Calculating P-R Interval and ECG Parameters with Automated Feature Detection

Process given ECG data files to plot electrocardiogram waveforms, automatically mark characteristic points' positions, and calculate parameters such as P-R intervals. The attached .dat file is a binary data file where every 2 bytes represent one sampling point at 1000Hz sampling frequency. Data is structured sequentially: first sampling points for leads 1-12, followed by second sampling points for leads 1-12, etc. The .hea header file is a text format specification file that reveals the storage sequence of the twelve leads.

During ECG analysis, beyond plotting waveforms and marking characteristic points, further analysis can compute parameters like P-R intervals. These parameters provide detailed cardiac function information, aiding in diagnosis and monitoring of patients' heart conditions. Code implementation typically involves signal processing algorithms for peak detection (using methods like wavelet transforms or differential thresholds) and interval calculations between identified P-wave and R-wave peaks.

The attached .dat data file uses binary format storage with 2 bytes per sample at 1000Hz sampling rate. The file sequentially stores sampling point data following lead 1-12 order, facilitating easy lead data extraction and analysis. Programmatically, this requires reading binary data with proper byte ordering (little-endian/big-endian handling) and reshaping the data matrix based on lead count and sampling points.

Additionally, the .hea header file is a text-based specification file providing detailed information about lead storage sequence. By parsing the header file, we can identify each lead's position within the data file, enabling better understanding and processing of ECG data. Implementation typically involves reading ASCII headers line-by-line to extract metadata like lead names, sampling frequency, and ADC gain values.

Through these analysis and processing steps, we obtain comprehensive ECG analysis results, providing physicians with accurate cardiac health assessments and diagnostic recommendations. The complete workflow involves: 1) Header parsing to determine data structure, 2) Binary data reading and reshaping, 3) Digital filtering (e.g., bandpass 0.5-40Hz), 4) QRS complex detection using Pan-Tompkins algorithm, 5) P-wave/T-wave identification using template matching, and 6) Parameter calculation with medical reference ranges.