app

ECG Print

ECG Print reads a wearable recording, filters one channel, detects beats, builds event-centered segments and a representative template, and reports signal quality over time. It can export the segment measurements and a printable waveform image.

Open ECG Print

From the LabKit launcher, select ECG Print and choose Open. From a source checkout, run:

labkit_ECGPrint_app

Supported Inputs

Choose Open recording to load a MAT, CSV, TXT, or TSV biosignal file. MAT files can contain a supported timetable or recording structure. Delimited text files can be detected automatically or parsed with the controls under Import Parsing.

For a difficult text file, choose Preview file header, then set:

• the line containing column names;
• whether a header is present;
• the time column and its unit;
• the signal columns;
• a fallback sample rate when time values are unavailable.

Choose Parse / refresh file after changing import settings. The app reports the detected channels and lets you select one for analysis.

The recording is saved as a portable project source. Older projects are upgraded on load.

Project And Session State

ECG Print stores only durable workflow state in its project file:

• the portable recording source;
• import, channel, ROI, filter, detector, segment, template, and view settings;
• the compact last-analysis summary and export records.

Decoded recordings, signal arrays, events, segments, templates, measurements, header previews, and plot models are transient session data. They are rebuilt from the recording and durable parameters when a project is opened. This keeps saved projects portable and avoids duplicating large waveform caches.

Analyze ECG

1. Open and parse a recording.
2. Select a channel and, if needed, set the start and end of the time region. When the end is not greater than the start, the full signal is used.
3. Set the bandpass range and peak method.
4. Set minimum peak distance, segment half-window, template count, and smoothing count.
5. Choose Analyze current ROI.
6. Review the four plots: waveform and peaks, template-noise RMS, template SNR, and the template with either a residual band or individual segments.

The filter is applied to the full selected channel before the time region is cropped. This reduces boundary artifacts at the region edges.

The Files + Analysis tab keeps the workflow in five ordered sections: Recording, Import Parsing, Channel + ROI, Signal Processing + SNR, and Exports. Bounded numeric settings use paired spinner-and-slider controls. Summary + Results contains the analysis summary and file-header preview, while Log records the current session workflow. The ECG Preview workspace keeps four vertically stacked time-series axes available on every tab.

Analysis Parameters

Peak polarity is selected automatically. The default detector threshold is 2.8 standard deviations inside the app calculation.

Output Files

Export segment SNR CSV writes ecg_segment_snr.csv and a matching ecg_segment_snr.labkit.json manifest. The CSV contains per-segment measurements and smoothed trends.

Export waveform PNG writes ecg_waveform.png and ecg_waveform.labkit.json. The manifest records the source, import and analysis settings, output identity, event count, segment count, and summary.

Analyze A Signal In MATLAB Code

[recording, status] = labkit.biosignal.readRecording("ecg.csv");
assert(status.ok, status.message)

signal = labkit.biosignal.getChannel(recording, 1);
cache = struct("signal", signal);
parameters = struct( ...
    "lowCut", 0.5, ...
    "highCut", 40, ...
    "roiStart", 0, ...
    "roiEnd", 0, ...
    "peakMethod", "QRS streaming", ...
    "peakDistance", 0.28, ...
    "segmentWindow", 0.7, ...
    "templateTopN", 30);

cache = ecg_print.analysisRun.analyzeSignal(cache, parameters);

The returned cache contains the working and filtered signals, detected events, segments, template, and measurements. For a more customized pipeline, call labkit.biosignal.filterSignal, detectEcgPeaks, segmentByEvents, buildTemplate, and measureSegments directly.

Errors And Limitations

• Time or a valid sample rate is required to interpret filter frequencies and event spacing.
• Automatic parsing should be checked when a text file has long preambles, unusual headers, or mixed metadata rows.
• A failed Parse / refresh file keeps the selected source and header preview available so import settings can be corrected in place.
• Automatic peak polarity and thresholds still require visual review when morphology or noise changes within a recording.
• Filtering and detection settings must accompany any reported heart rate or signal-quality result.

Related Functions And Documentation

• ecg_print.analysisRun.analyzeSignal
• labkit.biosignal.readRecording
• labkit.biosignal.detectEcgPeaks
• labkit.biosignal.measureSegments
• Biosignal library