Patent Description:
<CIT> discloses a method and devices for measuring respiratory parameters from an ECG device.

In subjects with chronic obstructive pulmonary disease (COPD) and other respiratory diseases, the assessment of the parasternal muscle activity (measured from surface electromyography (EMG), e.g. with electrodes positioned at the 2nd intercostal space) can be useful to estimate the intensity, timing and duration of subject respiratory effort, as an indicator of the balance between respiratory muscle load and respiratory muscle capacity. It is known from prior studies that the maximum EMG level that occurs during relaxed inhalation is related to the neural respiratory drive (NRD). In COPD subjects, during increasing lung hyperinflation as observed during acute exacerbation, there is a change in the balance between respiratory muscle load and capacity, which is reflected in the neural respiratory drive (lower capacity and higher load resulting in increased NRD). A way of assessing the NRD in a subject is to measure the respiratory effort of the subject.

In order to determine a normalized version of the respiratory effort, a sharp maximum inspiration through the nose is used. However, such maneuvers by themselves are known to have a large variance and may be potentially biased due to a lack of motivation or pain inhibition in affected subjects. Especially in clinical applications with pain-related inhibition (e.g. acute subjects) and elderly subjects, such full activation is difficult to achieve. Furthermore subjects might recruit other muscles during the maximum inspiratory maneuver, e.g. postural muscles, which act as a disturbance for the measurement and potentially elevate the maximum RMS values obtained during the maximum maneuver.

Therefore, there is a need to improve the way in which a respiratory effort is obtained.

According to examples in accordance with an aspect of the invention, there is provided a system for determining a respiratory effort for a subject, the system comprising:
a processor configured to:.

The relaxed signal is obtained by asking the subject to breathe in a relaxed manner and measuring the signal from the electrodes. The forced signal is obtained by asking the subject to breathe in a forced, sharp manner (a sniff). Forced peaks can thus be derived from the forced signal (e.g. from an EMG waveform) at the corresponding time events. The forced peaks derived from the forced signal coincide with a sniff event. Some of the peaks may not be of appropriate quality due to various reasons (e.g. subject not breathing in as much as possible, subject stopping mid-sniff, subject being tired etc.). Therefore, candidate peaks are selected which correspond to "good" peaks which can be used to find the respiratory effort. The final selection of a user identified peak is made by a user. This provides a compromise between automated selection and user selection in that the user selects a peak from a limited set of peaks, in order to make the user involvement easier. Thereafter, the respiratory effort of the subject can be found based on the relaxed signal and the user identified peak.

The features of the forced peaks may be based on:.

The maximum value of a forced peak corresponds to the peak amplitude of the forced peak. The sharpness indication is used to indicate the quality of a sniff. It corresponds to the duration of the peak and/or the effort exerted by the subject to perform the sniff. The sharpness, for example, may be calculated by calculating the derivative with respect to time of the forced peak, wherein the value of the derivative at the time of a sniff may be used to determine the sharpness indication.

The spectral flatness indication is also used to indicate the quality of a sniff. It corresponds to a comparison (e.g. ratio) of the contribution of high frequency components in the forced signal to the contribution of low frequency components at the time of a sniff. It may be calculated from using spectral analysis (e.g. applying Fourier transform) to the forced signal at the time of a sniff and, for example, determining the ratio of low frequency (e.g. < <NUM>) components in the spectral domain to the high frequency (e.g. > <NUM>) components in the spectral domain.

The system may further comprise a respiration sensor for monitoring movement or breathing flow during respiration, and wherein the processor is further configured to:.

A second type of signal can also be used, a respiration signal. The respiration signal represents the physiological effects of breathing and can thus indicate the properties of an inspiratory manoeuver. The properties may include: whether the manoeuver corresponds to relaxed or forced breathing, the length of the manoeuver, the effort exerted by the subject during the manoeuver etc. For example, it can indicate the flow of air into the nose or the tilt of an accelerometer during breathing. The respiration signals can be used to determine whether a forced peak corresponds to a "good" sniff by, for example, comparing the corresponding respiration signal during the sniff to the respiration signal during relaxed breathing, or to respiration signals during previous sniffs.

The respiration sensor may be one or more of:.

The system further comprises an output interface for providing feedback in real time for each of the forced peaks, wherein the feedback indicates one or more of:.

The invention also provides a method for determining a respiratory effort for a subject, the method comprising:.

A feature of the forced peaks may comprise the values of each of the forced peaks and wherein selecting candidate peaks comprises comparing value of each of the forced peaks to one or more of:.

The method may further comprise obtaining a plurality of relaxed peaks from the relaxed signal, wherein selecting candidate peaks further comprises comparing each of the forced peaks to at least one of the relaxed peaks.

The forced signal may be obtained in real time and the candidate peaks may be selected in real time.

The method further comprises providing feedback in real time for each of the forced peaks, wherein the feedback indicates one or more of:.

The invention also provides a computer program comprising code for implementing the method defined above when said program is run on a processing system.

The invention provides a system and method for determining a respiratory effort for a subject. The method comprises obtaining a relaxed signal representing the subject breathing in a relaxed manner and a forced signal representing the subject breathing in a forced manner. A plurality of forced peaks is derived from the forced signal and candidate peaks are selected from the plurality of forced peaks. The candidate peaks are selected based on features of the forced peaks. A user selects a user identified peak from the candidate peaks and thus, a respiratory effort is determined based on the relaxed signal and the user identified peak.

<FIG> shows an example of the locations on the body of a subject <NUM> at which respiratory muscle activity can be measured. Two electromyogram (EMG) electrodes <NUM> located at the second intercostal space symmetrically near the sternum (parasternal) may be used for the measurement. The two electrodes <NUM> can be mounted inside (or attached on) a single EMG patch, which eases the placement of the two electrodes <NUM> to assess the same respiratory muscle groups for every sequential measurement (e.g. every day). It is known that the electrodes <NUM> mainly measure the inspiration breathing effort due to the activation of the parasternal internal intercostal muscles during inhalation by the subject <NUM>.

Via the two EMG electrodes <NUM>, the signal measured at the 2nd intercostal space parasternal muscle during inhalation can be used as an indicator of the day-to-day deterioration or improvement of the COPD subject <NUM> when multiple measurements are performed over a number of days, and as a predictor of hospital readmission after discharge as well.

However, signals from the respiratory muscle activity at the 2nd intercostal space also include electrocardiogram (ECG) signals from the heartbeat.

<FIG> shows three graphs representing signals from a subject <NUM> during breathing.

The top graph a) shows the raw EMG and ECG signal (that includes the contribution from the electric activity of the heart - ECG contribution - that needs to be removed) during relaxed breathing of a COPD subject <NUM>. The ECG contribution can be recognized by periodic strong peaks (may also be known as QRS complex).

The middle graph b) shows RMS values (in µV on the y-axis) from which it can be seen that the maximum RMS level in a particular single regular breath <NUM> is around <NUM>µV. This maximum RMS level corresponds to the maximum recruitment of the inspiratory parasternal muscle. The ECG RMS signal peaks <NUM> can also be seen in graph b).

The bottom graph c) shows the signal from a flow sensor that measures the pressure in the nose of a subject. A valley (negative pressure) in this signal indicates an inspiration.

When the maxima in the RMS signal is looked at during the relaxed breathing phase, there is the disadvantage that the levels of this RMS signal can be influenced by the level of subcutaneous skin tissue of the subject. It has also been seen from experiments that when subjects <NUM> are more obese, the RMS values are typically lower. Furthermore, it is known that the EMG amplitudes decrease with the distance between the electrode <NUM> and the muscle.

As a solution to this problem, the subject <NUM> can also perform a series of maximum effort maneuvers (in e.g. <NUM> minute) and measurements may be taken of the maximum RMS peak level during this maneuver. This allows the average of the RMS peak levels during the relaxed breathing to be divided (normalized) with the maximum of the RMS peak levels during maximum effort maneuvers. An important benefit of this normalization is to obtain a measurement during relaxed breathing that is presented as a percentage of respiratory muscle recruitment with respect to the maximum possible muscle recruitment. By having this result (e.g. a percentage) a threshold can more easily be defined in order to assess the subject <NUM> in terms of improvement or deterioration.

The highlighted area <NUM> in the middle graph b) shows the time when a subject <NUM> performed an inspiration. The inspiration EMG signal <NUM> are distinguished from the ECG signals <NUM> by a longer duration and a lower amplitude in the RMS signal. ECG signals <NUM> are periodic and have a sharp peak when compared to the inspiration EMG signals <NUM>.

<FIG> shows six graphs representing the EMG signals of a subject breathing.

The first graph a) shows the EMG and ECG signal. The ECG contribution is significant, since the signal is measured at the parasternal location. The x axis shows time in seconds.

The second graph b) shows a trace with the ECG contribution removed ("ECG-removed EMG"), where a <NUM> high pass filter is used for the removal of the ECG contamination.

The third graph c) shows the RMS of the ECG-removed EMG, where two averaging windows are used for the computation of the RMS: a <NUM> averaging window and a <NUM> second averaging window.

The fourth graph d) shows the RMS of the ECG-removed EMG with an averaging window of <NUM> second.

The fifth graph e) shows the RMS of the ECG-removed EMG with an averaging window of <NUM>.

The sixth graph f) shows the RMS of the ECG-removed EMG with an averaging window of <NUM> second for the first <NUM> seconds and an averaging window of <NUM> for the last <NUM> seconds.

The <NUM> second averaging time is preferred in the relaxed breathing, because the <NUM> second RMS removes some ECG (residual) contamination and the <NUM> second RMS has a better defined peak level (better averaging of noise) for the relaxed signal. For example, the relaxed peak 302a has a better defined peak and lower noise level compared to 302b.

However, for the region of the sniffs (forced signal) in the last <NUM> seconds it can be seen that the RMS computation with <NUM> second averaging lowers the RMS levels during the sniffs maneuvers. This relates to the fact that the sniff is a sharp inspiratory maneuver that only has strongly activated parasternal respiratory muscles for a very short amount of time. Furthermore, it can be seen that for the <NUM> second RMS computation, sniffs with a longer duration (e.g. the first sniff maneuver at around <NUM> seconds) produce much higher RMS values compared to the sniffs with a short duration (e.g. the last sniff maneuver at around <NUM> seconds). This is not desired, since it would be preferable to have a reproducible maximum sniff level measurement that is independent of its duration. For example, the forced peak 304a has a much lower RMS peak level when compared to forced peak 304b.

Hence, there is not a unique choice possible for the RMS averaging windows that gives best output results for both the relaxed breathing and the sniff maneuvers.

It would be desirable to have a long averaging window for the RMS computation for relaxed breaths and short averaging window for the RMS computation for sniff maneuvers. Thus, it may be favorable to distinguish between RMS computations for the relaxed breaths and the sniff maneuvers, i.e. RMS with a first (long) averaging window (e.g. <NUM> second) for the relaxed breathing and RMS with second (short) averaging window (e.g. <NUM>) for the sniff maneuvers as is shown in graph f) of <FIG>.

<FIG> shows a first example of a system for determining a respiratory effort. A signal <NUM> is first obtained representing a subject breathing. The signal may be obtained from electrodes <NUM> on the subject <NUM> or from pre-recorded historic data for the subject <NUM>. The signal <NUM> may cover time period during which the subject is breathing in a relaxed manner and/or the subject is breathing in a forced manner. The signal <NUM> is then processed by a processor <NUM>.

Optionally, the signal <NUM> may first be filtered with an ECG removal block <NUM> if the signal <NUM> has not already been filtered. This will yield a filtered relaxed signal and a filtered forced signal. Since the EMG signal is measured on the parasternal area, there will be ECG contamination (ECG and EMG) in the signal. An ECG removal block <NUM> may thus be used on the EMG and ECG signal, where two types of ECG removal techniques may be applied:.

The spectral ECG removal removes all spectral contributions of the ECG signal by having a high cut-off frequency. The temporal ECG removal retains the higher frequency spectral components in the frequency domain, but removes the high frequency QRS complexes based on the characteristic shape in the time domain, for example using a time-gated filtering technique, based on an ECG model.

Using either of these methods depends on the application, e.g. how much contribution to preserve in the frequency range of <NUM> to <NUM>. For an easy implementation and robustness against arrhythmias, the spectral ECG removal may be used, since the detection of R-peaks and the construction of an ECG model are avoided.

The output of the ECG removal block <NUM> represents the signal that should be free of ECG contamination to such a level that is will not hamper the EMG measurement. In the inspiratory EMG block, the inspiratory phases <NUM> are selected from this ECG-removed signal.

The inspiratory phase <NUM> may comprise at least relaxed breathing and forced breathing, based on the subject breathing in a relaxed manner or performing sniffs.

Based on the inspiratory phase <NUM> being relaxed breathing, a smoothing function with a first (long) averaging window <NUM> is applied to the EMG signal in order to obtain a smoothed relaxed signal <NUM>. Based on the inspiratory phase <NUM> being forced breathing (sniffs), a smoothing function with a second (short) averaging window <NUM> is applied to the EMG signal in order to obtain a smoothed forced signal <NUM>.

The inspiratory phase <NUM> may be determined based on, for example, a nurse telling the subject <NUM> when to perform relaxed breathing and when to perform sniffs or by an automatic sniff detector.

The inspiratory phases <NUM> can be used as a guide to select the maximum peak <NUM> in the RMS of the ECG-removed signal. During relaxed breathing, a peak in every respiration cycle is selected and the average <NUM> over e.g. <NUM> minute may be computed. After the relaxed breathing, the subject <NUM> is asked to perform sniffs. The peak in each sniff maneuver is detected and the maximum <NUM> over all sniffs available (performed in e.g. <NUM> minute) may then be computed. The clinical EMG parameter that is computed is the respiratory effort <NUM> based on, for example, the average peak value <NUM> of the relaxed breathing which is normalized with (divided by) the maximum peak value <NUM> obtained from the sniff maneuver. In this way, a metric is obtained that represents a percentage of respiratory muscle recruitment with respect to the maximum possible muscle recruitment for the assessment of the subject in terms of improvement or deterioration.

The ECG and EMG signal may be buffered to collect samples in a block or a window of e.g. <NUM> seconds or <NUM> minute. Possibly the buffering also includes overlap with previous iterations to allow an output of <NUM> minute of data that is advanced with e.g. <NUM> seconds every iteration.

The ECG and EMG signal may alternatively be received from, for example, a memory module and further be processed (with different averaging windows). This may be for analyzing historical data of a subject. The signal received may also be pre-filtered (ECG signal removed).

Optionally the inspiratory phase <NUM> is determined with the help of a respiration unit <NUM> determining a respiration signal <NUM>. For example, an accelerometer may be placed on the chest of the subject in order to measure the tilt of the chest. Alternatively, a flow sensor that measures the pressure in the nose of the subject may be used.

A respiration signal <NUM> may be obtained representing the subject breathing in a relaxed manner and the subject breathing in a forced manner, similar to how the EMG signal <NUM> is obtained. A plurality of peaks may be obtained from the forced respiration signal and from the relaxed respiration signal. The maximum value (or minimum value, depending on the direction of the peak) of the peaks may be representative of the quality of the sniff in the forced respiration signal. The maximum value (e.g. peak amplitude) of a forced respiration peak can be compared to the values of the other forced respiration peaks, to a threshold value or to the relaxed respiration peaks.

The respiration signal <NUM> may also be used to determine whether the EMG signal <NUM> is representative of the subject <NUM> breathing in a relaxed manner or a forced manner based on the duration, maximum value, minimum value and/or noise of the respiration signal <NUM>. For example, if a flow sensor is used to determine the respiration signal <NUM>, a relaxed breathing maneuver would cause a lower amount of flow than a sniff, such that the inspiratory phase <NUM> may be determined by the flow rate of the flow sensor.

An automatic sniff detection module may also be used. The automatic sniff detector may be able to detect, based on the respiration signal <NUM> and/or based on the EMG signal <NUM> whether the signal is a relaxed signal or a forced signal. This may be done with pre-calibration on a number of subjects, calibration on the subject <NUM> who generated the EMG signal <NUM> or with a pre-determined threshold voltage (or RMS voltage) in the EMG signal.

The smoothing function used, and corresponding averaging windows, may depend on the user (nurse) preference, the quality of the signals <NUM> obtained and/or on the processing equipment/software available. For example, a moving root mean square (RMS) may be used on both the relaxed signal and the forced signal, with the averaging window being the averaging window of the moving RMS, as has been shown in graphs c) d) e) and f) of <FIG>. Alternatively, a moving average may be computed for the signal or curve fitting may be used to approximate the signal.

In order to determine a most suitable averaging window, a set of smoothed signals with a variety of averaging windows may be computed and compared against each other. The suitability of the averaging window may depend on the judgment of the user (e.g. nurse) or on the average difference between data points of the real signal and the smoothed signal.

There may also be a user input interface for the user to input certain parameters. For example, the user may be able to input the type of smoothing function for the relaxed and forced signals, the durations of the first and second averaging windows and/or the ECG removal technique, as well as any further filtering required.

<FIG> shows an example of a method for determining a respiratory effort <NUM>. Since it is known know from several studies that a fully automated detection of sharp maximum inspiration maneuvers is very difficult to realize in practice, it is preferable to "guide" the nurse (performing the spot check respiratory effort measurement) in the procedure to obtain the best possible maximum inspiratory maneuver by the subject.

Forced peaks <NUM> can be first identified from the smoothed forced signal <NUM>. A series of candidate peaks <NUM> can then be chosen based on the forced peaks <NUM> and features of the forced peaks <NUM>. A user (e.g. nurse) can then select one of the candidate peaks through a user input <NUM> or a user identified peak <NUM>, based on the judgment of the user. The user identified peak <NUM> and the smoothed relaxed signal <NUM> can then be used to calculate the respiratory effort <NUM>.

For example, relaxed peaks can be identified from the smoothed relaxed signal <NUM> and the average value of the relaxed peaks can then be determined. The respiratory effort <NUM> can, for example, be obtained as the average relaxed peak value divided by the value of the user identified peak <NUM>.

Also, the "series" of sniff maneuvers can be manually aborted by the user (e.g. nurse) when a valid sniff maneuver has been performed. Thus, the time spent by a subject <NUM> performing forced respiratory maneuvers (sniffs) can be minimized.

<FIG> shows three graphs representing features <NUM> of the peaks.

The top graph a) shows the RMS signal of a high pass filtered (> <NUM>) EMG signal. The x axis corresponds to time in seconds.

The middle graph b) shows the low to high (L/H) frequency component ratio, where the low frequency component is computed from <NUM> up to <NUM> of the EMG signal, whereas the high frequency component is computed from > <NUM> of the EMG signal.

The bottom graph c) shows the duration of each inspiration.

Selection of candidate sniffs may be determined based on the measurement of some features <NUM> of the candidate sniff maneuvers (e.g. sharpness and flatness of the spectrum) and the logging of the history of the sniffs metrics. All this information can be provided to the nurse (in real time) to select the best possible sniff maneuver and abort the sniffs session when a good sniff maneuver has been obtained.

For example, the following features <NUM> may be used to determine the candidate sniffs:.

Larger values in the high to low frequency ratio, in graph b), represent either a higher proportion of high frequency components compared to low frequency components, which indicates a smaller recruitment of postural muscles during the sniff. During the first three sniffs (from <NUM> up to <NUM> seconds), it can be seen that the ratio is smaller compared to the last three sniffs (from <NUM> up to <NUM> seconds). Hence, the last three sniffs are considered to be performed better as they contain a lower proportion of the contaminating low frequencies from the postural muscles.

The duration of the EMG signal is shown in the lower graph, where it can be clearly seen that for the relaxed breaths (from <NUM> up to <NUM> seconds), the duration of the breath are much longer (less sharp) compared to the sniff maneuvers (from <NUM> up to <NUM> seconds). It can also be seen that the last <NUM> sniffs are more sharp (have shorter duration) compared to the first two sniffs.

Candidate peaks <NUM> can be selected based on a spectral flatness indication and a sharpness indication for each peak (e.g. in real time as the subject is performing the sniffs). Appropriate feedback can thus be communicated to the nurse in such a way that a next maximum effort maneuver can be done in a better way. For example, an automatic indication that a maneuver was not strong enough, was not done quickly enough (sharpness) or that the maneuver was not solely performed with the respiratory muscles alone (e.g. postural muscles recruited as well, giving not sufficient spectral flatness) can be communicated to the nurse, to further provide feedback to the subject.

The feedback may be communicated by an audiovisual output device, such as, for example, a display, a speaker, an interactive user interface or any combination thereof. The feedback provided may comprise whether the forced peak is selected as a candidate peak <NUM> based on the sharpness indication and/or the spectral flatness indication of the peak, the number of candidate peaks <NUM> which have been selected, why a forced peak <NUM> was not selected as a candidate peak <NUM> (e.g. took too long, used postural muscles, not strong enough etc.) and the features measured for each peak an how these features compare to the features of other forced peaks <NUM>. The feedback on previous forced peaks <NUM> may also be displayed.

Based on the candidate peaks <NUM> and the feedback for each peak, the nurse can decide when to abort the sniff maneuvers session. Again, some guidance can be provided to the nurse to make this decision easier. For example, some information regarding last performed sniffs can be presented. In another example, the trend of the amplitudes or quality of the last performed sniff can be shown to easily observe that there is no room for improvement anymore. By early abortion of the sniffs session, unnecessary stress for the subject <NUM> can be avoided.

<FIG> shows a second example of a system for determining a respiratory effort <NUM>. For example, the electrodes <NUM> may record the EMG signal <NUM> for a few minutes (e.g. <NUM> minutes) focusing on obtaining the average of the relaxed phase RMS peak levels <NUM>.

The smoothed relaxed signal <NUM> and the smoothed forced signal <NUM> are shown, but to prevent the figure being cluttered, the averaging windows (<NUM> and <NUM>) in <FIG> are omitted.

Once the first few minutes have been passed, the average <NUM> and maximum <NUM> of the regular relaxed breaths are computed and the system goes automatically into the mode where the system tries to identify the RMS peaks during the maximum effort maneuvers (sniffs).

For the identification of the forced peaks from the smoothed forced signal <NUM>, information obtained from the relaxed breathing phase (which is prior to the maximum effort breathing phase) can be used, for example:.

Candidate peak selection may also be based on the relaxed breathing phase. The features <NUM> (e.g. sharpness indication and spectral flatness indication) of the EMG signal <NUM> can be considered during the maximum inspiratory maneuver in order to provide information for the nurse regarding the quality of the sniff performed by the subject. The features <NUM> can be output to a display <NUM>, such that the nurse can determine which of the candidate peaks <NUM> is most appropriate for the calculation of the respiratory effort <NUM>.

Additionally, the smoothed relaxed signal <NUM>, the relaxed signal, the smoothed forced signal <NUM>, the forced signal, the respiration signal <NUM> and/or the candidate peaks <NUM> may be displayed on the display <NUM>. The nurse can thus select the user identified peak <NUM> based on the candidate peaks <NUM> through a user input interface <NUM> based on the information on the display <NUM>. Alternatively, the nurse may select the user identified peak <NUM> based on the performance of the maneuver performed by the subject <NUM> (e.g. sound, duration etc.) based on the judgment of the nurse.

The skilled person would be readily capable of developing a processor for carrying out any herein described method. Thus, each step of a flow chart may represent a different action performed by a processor, and may be performed by a respective module of the processing processor.

As discussed above, the system makes use of processor to perform the data processing. The processor can be implemented in numerous ways, with software and/or hardware, to perform the various functions required. The processor typically employs one or more microprocessors that may be programmed using software (e.g., microcode) to perform the required functions. The processor may be implemented as a combination of dedicated hardware to perform some functions and one or more programmed microprocessors and associated circuitry to perform other functions.

Claim 1:
A system for determining a respiratory effort (<NUM>) for a subject (<NUM>), the system comprising an output interface (<NUM>) and a user input (<NUM>);
the system further comprising a processor (<NUM>) configured to:
receive a relaxed signal representing a subject (<NUM>) breathing in a relaxed manner;
receive a forced signal representing the subject (<NUM>) breathing in a forced manner, wherein the relaxed signal and the forced signal are EMG signals from the second intercostal space, obtained by at least two EMG
electrodes (<NUM>) attached to the subject at the second intercostal space;
derive a plurality of forced peaks (<NUM>) from the forced signal;
select candidate peaks (<NUM>) from the plurality of forced peaks (<NUM>), wherein a candidate peak (<NUM>) is distinguished from a non-candidate peak based on features (<NUM>) of the forced peaks (<NUM>);
provide the selected candidate peaks on the output interface;
obtain a user identified peak (<NUM>) through the user input, wherein the user identified peak (<NUM>) has been selected by a user from the candidate peaks provided on the output interface; and
determine and output a respiratory effort (<NUM>) based on the relaxed signal and the user identified peak (<NUM>); and the processor being further configured to provide feedback in real time for each of the forced peaks (<NUM>), wherein the feedback indicates one or more of:
whether a forced peak (<NUM>) is selected as a candidate peak (<NUM>);
the number of candidate peaks (<NUM>) currently selected;
based on a forced peak (<NUM>) not being selected as a candidate peak (<NUM>), why the forced peak (<NUM>) was not selected as a candidate peak (<NUM>);
the features (<NUM>) of one or more of the forced peaks (<NUM>): and
feedback on previous forced peaks (<NUM>).