Cross-Domain SignalsMedical-device makers / primary care / AI-assisted diagnosis
Heart-Sound (PCG) Anomaly Screening
01
Pain Points
- !Stethoscope recordings need S1/S2 segmentation and murmur spectral features, but lack a standardized workflow.
- !Normal/abnormal binary classification depends on experience and is hard to scale and reproduce.
- !Low-sample-rate, single-channel, low-frequency signals need dedicated preprocessing and feature engineering.
02
The Tinia Approach
Low-frequency band-pass + S1/S2 localization + murmur-segment spectral features + AutoML discrimination — the same operators carry directly to physiological signals.
20–400 Hz band-passS1/S2 localizationMurmur spectrumMel featuresAutoML discrimination
03
Nodes Used
IIR Filteriir_filter
20–400 Hz band-pass preprocessingActive Segment Detectionactive_segment
S1 / S2 beat localizationFFT Spectrum Analysisfft_spectrum
Murmur-segment spectrumSpectral Feature Extractionfbank_extract
Mel spectral featuresFeature Mergefeature_merge
Feature mergingCore nodes; add or remove steps as needed for your data and standards — the node graph is always editable.
04
Expected Output
- S1 / S2 beat periods and murmur-segment spectral features.
- Normal / abnormal binary classification (AutoML discriminant function).
- A scalable, reproducible screening workflow.
Reference Standards / Engineering PracticePhysioNet open datasets · clinical guidelines
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