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Sequential Probability Assignment for Outlier Detection in Heartbeat Timings.

Sabrina Liu, Todd P Coleman

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    View abstract on PubMed

    Summary
    This summary is machine-generated.

    This study introduces a new method to detect outlier heartbeats, improving heart rate accuracy from wearables. The novel approach effectively identifies incorrect heartbeat detections caused by noise or physiological irregularities.

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    Area of Science:

    • Biomedical Engineering
    • Signal Processing
    • Cardiology

    Background:

    • Inaccurate heart rate and heart rate variability estimates arise from artifacts, noise, and physiological outlier beats.
    • The proliferation of wearable devices necessitates robust methods for identifying erroneous heartbeat detections due to motion artifacts and poor sensor contact.

    Purpose of the Study:

    • To propose a sequential probability assignment procedure for detecting outlier heartbeats.
    • To develop a flexible time-varying point process model capable of capturing changes in both mean and variance of interbeat intervals.

    Main Methods:

    • A time-varying point process model estimating a two-parameter exponential family distribution per time index.
    • Formulation of a maximum likelihood problem with a Kullback-Leibler regularizer at each time step.
  • Testing of inverse Gaussian, gamma, and log-normal distributions, with inverse Gaussian showing the best fit for interbeat intervals via Kolmogorov-Smirnov statistic.

    • Main Results:

    • The inverse Gaussian distribution demonstrated the best fit for interbeat interval data from clinical electrocardiogram (ECG) data.
    • The proposed model successfully detected outlier heartbeats in both simulations and clinical data.
    • The sequential probability assignment procedure proved effective in identifying statistically unlikely heartbeat timings.

    Conclusions:

    • The developed outlier detection method enhances the reliability of heart rate and heart rate variability measurements, particularly in noisy environments.
    • This technique is crucial for improving the accuracy of wearable health monitoring devices.
    • The model's ability to identify ectopic beats and arrhythmic events contributes to more precise clinical relevance.