídos ou desgastes, agravados pela falta de medidas preventivas. Ruídos podem surgir de desalinhamento, desgaste, ou interferências. Seguir um procedimento validado de calibração é fundamental para mitigar essas fontes de erro. Estabelecendo padrões consistentes, o processo identifica desvios, mantendo a integridade dos equipamentos e reduzindo a probabilidade de ruídos decorrentes de uma calibração inadequada.
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The main issue with biomedical signals is that they have very low magnitude and a reduced bandwidth (except in the case of EMG), requiring the use of amplifiers with high gain to properly process the signal. However, high gain amplification can also amplify the same interferences that mask the signal, especially capacitances that occur between the patient and the electrical network or ground. Therefore, it is crucial to employ a differential amplification system. Additionally, considering that the maximum allowed interference level in a medical signal is 1% of the signal of interest, the common mode rejection ratios (CMRR) of the amplifiers must be very high, exceeding 120dB to obtain an acceptable signal.
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Dans le traitement des signaux ECG, l’élimination des interférences des lignes électriques 50/60Hz des formes d’onde ECG biomédicales délicates et riches en informations est une tâche difficile! Le défi est encore compliqué par l’ajustement des effets de l’EMG, tels que le mouvement d’un membre ou du torse du patient ou même la respiration. Une approche traditionnelle adoptée par beaucoup consiste à utiliser un filtre coupe-bande IIR de 2e ordre :
Another important aspect of biomedical signal processing is to extract meaningful and relevant features from the signals that can represent their properties and patterns. Feature extraction techniques aim to transform the signals into a lower-dimensional and more informative space, where they can be easily compared, classified, or modeled. Some of the most common feature extraction techniques are Fourier transform, which converts the signal from time domain to frequency domain and reveals its spectral components; principal component analysis (PCA), which reduces the dimensionality of the signal by finding the orthogonal directions of maximum variance; and wavelet transform, which decomposes the signal into different frequency bands and captures its local and global characteristics.
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A escolha criteriosa das características extraídas pode influenciar diretamente na capacidade de discriminação e na precisão dos modelos subsequentes. Métodos de seleção de características são aplicados para identificar as mais relevantes, descartando aquelas que podem introduzir ruído ou redundância ao processo. Essa etapa contribui para aprimorar a eficiência computacional e facilitar a interpretação dos resultados obtidos.
Signal segmentation is the process of dividing the signals into segments or intervals that correspond to different events, phases, or states. Signal segmentation techniques aim to identify and locate the boundaries of the segments, and to assign labels or categories to them. Signal segmentation can facilitate the analysis and interpretation of the signals, and enable the extraction of segment-specific features or parameters. Some of the most common signal segmentation techniques are thresholding, which applies a predefined or adaptive criterion to separate the segments; peak detection, which finds the local maxima or minima of the signal and defines the segments around them; and hidden Markov models (HMM), which model the signal as a sequence of discrete states and estimate their transition probabilities.
Signal classification is the process of assigning the signals or their segments to predefined classes or groups based on their features or parameters. Signal classification techniques aim to distinguish between different types or categories of signals, and to provide a decision or an output based on the classification results. Signal classification can enable the diagnosis, monitoring, or treatment of various medical conditions or disorders, and provide feedback or guidance to the users or patients. Some of the most common signal classification techniques are linear discriminant analysis (LDA), which finds a linear boundary that separates the classes; support vector machines (SVM), which find a nonlinear boundary that maximizes the margin between the classes; and artificial neural networks (ANN), which learn a complex function that maps the input features to the output classes.
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One important consideration when developing classifiers for biomedical signals is to be rigorous in addressing patient variability. It is crucial never to treat multiple recordings from the same patient as if they were from different subjects. Additionally, avoiding classifier overfitting is essential. This should be taken into account during data partitioning for training, validation, and testing, employing techniques such as 'leave one out' to enhance model generalization.
Signal reconstruction is the process of restoring or recovering the signals from incomplete, missing, or corrupted data. Signal reconstruction techniques aim to fill the gaps or fix the errors in the signals, and to obtain a complete and consistent representation of the signals. Signal reconstruction can improve the quality and reliability of the signals, and enable the use of other signal processing techniques that require complete data. Some of the most common signal reconstruction techniques are interpolation, which estimates the missing values from the neighboring values; compressed sensing, which exploits the sparsity of the signal and recovers it from a small number of measurements; and inpainting, which restores the damaged or occluded regions of the signal by using the information from the surrounding regions.
Signal visualization is the process of displaying or presenting the signals or their features in a graphical or pictorial form. Signal visualization techniques aim to enhance the perception and understanding of the signals, and to reveal their patterns, trends, or anomalies. Signal visualization can facilitate the communication and dissemination of the signal processing results, and provide insights or feedback to the users or patients. Some of the most common signal visualization techniques are time series plots, which show the variation of the signal over time; frequency spectrum plots, which show the distribution of the signal over frequency; and heat maps, which show the intensity of the signal over a two-dimensional or three-dimensional space.
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Ao apresentar de forma gráfica ou pictórica os sinais ou suas características, as técnicas de visualização têm o objetivo de enriquecer a percepção e compreensão dos padrões, tendências ou anomalias presentes nos dados. Os gráficos de séries temporais destacam a variação do sinal ao longo do tempo, oferecendo uma representação dinâmica e intuitiva. Os gráficos do espectro de frequência revelam a distribuição do sinal em termos de frequência, proporcionando insights sobre as características espectrais predominantes. Já os mapas de calor apresentam a intensidade do sinal em um espaço bidimensional ou tridimensional, permitindo uma visualização mais abrangente das variações espaciais.
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L’avenir du traitement du signal biomédical recèle un immense potentiel. Avec les progrès de l’apprentissage automatique, de l’intelligence artificielle et des technologies portables, la précision et l’efficacité de l’analyse des signaux devraient encore s’améliorer. Cela mènera au développement d’outils de diagnostic plus sophistiqués et d’approches thérapeutiques personnalisées.
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