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HMM Heart


Problem identified

  • Increased adoption of wearable heart-rate sensors opens opportunity to understand mobile health behavior
  • Classification of arrhythmias and other heart complications saves lives and costs

Solution created

  • Pipeline to input, clean, de-trend, process, and fit ballistocardiogram (BCG) and electrocardiogram (ECG) waveforms to Hidden Markov Models.

Technologies used

  • MATLAB + DSP Toolbox
  • Python hmmlearn

De-trended signals and labelled R and S peaks

Overlaying S-to-S ECG complexes

HMM applied to processed ECG signals


© Pramod Kotipalli 2016

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