def _checkup(peaks, ecg_integrated, sample_rate, rr_buffer, spk1, npk1, threshold):
    """
    Check each peak according to thresholds

    ----------
    Parameters
    ----------
    peaks : list
        List of local maximums that pass the first stage of conditions needed to be considered as
        an R peak.
    ecg_integrated : ndarray
        Array that contains the samples of the integrated signal.
    sample_rate : int
        Sampling rate at which the acquisition took place.
    rr_buffer : list
        Data structure that stores the duration of the last eight RR intervals.
    spk1 : float
        Actual value of SPK1 parameter defined in Pan-Tompkins real-time R peak detection algorithm
        (named signal peak).
    npk1 : int
        Actual value of NPK1 parameter defined in Pan-Tompkins real-time R peak detection algorithm
        (named noise peak).
    threshold : float
        Initial value of the adaptive threshold level (relevant parameter for the application of
        specific criteria during the identification of R peaks).

    Returns
    -------
    out : list
        List with the position of the peaks considered as R peak by the algorithm.

    """
    peaks_amp = [ecg_integrated[peak] for peak in peaks]
    definitive_peaks = []
    for i, peak in enumerate(peaks):
        amp = peaks_amp[i]

        # accept if larger than threshold and slope in raw signal
        # is +-30% of previous slopes
        if amp > threshold:
            definitive_peaks, spk1, rr_buffer = _acceptpeak(peak, amp, definitive_peaks, spk1,
                                                            rr_buffer)

        # accept as qrs if higher than half threshold,
        # but is 360 ms after last qrs and next peak
        # is more than 1.5 rr intervals away
        # just abandon it if there is no peak before
        # or after
        elif amp > threshold / 2 and list(definitive_peaks) and len(peaks) > i + 1:
            mean_rr = numpy.mean(rr_buffer)
            last_qrs_ms = (peak - definitive_peaks[-1]) * (1000 / sample_rate)
            last_qrs_to_next_peak = peaks[i+1] - definitive_peaks[-1]

            if last_qrs_ms > 360 and last_qrs_to_next_peak > 1.5 * mean_rr:
                definitive_peaks, spk1, rr_buffer = _acceptpeak(peak, amp, definitive_peaks, spk1,
                                                                rr_buffer)
            else:
                npk1 = _noisepeak(amp, npk1)
        # if not either of these it is noise
        else:
            npk1 = _noisepeak(amp, npk1)
        threshold = _buffer_update(npk1, spk1)

    definitive_peaks = numpy.array(definitive_peaks)

    return definitive_peaks
Check each peak according to thresholds

    ----------
    Parameters
    ----------
    peaks : list
        List of local maximums that pass the first stage of conditions needed to be considered as
        an R peak.
    ecg_integrated : ndarray
        Array that contains the samples of the integrated signal.
    sample_rate : int
        Sampling rate at which the acquisition took place.
    rr_buffer : list
        Data structure that stores the duration of the last eight RR intervals.
    spk1 : float
        Actual value of SPK1 parameter defined in Pan-Tompkins real-time R peak detection algorithm
        (named signal peak).
    npk1 : int
        Actual value of NPK1 parameter defined in Pan-Tompkins real-time R peak detection algorithm
        (named noise peak).
    threshold : float
        Initial value of the adaptive threshold level (relevant parameter for the application of
        specific criteria during the identification of R peaks).

    Returns
    -------
    out : list
        List with the position of the peaks considered as R peak by the algorithm.