How to use the wfdb.processing.compare_annotations function in wfdb

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github JohnDoenut / biopeaks / benchmark_ecg.py View on Github external
data = wfdb.rdrecord(subject[0][:-4], sampto=sampto)
    annotation = wfdb.rdann(subject[1][:-4], 'atr',
                            sampfrom=sampfrom,
                            sampto=sampto)

    sfreq = data.fs
    ecg = data.p_signal[:, 0]

    manupeaks = annotation.sample
    #algopeaks = peaks_signal(ecg, sfreq)
    algopeaks = rpeaks(ecg, sfreq)

    # tolerance for match between algorithmic and manual annotation (in sec)
    tolerance = 0.05
    comparitor = compare_annotations(manupeaks, algopeaks,
                                     int(np.rint(tolerance * sfreq)))
    tp = comparitor.tp
    fp = comparitor.fp
    fn = comparitor.fn

#    plt.figure()
#    plt.plot(ecg)
#    plt.plot(np.square(ecg))
#    plt.plot(np.abs(ecg))
#    plt.scatter(manupeaks, ecg[manupeaks], c='m')
#    plt.scatter(algopeaks, ecg[algopeaks], c='g', marker='X', s=150)

    sensitivity.append(float(tp) / (tp + fn))
    precision.append(float(tp) / (tp + fp))
    print(sensitivity[-1], precision[-1])
github JohnDoenut / biopeaks / biopeaks / benchmarks / benchmark_utils.py View on Github external
record : 1d array
            The raw physiological record.
        annotation : 1d array
            The manual extrema annotations.

        Returns
        -------
        precision : float
            The detectors precision on the record given the tolerance.
        sensitivity : float
            The detectors sensitivity on the record given the tolerance.

        """
        detector_annotation = self.detector(record, self.sfreq)

        comparitor = compare_annotations(detector_annotation, annotation,
                                         self.tolerance)
        tp = comparitor.tp
        fp = comparitor.fp
        fn = comparitor.fn
        sensitivity = tp / (tp + fn)
        precision = tp / (tp + fp)

        return precision, sensitivity
github neuropsychology / NeuroKit / neurokit2 / benchmark / benchmark_utils.py View on Github external
Maximum difference in millisecond that is permitted between the manual
        annotation and the annotation generated by the detector.
    detector : function
        A function that takes a physiological record as first positional
        argument as well as a `sampling_rate` keyword argument.

    Returns
    -------
    precision : float
        The detectors precision on the record given the tolerance.
    sensitivity : float
        The detectors sensitivity on the record given the tolerance.
    """
    detector_annotation = detector(record, sampling_rate=sampling_rate)

    comparitor = compare_annotations(detector_annotation, annotation, tolerance)
    tp = comparitor.tp
    fp = comparitor.fp
    fn = comparitor.fn

    sensitivity = tp / (tp + fn)
    precision = tp / (tp + fp)

    return precision, sensitivity

wfdb

The WFDB Python package: tools for reading, writing, and processing physiologic signals and annotations.

MIT
Latest version published 1 year ago

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