Concept:
Biomedical algorithms for processing ECG data are categorized based on their primary operational goals, such as data compression (minimizing storage and transmission bandwidth) or morphological feature extraction (detecting waves for diagnostic purposes).
Step 1: Reviewing the compression algorithms listed.
• AZTEC (Amplitude Zone Time Epoch Coding): A classic data reduction algorithm that compresses ECG signals by transforming raw sample series into a simplified sequence of flat plateaus and linear slopes.
• CORTES (Coordinate Reduction Time Axis Symmetry): A hybrid algorithm that improves upon AZTEC. It applies AZTEC data reduction to slow-changing sections (like the baseline) but preserves raw, uncompressed sample coordinates for fast-changing regions (like the QRS complex) to retain clinical accuracy.
• TPA (Turning Point Algorithm): A data reduction method that reduces the sampling rate by a factor of two. It processes data points in pairs and saves only the critical "turning point" (the peak or trough value) within each pair to preserve the signal's shape.
Step 2: Identifying the non-compression algorithm.
The Pan-Tompkins algorithm is not a data compression method. Instead, it is a widely used algorithm developed to detect the QRS complex (R-peak detection) in real-time. It uses a sequence of digital filtering steps—including low-pass filtering, high-pass filtering, differentiation, squaring, and moving-window integration—to isolate ventricular contractions. It does not compress or reduce the storage footprint of the signal. Therefore, Pan-Tompkins is the correct choice, matching Option (C).