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    • 专利摘要:
    inhaler. the present invention relates to an inhaler (1) comprising a capsule housing (2) for containing a medicament capsule (4). the inhaler includes an air flow path (6) through which air flows during an air flow event from at least one air inlet (8) to an outlet (10), the air flow path passing through the capsule housing. there is a first sensor (16), a processor (18) and a power source (20) to drive the processor. the capsule housing is defined by at least one wall (22) and configured so that when the capsule is located in the capsule housing and sufficient air flows along the airflow path through the capsule housing, the capsule moves inside the capsule housing. the first sensor is arranged in the inhaler so that it is able to detect the movement of the capsule within the capsule housing and generate a first signal indicative of said movement. the processor receives the first signal from the first sensor and uses said first signal to determine whether the first signal is indicative of the presence, or absence, of the capsule in the capsule housing during an airflow event and to generate a capsule signal indicative of it.
    公开号:BR112013023516B1
    申请号:R112013023516-0
    申请日:2012-03-13
    公开日:2021-03-30
    发明作者:Michael Hosemann；Desmond Phillips；David Ramble；Sean Reynolds；Edward Vernon- Harcourt
    申请人:Novartis Ag；
    IPC主号:
    专利说明:

    [0001] The present invention relates to an inhaler, specifically a capsule based inhaler.
    [0002] There are many types of known inhalers through which a user can inhale to receive a medicine contained therein. Some inhalers contain multiple doses of medication that can be sequentially accessed by a user, while others are capsule based and require a user to insert at least one capsule into the device for each shipment. It can be difficult to accurately monitor the way in which a user uses the device when not directly supervised as some users do not precisely track its use. This can lead to poor compliance with a therapeutic regimen that is not apparent to what you have prescribed so that the cause of a persisting symptom is not clear. Dispensers have been proposed to allow a user or a third party to review the number of doses taken by a user, but this does not necessarily correspond precisely to the number of correct doses precisely taken by the user. For example, capsules can be removed from a dispenser, but never put in the inhaler, or put in the inhaler, but the drug was not shipped for some reason.
    [0003] The present invention provides an inhaler comprising a capsule housing to contain a medicament capsule, an airflow path through which air flows during an airflow event from at least one air inlet to an outlet, the airflow path passing through the capsule housing, a first sensor, a processor and a power source to drive the processor, the capsule housing being defined by at least one wall and configured so that when the capsule is located in the capsule housing and sufficient air flows along the airflow path through the capsule housing, the capsule moves within the capsule housing, the first sensor is disposed in the inhaler so that it is capable of detecting the movement of the capsule within the capsule housing and generating a first signal indicative of said movement, the processor receiving the first signal from the sensor and using said first si to determine whether the first signal is indicative of the presence, or absence, of the capsule in the capsule housing during an airflow event and to generate a capsule signal indicative of the same.
    [0004] The inhaler is intended to allow the delivery of medication from a capsule to an airway, for example, the lung, of a patient. The drug can be a dry powder, liquid or other suitable formulation and can include one or more active components to treat one or more disease states. The drug may include one or more non-active components which may be used to stabilize, bulk up or otherwise change one or more characteristics of the formulation. The medication may not include any active components, for example, the medication may be a placebo.
    [0005] The air flow path includes an inlet to allow air to go into the air flow path. The term air should be read to include any suitable gas, for example, a gas that can be provided to a patient who may not have an identical composition to air, for example, oxygen-enriched gas. The outlet from the airflow path can be a mouthpiece or nose piece through which a user inhales in order to receive medication from the capsule.
    [0006] An airflow event is when air flows through the airflow path. This can be caused by a user inhaling through the inhaler, for example, inhaling through a mouthpiece, or nose piece, or it can be caused by a pressure source causing air, or other gas, to flow through the path of air flow from the inlet to the outlet and into the user. Typically the air flow through the inhaler will be in the range of 15 to 150 liters per minute.
    [0007] The processor can be electronic, for example, it can include one or more analog or digital integrated circuits, distinct circuits or programmable digital processors. The processor may require a power source, for example, an electrical power source to function. The sensor may be electronic and may also need a source or electrical power to function, or it may be a passive sensor.
    [0008] The signals generated by the sensor and / or processor can be electromagnetic and can be a variable time signal, for example, a waveform, or it can be an electronic on / off or high / low signal or any other form adequate signal.
    [0009] The sensor can be any suitable type of sensor capable of generating a signal capable of being processed to provide a determination as to whether the capsule is present in the inhaler. For example, an optical sensor can be arranged to monitor the capsule housing and a signal from said sensor can be processed to determine whether the signal is indicative of movement of the capsule in the capsule housing. It is expected that a basic algorithm and test and error can produce an adequate way to process the signal.
    [00010] In one embodiment, the inhaler includes a first sensor which is an impact sensor and the first signal is an impact signal. The capsule housing is defined by at least one wall and is configured so that as the capsule moves within the capsule housing the capsule repeatedly impacts at least one wall. The impact sensor is arranged in the inhaler so that it is able to detect the impacts of the capsule on the wall of the capsule housing and generate an impact signal indicative of each impact.
    [00011] The first sensor is arranged in the inhaler so that it is able to detect the movement of a capsule within the capsule housing. It can detect the movement directly, for example, an optical sensor seeing a movement of the capsule. In an alternative modality, the sensor can detect movement indirectly by reading a parameter that can be analyzed to determine the presence or absence of a characteristic connected with the movement of the capsule, for example, the impact of the capsule with the wall, or a variation in the airflow pattern as the capsule moves through the air inlets or outlets.
    [00012] The advantage of an impact sensor, for example, over an optical sensor, is that no part of the impact sensor needs to be arranged in the air flow path which can simplify the construction of the air flow path and can make easier to retrofit said sensor to an existing inhaler design. An optical sensor would need at least one window in the airflow path through which one can 'see' inside the capsule housing and generate a signal so that the inhaler can process that signal to detect the capsule in it. The impact sensor can be any suitable sensor, for example, a pressure transducer, a microphone, or a piezo element. In one embodiment, the sensor is a microphone arranged in the inhaler in a location where the impact of the capsule on the wall of the capsule housing can be 'heard', or 'felt', by the sensor. The output from more than one type of sensor can be combined to produce a suitable impact signal. It should also be noted that the capsule can be modified to make it more readily detectable by a sensor, for example, the capsule can include a metallic or magnetic part that could be detected by a suitable sensor. In another embodiment, the sensor is a piezo element disposed in the inhaler in a place where the impact of the capsule on the wall of the capsule housing can be 'heard', or 'felt', by the sensor.
    [00013] The inhaler may additionally include memory to store a capsule signal for one or more airflow events for later retrieval. This can be any suitable form of memory and can be erasable or permanent. For example, memory may be electronically capable of being read and / or capable of being written and / or capable of being rewritten and must include flash memory, RAM, EPROM. The memory can also register the first signal, data about the time in which the signal was generated and any other data. The inhaler may include additional sensors that can provide data usage to a subscriber's user and that data can be stored in a memory for later retrieval. The data can be associated with particular airflow events.
    [00014] The inhaler may additionally include an output from which a capsule signal and / or the contents of the memory can be accessed by an external device, such as a computer. The output can include a socket into which a communication cable can be inserted. Additionally, or alternatively, the output must include a signal generator to generate and transmit a wireless signal that can be received by an external receiver. The output can be a wireless transmitter, for example, a WiFi ™ transmitter.
    [00015] The processor can analyze the first signal using one or more different algorithms. The processor can analyze the first signal from the sensor using a peak find algorithm and determine whether the calculated peak frequency is within predetermined limits in order to produce a capsule signal. These limits will be determined based on the typical frequency of rotation of the capsule in flow coefficients expected within the geometry of the inhaler. During an airflow event it was observed that the frequency with which the impact of the capsule on the wall of the capsule housing is substantially consistent and appropriate limits can therefore be generated. A peak-finding algorithm is used to reduce the effects of signal noise in detecting impact events and the computation complexity is relatively low.
    [00016] The processor can analyze the impact signal from the sensor using a frequency domain discriminating algorithm and determines whether the variation of the signal resistance ratio between two different predetermined frequency bands is within the predetermined limits in order to produce a capsule signal. During an airflow event it was observed that the impact signals differ in particular frequency ranges between signals with the capsule present and those with no capsule present. Comparing the signal resistance ratio between two different predetermined frequency ranges reduces the effects of signal noise.
    [00017] The processor can analyze the impact signal from the sensor using the two-variable statistical algorithm that calculates two statistical variables to characterize the signal and determines whether the calculated statistical measurements fall within a predetermined domain in a scatter plot. one variable against the other in order to produce a capsule signal.
    [00018] When performing statistical analysis to calculate the statistical variables to characterize the impact signal it was observed that for some measures of the impact signal it differs for signals with the capsule present and those without capsule present.
    [00019] Kurtosis is a potentially useful statistical variable for this purpose. On a probability plot against a particular variable (x), if the variable x is Gaussian, then K = 0. If, however, K> 0 the distribution extensions are thicker due to the central peak. Conversely, if K <0 then the distribution has thinner extensions and a thicker peak edge. K is thus a bidirectional measure of non-Gaussianity.
    [00020] Kurtosis (K) can be used to detect provisional capsule collisions due to the fact that said events tend to push the sample distribution extensions out in a predicted observation mode making the result distinctly non-Gaussian. The noise of breathing itself is very Gaussian. However, background noise when the inhaler is not being inhaled through has very low power (and therefore a comparatively low variation (α2)) and can have extremely high kurtosis due to the fact that even very small transients can have a large proportional impact on prolongations. of signs. This results in two types of signal that need distinction: • Breathing noise, capsule absent, (low K, low to medium α2) • Breathing noise, capsule present (medium K, low to high α2)
    [00021] The peak-to-average ratio of any signal itself, or the square of the signal, can be used as a non-capsule signal tends to have fewer high peaks (impact events) and therefore a lower peak to average.
    [00022] The calculated variables can be kurtosis and variation, or they can be the peak-to-average ratio of the square, or the magnitude, of the impact and variation sign.
    [00023] For all such algorithms, the limits that can be used to classify signal types between 'capsule present' and 'capsule not present' will vary from type of inhaler to type of inhaler and can be determined using methods simple trial and error. It is likely that there will be minimal variations for inhalers of the same type and thus the limits can be readily calculated for one type of inhaler.
    [00024] The capsule housing can be of any suitable shape within which the capsule can move enough to allow a sensor to produce appropriate signals. The capsule housing can allow a capsule to move in one or more of the following ways, back and forth longitudinally, radially or rotationally, either with a full rotation or through a limited angular extent. The capsule housing may include a portion that is substantially cylindrical in shape with a diameter longer than the capsule to be contained therein and a height greater than the diameter of the capsule, but less than the length of the capsule and the path air flow is arranged to cause the capsule to rotate within the capsule housing. This arrangement allows the capsule to rotate about an axis passing substantially across its diameter. The rotation can be in addition to a substantially random oscillation created by the air flow over the other axes.
    [00025] The inhaler can include at least one trigger that can be activated by a user to cause an opening element to open the capsule inside the inhaler. The inhaler can additionally include a trigger sensor to read the trigger trigger and generate a trigger signal. The processor can be arranged to receive the trigger signal. The trigger can be a button coupled to an opening member, for example, a piercing element or a cutting blade that are adapted to create an opening in the capsule to allow access to continued medication therein. There can be two actuators, each with an associated opening element so that two openings can be created in a capsule. The trigger sensors can be push-button keys. Each trigger can be associated with a trigger sensor, but this need not be the case. The trigger sensors can be used to 'wake up' the rest of the electronics as the pressing of the buttons must be done by a user just before inhaling through the device.
    [00026] The processor can be arranged to generate the indicative dose signal if a user has followed the correct usage sequence for the inhaler. The processor can generate the dose signal based on a capsule signal and the trigger signal, in the order in which said signals were generated and the time between said signals.
    [00027] In any of these examples, one or more filters can be applied to the signal from, or each, sensor, before one or more of the algorithms is applied to it. Filters can include one or more of a high pass filter, a low pass filter, a noise reduction filter or any other suitable filter.
    [00028] The inhaler may be substantially similar to, or substantially the same as, a capsule inhaler described in WO2005 / 113042.
    [00029] It should be understood that throughout the present specification and in the following claims, unless the context indicates otherwise, the term "comprises", or variations such as "comprise" or "comprising", implies the inclusion of the number given integer or part, or group of integers or parts.
    [00030] The present invention will now be further described, by way of example, only with reference to the accompanying drawings, in which:
    [00031] Figure 1 shows an inhaler;
    [00032] Figure 2 shows a dispersion diagram for Kurtosis vs. Variation;
    [00033] Figure 3 shows a scatter diagram of Peak-to-average Ratio vs. Variation;
    [00034] Figure 4 shows a graph of an example, of the analysis of the frequency domain discriminator;
    [00035] Figures 5a and 5b show a graph showing an example, of a peak maintenance analysis;
    [00036] Figure 6 shows an example, of electronic hardware based on digital processing; and
    [00037] Figure 7 shows an example, of a signal processing algorithm.
    [00038] Figure 1 shows an inhaler 1 comprising a capsule housing 2 containing a medicament capsule 4. Inhaler 1 comprises an air flow path 6 through which air flows during an air flow event. The airflow path 6 extends from at least one air inlet 8 to an outlet 10 and passes through the capsule housing 2. Inlet 8 enters the capsule housing 2 at a distance from the center line. In this example, the top portion 10 of the capsule housing part 2 is substantially cylindrical and the air inlet 8 enters substantially tangentially to the capsule housing 2 to encourage air to rotate within the capsule housing 2. The top portion 10 of the capsule housing 2 is substantially cylindrical in shape with a diameter longer than the capsule 4 contained therein and a height greater than the diameter of the capsule, but less than the length of the capsule 4. The capsule housing 2 includes a bottom part 12, or box, in which the capsule 4 initially sits. Capsule 4 contains a dry powder drug formulation 14.
    [00039] Inhaler 1 additionally comprises a sensor 16, in this case a microphone, located adjacent to the bottom part 12 of the capsule housing 2. The sensor 16 is coupled to a processor 18 which is driven by a power source 20, in that case a battery.
    [00040] The capsule housing 2 is defined by at least one wall 22 and is configured so that when the capsule 4 is located in the capsule housing 2 and sufficient air flows along the airflow path 6, the capsule 4 it is dragged into the top portion 10 of the capsule housing 2 and rotates in the air flow. As the capsule 4 rotates it makes repeated impacts on the wall 22 and the sensor 16 is arranged so that it is capable of detecting said impacts within the housing of the capsule 2. The sensor 16 generates a signal indicating the impacts. Processor 18 receives the signal from sensor 16.
    [00041] Inhaler 1 also includes a pair of trigger buttons 24 that are coupled to piercing members 26. The buttons 24 can be pressed by a user to have the piercing members 26 drill holes in the ends of the capsule 4 arranged in the bottom part 12 of the capsule housing 2. There are trigger sensors 28 that can generate trigger signals indicating whether the trigger button 24 has been pressed or not.
    [00042] Processor 18 receives signals from sensors 16, 28 and produces an output signal that can be indicative of one or more of the capsule's presence during an airflow event, the activation of the activation buttons 24, the correct use of the inhaler (correct sequence and timing of activation and the capsule being present during an airflow event). The output from the processor 18 and / or the raw output from the sensors are stored in a memory 30 and can be accessed using an output 32, in this case a wireless transmitter.
    [00043] It should be noted that with a microphone sensor a significant amount of noise can be detected in addition to detecting the desired impact events. The noise can be environmental, or caused by the flow of air through the inhaler. Said noise can vary considerably in volume and type so that some way of discriminating between an indicative impact signal and one that does not indicate those impacts is necessary.
    [00044] To use the device correctly it is necessary for a user to load the capsule into the inhaler, press the buttons to pierce the capsule and then inhale through the device so that the capsule is agitated and rotates in the air flow so that a powdered medicine in it is dispensed from the capsule and captured in the air flow to the patient.
    [00045] The way in which the electronic elements of the inhaler should work is as follows: 1. The user presses the buttons and trigger signals received by the processor. 2. The processor starts sampling data from the first sensor for a predefined period of time. Data is processed online according to one or more of the algorithms discussed here. The intermediate data is stored. 3. Intermediate data is checked for plausibility. Data from multiple approaches are compared if necessary. 4. The results are stored for later transmission.
    [00046] Some examples of the way in which the processor can process the impact signal are described below.
    [00047] One way to detect an impact on the signal from the inhaler when filled with the capsule is to compare the signal with a particular threshold. For example, for low to medium respiratory flow coefficients the impact of the capsule can be identified in the signal by applying a threshold and assuming that each excess threshold was caused by an impact of the capsule. If during the signal processing a sufficient number of impacts is observed, the signal can be determined as indicative of the presence of the capsule. The number of impacts depends on the frequency of rotation of the capsule which depends on the design of the inhaler and needs calibration for each type of inhaler.
    [00048] Another method of analyzing the signal from the sensor is a statistical approach in which the statistical variables are calculated to characterize the signal. The capsule inside the inhaler causes a very distinct impact agitation with high signal peaks at a low frequency. This creates a distinct amplitude distribution in the signal.
    [00049] For this analysis, the signal is first passed through a high pass filter (HPF) with the z-transform in equation (1)

    [00050] This has the double effect of (i) reducing low frequency noise and any DC compensation and (ii) stimulating high frequency noise and transient capsule collisions.
    [00051] A sliding window algorithm is then performed by operating on N data samples (typically N = 2048), grouping N samples at a time by economy. In each window, kurtosis K and variation α2 are computed using equations (2) and (3). The sliding window that has maximum power (empirically associated with maximum 'information' for the cycle of use) generates the necessary detector output (K, α2). Assuming that the data is going to be zero mean after the HPF, the sums can be taken instantly without being aware of the average.

    [00052] As previously described, Kurtosis is thus a bidirectional measure of non-Gaussianity. If a random variable x is Gaussian, then K = 0. If, however, K> 0 the extension of the distribution is thicker at the expense of the central peak. Conversely, if K <0 then the distribution has thinner extensions and a wider and thicker peak K.
    [00053] Kurtosis is suitable for detecting the impact events of the capsule by the fact that the referred events tend to push the extension of the sample distribution out in an observable and predicted way making the result distinctly non-Gaussian. Breathing noise alone is much more Gaussian.
    [00054] This provides two types of signal that need distinction: • Breathing noise, capsule absent, (low K, low to medium α2) • Breathing noise, capsule present (medium K, low to high α2)
    [00055] Simulation tests were conducted with the aim of being able to classify an input signal as belonging to one of two classes (H1 = breath noise + capsule present, H0 = not H1). Some examples, the results are shown in the scatter diagram in Figure 2.
    [00056] It shows all the data sets registered using an exemplary type of microphone. Two distinct areas of results can be identified in the scatter diagram. Among them is an area where no results are found. The two areas represent the breathing noise with the capsule rotating and only the breathing noise.
    [00057] As mentioned, environmental noise has a lower Kurtosis. When the aforementioned signal is added to the capsule + the breath noise signal, the general Kurtosis becomes smaller. Thus, the data points will move lower in the scatter diagram.
    [00058] After calculating the variation and Kurtosis, the decision to classify the results must be made. This is done by checking which of the three areas outlined by the lines in Figure 2 the data points fall into.
    [00059] Anything that falls above the upper line 'C' represents a sign indicating the capsule being present. Anything that falls below the bottom line 'D' represents only the noise of breathing. Anything that falls between the two lines represents the capsule with noise.
    [00060] It is observed that very high levels of environmental noise can mask the noise of the capsule and thus push the data points from the region of the capsule into the region of the capsule. In order to detect these items, the techniques described later can be used.
    [00061] This algorithm is useful because it is robust, of low computational complexity and has low memory requirements. It is observed that calculating Kurtosis requires a relatively large dynamic range as squares and squares of squares have to be calculated.
    [00062] This technique works for coefficients of samples as low as few kilohertz. Nyquist sampling is not necessary as long as the peaks can still be sampled.
    [00063] The high-order statistical method described above can be simplified. The purpose of that method is to detect the presence of high peaks in the signal while the majority of the signal is relatively low. This was accomplished when calculating Kurtosis. A potentially simpler method is to use the peak-to-average ratio of the signal square.
    [00064] For this method, the signal is once again passed through the high pass filter described in equation (1). Then, again the variation is calculated for the windows of typically 2048 samples. Also, the largest square of a signal sample is recorded for each window. Its value is divided by the mean to become the peak-to-average ratio.
    [00065] The variation and peak-to-average ratio are then used as Variation and Kurtosis before. This is illustrated in Figure 3.
    [00066] The same processing as previously where data points are classified according to their area on the scatterplot is performed.
    [00067] This method has the same low memory requirements as the high-order statistical method. Additionally, it requires less computations and has a smaller dynamic range. This simplifies operation on economical, small, low-energy processors that typically offer only fixed-point computations.
    [00068] Another algorithm that can be used to analyze the signal from the sensor is the analysis of the frequency domain discriminator. Figure 4 shows the comparison of the analysis of a signal from an airflow event (50 l / min) in the inhaler with the present capsule, Line A, and without, Line B. It is apparent that the frequency spectrum with data from a full capsule and without a capsule are similar in amplitude in a frequency range 1 kHz to 2.5 kHz, but very different at frequencies above 4 kHz which is due to the distinct signals from the impacts of the capsule inside the inhaler.
    [00069] This algorithm compares the signal energy in the range of 1 - 2.5 kHz with that above 3 kHz. This can be accomplished by performing the fast Fourier transform and adding the energy in the different bands, or simpler in the time domain through the use of a combination of bandpass filters and high-pass filters. Said filters and the subsequent energy comparison can be implemented using analog or digital techniques. This algorithm is useful to the extent that there is a low computational complexity, if implemented in filters that use time domain.
    [00070] From the test on several flow coefficients and with simulated noise and breathing profiles it was observed that the referred method provides a reasonably robust method to detect the presence of the capsule in the inhaler, even in the presence of high noise levels. It was observed that the majority of the content of the tested environmental noise spectra was below 1000 Hz and therefore it would not affect the calculation of the energy ratio performed here.
    [00071] Another method is to apply a peak detection algorithm, which aims to identify all the peaks in the signal that were caused by the impacts of the capsule. Again, the signal is filtered from high pass as in equation (1). The rest of the algorithm can be performed on square samples of the filtered signal or just the filtered signal itself. The signal can be processed in a sliding window mode to allow the profile to be calculated over time but this is not necessary.
    [00072] For this algorithm the amplitudes of the samples or their square are compared to the peak lift value. If the sample is greater than the peak lift value, a new peak lift event is said to have occurred. In this case, the peak counter is incremented and the peak lift value is adjusted to the sample value. If, however, the next sample is smaller in amplitude than the peak lift value, no peak lift event is observed, and the current peak lift value is simply reduced by multiplying it by an appropriate reduction factor. (in this case, a suitable value is around 0.99). The pseudo code for this algorithm is given below where d (k) is kth sample data, and pk_sustain is the peak sustain value. IF d (k)> pk_sustentação pk_sustentação = d (k) pico_counter = pico_counter + 1 pico_evento (k) = 1; Or otherwise pk_sustentação = 0.999 * pk_sustentação FIM
    [00073] Not shown in the pseudo code is that in a modality of this algorithm at least 20 samples must have been processed before the next peak sustain event can be considered to have occurred. This prevents a set of peak sustaining events from occurring around the start of the impact of the event capsule and ensures that each impact is counted only once.
    [00074] Also, low level noise can be removed using a threshold. Only sample values above a threshold are considered to be valid peaks. This avoids counting very small peaks that are not actually impacts from the capsule.
    [00075] I try to find peak sustain events, the algorithm measures the time between each event from which a fundamental frequency can be calculated. Then the number of occurrences of particular fundamental frequencies within the 10 Hz band is counted. Test results with the current type of inhaler show that because of well-defined capsule impact events, the impact signal from a full capsule measurement has a lower frequency content than high frequency content, and so comparing the signal energy below 110 Hz to that above 300 Hz this is an adequate metric to differentiate between full and non-capsule events.
    [00076] Figures 5a and 5b show the results of peak lift processing for the measurement of full capsule and without capsule respectively. Both tests were conducted at 20 L / min of flow through the inhaler and the graphs show the amplitude of the signal on the vertical axis and the number of samples along the horizontal axis.
    [00077] The symbol 'o' represents a peak holding event and the lines joining the 'o's show how it falls between each of the aforementioned events. It should be noted from the Figures that the prominent capsule impact peaks were located for the measurement of the full capsule, but for the measurement without capsule the algorithm locates only the closely spaced peaks due to the nature of the noisy waveform.
    [00078] Tests on higher flow coefficients (150 l / min) showed that the individual impact peaks are closer together so that there is more high frequency content in the filled capsule measurements and as such the proportion between the sub- 110 Hz for 300-1000 Hz of energy is not as large as for the lower flow coefficient measurements, but it is still usable.
    [00079] Although all the algorithms described to date provide good performance in ideal conditions, and quiet, environmental noise or noise caused by manipulation of the inhaler can cause false results.
    [00080] In order to avoid false results, the following techniques can be used:
    [00081] Dealing with noise can cause high individual peaks in the signal. These are relatively similar to the peaks caused by impact of the capsule. However, only a very limited number of spikes are caused, for example, by dropping the inhaler on or impacting it against a hard surface. Although the statistical algorithms or the frequency domain discriminator may not distinguish these events from the capsule events, they can be supplemented by the peak support method. The capsule rating present for a signal can only be considered valid if there are enough peaks present in a time loop. Otherwise, the result is classified as noise.
    [00082] Loud background noise can disguise the signal peaks that are used to detect the capsule using the various algorithms. As breathing lasts only a limited time before and after breathing where no breathing capsules and noises are expected. Thus, the first part of the signal after the buttons are pressed (typically 0.1 to 0.5 seconds after pressing the button) and the last part before stopping to evaluate the signal (typically after 10 to 30 seconds) can be used to check environmental background noise levels. If the referrals are above a certain level that makes the capsule detection algorithm (s) employed unreliable, a noise result must be created.
    [00083] Figure 6 shows the hardware used in the example. The signal from microphone 16 is passed to a high pass analog filter 50 which is a simple first order RC filter with a 3dB frequency of 1 kHz. From there the signal passes to an analog to digital converter (ADC) 52 that samples at 9.6 kHz and has a resolution of 12 bits. The ADC can be integrated into a microprocessor chip 54.
    [00084] Figure 7 shows an example, of the combination of algorithms that can be performed on the samples once they are sent to the microprocessor 54. First, the signal is sectioned in windows of 2048 samples in an assembly operation. windows 56.
    [00085] These are processed by a simple high pass filter 58. The simplest implementation is to subtract the previous sample from the current one. This removes any DC compensation that may be present due to the circuit items in ADC. In one example, the signal may be an inhalation lasting about two seconds at the beginning of the signal which is followed by a silent period. The search window has to be much longer than the average breath in which the time taken by the user between piercing the capsule and inhaling is unknown.
    [00086] The sign is then square 60 and the average of the square samples is calculated 62 for a total of 2048 window samples. This is done in the upper branch of the algorithm diagram in Figure 7. Also, the highest value of the square is recorded 64 in the central branch. This can be done while computing the squares or via a search on all square samples if they are stored in memory. After computing all the sample squares and their mean and finding their peak value, the peak-to-average ratio is computed 66. Both the square means (range) and peak-to-average ratio are stored for that window for later sorting.
    [00087] The lower branch in the algorithm diagram in Figure 7 counts the peaks within window 68. First, a threshold is used to remove the small peaks caused by noise. Then the peak detection algorithm is applied to find the peaks caused by the capsule's impacts.
    [00088] This process is repeated for each window until all windows are processed. It is expected that there will be a group of results in about zero variation and with a low peak-to-average ratio. These are the results from the windows that include only environmental noise, for example, after inhalation. Windows during inhalation tend to produce results with a higher ratio of variation and peak to average.
    [00089] Classification 70 begins with the search for the window with the highest variation. As it contains the highest signal energy, it provides the most reliable information in the presence of other noises. Other measures such as searching for a set of continuous windows with the highest energy are also possible to achieve optimum reliability. For the window with the highest variation, the peak-to-average ratio is seen in the results.
    [00090] The result now needs to be classified by comparing it to the set of thresholds. These thresholds were determined by carrying out a large number of experiments with and without capsules for various flow coefficients. A scatter plot of the results of said experiments can typically be divided into four regions: 1. "Capsule" is typically at the top. This is the region of high peak-to-average ratio (PMR) due to the peaks from the capsule. 2. "Silence". This region has a very low variation and low PMR. 3. "Without Capsule". This region has low and low PMR variation. The maximum variation is much lower than that found for the capsules since no impact from the capsules can increase the noise level. 4. "Noise Capsule". This region falls between "Capsule" and "Without Capsule". Results will drop in that region if a capsule signal has been subjected to high levels of environmental noise. Since the environmental noise level has a lower PMR than capsule noise, it reduces the total PMR.
    [00091] Finally, checks on two items that may arise from the processing carried out so far are carried out. Noises from handling the inhaler 72, e.g. accidentally dropping it on a hard surface causes large spikes in the signal. Referrals can dominate the variation and cause a very high PMR. This would lead to an erroneous classification as "Capsule". Said manipulation noise events typically show only two to 4 peaks per window while a rotating capsule shows more than 10. Also, due to the duration of a breath, the capsule rotates for at least one second. Thus, the number of peaks in five consecutive windows is added. If this sum is greater than 50, the "Capsule" rating is confirmed. Otherwise, the classification verdict is revised to "Noise".
    [00092] In some cases, a silent capsule signal can be masked by high environmental noise. Due to the low PMR, this would be inserted in the "Without Capsule" region. To recognize this situation, the variation in the last processed window is checked 74. If it is greater than about twice the threshold of silence, the classification verdict is changed to "Noise". This helps to ensure that none, or very few, false negative results are reported.
    [00093] It should be understood that the present invention has been described above only as an example only and that changes in details can be made without deviating from the scope of the claims.
    权利要求:
    Claims (14)
    [0001]
    1. Inhaler (1) comprising a capsule housing (2) for containing a medicament capsule (4), an air flow path (6) through which air flows during an air flow event from at least minus an air inlet (8) to an outlet (10), the airflow path (6) passing through the capsule housing (2), the capsule housing (2) being defined by at least one wall (22 ) and configured so that when a capsule (4) is located in the capsule housing (2) and sufficient air flows along the airflow path (6) through the capsule housing (2), the capsule (4) moves within the capsule housing (2), the inhaler (1) characterized by including a first sensor (16), a processor (18) and a power source (20) to drive the processor (18), the first sensor (16) is arranged in the inhaler (1) so that it is able to detect the movement of the capsule (4) inside the capsule housing (2) and generate a first signal indicative of said of movement, the processor (18) receiving the first signal from the first sensor and using said first signal to determine whether the first signal is indicative of the presence, or absence, of the capsule (4) in the capsule housing (2) during an airflow event and generate a capsule signal indicative of it.
    [0002]
    2. Inhaler according to claim 1, characterized by the fact that the first sensor (16) is an impact sensor and the first signal is an impact signal and the capsule housing is defined by at least one wall (22 ), the capsule housing (2) being configured so that as the capsule (4) moves within the capsule housing (2) the capsule (4) repeatedly impacts at least one wall (22), the impact sensor (16) is arranged in the inhaler (1) so that it is able to detect the impacts of the capsule on the wall of the capsule container (22) and generate an impact signal indicative of each impact.
    [0003]
    3. Inhaler according to claim 1, characterized in that the inhaler additionally includes memory (30) for storing a capsule signal for one or more airflow events for later retrieval.
    [0004]
    4. Inhaler according to claim 1 or 2, characterized in that the inhaler additionally includes an outlet (32) from which a capsule signal, or memory content can be accessed.
    [0005]
    5. Inhaler according to any one of the preceding claims, characterized by the fact that the processor (18) analyzes the impact signal from the sensor (16) using the peak detection algorithm and determines whether the calculated peak frequency it is within predetermined limits in order to produce a capsule signal.
    [0006]
    6. Inhaler, according to any of the preceding claims, characterized by the fact that the processor (18) analyzes the impact signal from the sensor (16) using the frequency domain discriminator algorithm and determines whether the resistance ratio signal between the two different predetermined frequency bands is within the predetermined limits in order to produce a capsule signal.
    [0007]
    7. Inhaler, according to any of the previous claims, characterized by the fact that the processor (18) analyzes the impact signal from the sensor (16) using the two-variable statistical algorithm that calculates two statistical variables to characterize the signal and determines whether the calculated statistical measure falls within a predetermined domain in the scatter plot of one variable against the other in order to produce a capsule signal.
    [0008]
    8. Inhaler, according to claim 7, characterized by the fact that the calculated variables are kurtosis and variation.
    [0009]
    9. Inhaler, according to claim 7, characterized by the fact that the calculated variables are the peak-to-average ratio of the square, or the magnitude, of the impact and variation signal.
    [0010]
    10. Inhaler, according to any of the preceding claims, characterized by the fact that the processor (18) analyzes the impact signal from the sensor (16) using at least two different algorithms.
    [0011]
    11. Inhaler according to any one of the preceding claims, characterized by the fact that the first sensor (16) is a microphone or a piezo element.
    [0012]
    12. Inhaler according to any one of the preceding claims, characterized in that the capsule housing (2) includes a portion that is cylindrical in shape with a diameter longer than the capsule (4) to be contained therein. and a height greater than the diameter of the capsule (4), but less than the length of the capsule and the airflow path (6) are arranged to cause the capsule to rotate within the capsule housing.
    [0013]
    13. Inhaler according to any one of the preceding claims, characterized by the fact that the inhaler includes at least one actuator (24) that can be actuated by a user to cause an opening element (26) to open the capsule inside of the inhaler, the inhaler additionally including a trigger sensor (28) to read trigger from the trigger (24) and generate a trigger signal, the processor being arranged to receive the trigger signal.
    [0014]
    14. Inhaler, according to claim 13, characterized by the fact that the processor (18) is willing to generate the dose signal indicative of whether a user followed the correct use sequence for the inhaler, the processor (18) generating the dose signal based on a capsule signal and the trigger signal, the order in which the said signals were generated and the time between the signals.
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    同族专利:
    公开号 | 公开日
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    KR20140012128A|2014-01-29|
    EP2865403B1|2016-06-15|
    CN103442758A|2013-12-11|
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    MX355950B|2018-05-07|
    JP2014513591A|2014-06-05|
    MX2013010579A|2013-10-03|
    RU2013145910A|2015-04-20|
    CA2829708A1|2012-09-20|
    CN103442758B|2016-03-23|
    EP2686049B1|2015-03-04|
    WO2012123448A1|2012-09-20|
    US9555200B2|2017-01-31|
    AU2012228300C1|2015-03-05|
    US20140000603A1|2014-01-02|
    BR112013023516A2|2016-12-06|
    EP2686049A1|2014-01-22|
    KR101938430B1|2019-01-14|
    JP6249778B2|2017-12-20|
    PT2686049E|2015-07-06|
    ES2591177T3|2016-11-25|
    AU2012228300B2|2014-07-31|
    AU2012228300A1|2013-09-12|
    CA2829708C|2019-09-03|
    PL2865403T3|2017-04-28|
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    法律状态:
    2018-12-18| B06F| Objections, documents and/or translations needed after an examination request according [chapter 6.6 patent gazette]|
    2019-10-01| B06U| Preliminary requirement: requests with searches performed by other patent offices: procedure suspended [chapter 6.21 patent gazette]|
    2020-08-18| B06A| Notification to applicant to reply to the report for non-patentability or inadequacy of the application [chapter 6.1 patent gazette]|
    2021-02-17| B09A| Decision: intention to grant [chapter 9.1 patent gazette]|
    2021-03-30| B16A| Patent or certificate of addition of invention granted|Free format text: PRAZO DE VALIDADE: 20 (VINTE) ANOS CONTADOS A PARTIR DE 13/03/2012, OBSERVADAS AS CONDICOES LEGAIS. |
    优先权:
    申请号 | 申请日 | 专利标题
    US201161452763P| true| 2011-03-15|2011-03-15|
    US61/452,763|2011-03-15|
    PCT/EP2012/054371|WO2012123448A1|2011-03-15|2012-03-13|Inhaler|
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