9.4 Oversampling and undersampling in biomedical applications

Sampling techniques in biomedical applications are crucial for capturing and processing vital health data. Oversampling and undersampling offer unique advantages, from improving signal quality to reducing acquisition time and radiation exposure.

These methods have far-reaching impacts in medical imaging and signal processing. Oversampling enhances ECG and EEG analysis, while undersampling revolutionizes MRI and CT scans. Understanding their trade-offs is key to optimizing biomedical data collection and analysis.

Sampling Techniques in Biomedical Applications

Oversampling in biomedical signals

• Oversampling captures signals at rates higher than the Nyquist rate, which is twice the highest frequency component of the signal
  • Spreads quantization noise over a wider frequency range, improving signal-to-noise ratio (SNR)
  • Allows for the use of simpler, less steep anti-aliasing filters, reducing hardware complexity
  • Enables detection of subtle signal features that may be missed at lower sampling rates (P-waves and T-waves in ECG)
  • Facilitates implementation of digital signal processing techniques like decimation and filtering

Undersampling for biomedical imaging

• Undersampling acquires signals at rates lower than the Nyquist rate by exploiting the sparsity or compressibility of the signal in a transform domain (Fourier, wavelet)
• Compressed sensing MRI (CS-MRI) accelerates MRI acquisition by undersampling k-space data and reconstructing images using optimization algorithms
• Sparse CT reduces radiation dose by undersampling projection data and reconstructing images using iterative algorithms
• Photoacoustic tomography improves temporal resolution by undersampling acoustic data and reconstructing images using model-based algorithms

Oversampling vs undersampling trade-offs

• Oversampling improves signal quality by increasing SNR and reducing aliasing artifacts but requires higher sampling rates, leading to increased data storage and transmission requirements and computational complexity of digital signal processing algorithms
• Undersampling reduces data acquisition time and storage requirements but may degrade signal quality if the signal is not sufficiently sparse or compressible and requires complex reconstruction algorithms, increasing computational complexity
• Trade-offs depend on the specific application, available resources, and desired signal quality

Applications of sampling techniques

• Oversampling applications:
  1. ECG signal processing: oversample to improve SNR and detect subtle features (P-waves, T-waves)
  2. EEG signal processing: oversample to capture high-frequency components and improve spatial resolution
  • Evaluate performance by comparing SNR, feature detection accuracy, and computational efficiency with lower sampling rates
• Undersampling applications:
  1. CS-MRI: undersample k-space data to reduce acquisition time
  2. Sparse CT: undersample projection data to reduce radiation dose
  • Evaluate performance by comparing data reduction factors, image quality metrics (PSNR, SSIM), and reconstruction time with fully sampled data