Patent Application
20150164428
The following relates to performing quality assessments for variability analyses.
Physiological waveforms are now recorded at the bedside for the purposes of storage and analysis. This valuable data can be used for retrospective review, and can also be processed and used by display and decision support algorithms. The challenge is shifting from data availability towards data quality: i.e., removing noise and artifacts and finding relevant information within the surplus of available data. Computerized data collection systems now allow for the analysis of vast quantities of data. To ensure reliability and fidelity of results provided by the intersection of computer science and health care, a quality assessment should be in place to ensure the waveforms are appropriately processed (G. Takla, J. H. Petre, D. J. Doyle, M. Horibe and B. Gopakumaran, “The problem of artifacts in patient monitor data during surgery: a clinical and methodological review,” Anesth. Analg., vol. 103, pp. 1196-1204, November 2006).
This process, which combines the flexibility of a visual inspection with objective physiological measurements identifies and removes signal features which may compromise the quality of downstream processing.
The science of variability measurement is gaining recognition for its importance as an indicator of illness severity and for its possible diagnostic use. The importance of examining waveforms and events (i.e. time series extracted from those waveforms) prior to variability measurement has been widely observed (see: i) Q. Li, R. G. Mark and G. D. Clifford, “Robust heart rate estimation from multiple asynchronous noisy sources using signal quality indices and a Kalman filter,” Physiol. Meas., vol. 29, pp. 15-32, January 2008; ii) T. C. Smith, A. Green and P. Hutton, “Recognition of cardiogenic artifact in pediatric capnograms,” J. Clin. Monit., vol. 10, pp. 270-275, July 1994; and iii) V. Papaioannou, C. Dragoumanis and I. Pneumatikos, “Biosignal analysis techniques for weaning outcome assessment,” J. Crit. Care, vol. 25, pp. 39-46, March 2010) and presenting one or more variability measurements to clinicians may provide a better understanding of the complexity of event patterns (see: i) I. Jabloński, K. Subzda and J. Mroczka, “Software Tool for Assessment of Complexity and Variability in Physiological Signals of Respiration,” International Journal of Measurement Technologies and Instrumentation Engineering, vol. 28, pp. 28, 2011; and ii) M. F. El-Khatib, “A diagnostic software tool for determination of complexity in respiratory pattern parameters,” Comput. Biol. Med., vol. 37, pp. 1522, 2007).
In 1996, the Task Force of the European Society of Cardiology the North American Society of Pacing published recommendations and highlighted the clinical relevance and applicability of HRV (Electrophysiology, Task Force of the European Society of Cardiology the North American Society of Pacing, “Heart Rate Variability Standards of Measurement, Physiological Interpretation, and Clinical Use,” Circulation, vol. 93, pp. 1043-1065, March 1996). Ten years later, an IEEE review drew attention to the significance of measuring HRV for the monitoring of sepsis, exercise, post myocardial infarction patients and sepsis in adults, as fetal distress and apnea in neonates (S. Cerutti, A. L. Goldberger and Y. Yamamoto, “Recent Advances in Heart Rate Variability Signal Processing and Interpretation,” IEEE Transactions on Biomedical Engineering, vol. 53, pp. 1, January 2006). Ever since, measurements of complexity have expanded to include numerous measures in the statistical, geometric, energetic, informational and invariant domains (A. Bravi, A. Longtin and A. J. Seely, “Review and classification of variability analysis techniques with clinical applications,” Biomed. Eng. Online, vol. 10, pp. 90, Oct. 10. 2011).
It has been recognized that the clinician viewing variability results needs to know the quality of the variability measurements, and the quality of the waveform and event time series that was used to calculate that variability. It has also been found that there is a need to measure the quality of the data presented to the clinician in an automated and reliable fashion since relying on a visual inspection of waveform is typically insufficient.
To address the above, the following provides a modular framework to assess the quality of an input, event time series and variability measures, for the purpose of variability analysis. The method relates generally to medical monitoring and specifically to a quality assessment for the purpose of variability monitoring. The analysis comprises of a comprehensive quality assessment including assessing the quality at the waveform, event, stationarity and variability measurement level. A use of the quality measurements in the display variability measurement over time in the vicinity of a specific clinical event is also presented.
In one aspect, there is provided a method of assessing quality for a variability analysis, the method comprising: obtaining at least one waveform corresponding to a corresponding physiological measurement; determining at least one measure of waveform quality of the at least one waveform; extracting from a waveform, at least one event time series; determining a measure of event time series quality of the at least one event time series; determining at least one measure of stationarity of the at least one event time series; computing a quality measure using the at least one measure of waveform quality and the at least one measure of stationarity; and displaying the quality measure.
In another aspect, the method further comprises at least one of: performing a variability analysis on the at least one event time series at least one waveform, and displaying variability data, wherein the quality measure is displayed with variability data; computing a quality index using the at least one measure of waveform quality, wherein the quality index is computed using at least one of a threshold applied to a range for the quality measure and a mathematical model; wherein determining the at least one measure of waveform quality further comprises performing physiological filtering to remove at least one event from the at least one event time series; wherein determining the at least one measure of waveform quality further comprises removing at least one segment of a waveform signal; wherein determining the at least one measure of stationarity comprises removing at least one segment of the time series; wherein determining the quality measure further comprises performing event filtering on events detected from the at least one waveform; wherein determining the waveform quality comprises analyzing the at least one waveform for at least one of disconnections, saturations in the signal, and wandering baselines; and wherein the at least one waveform is displayed with the quality measure.
In yet another aspect, there is provided a computer readable storage medium comprising computer executable instructions for performing the method.
In yet another aspect, there is provided a system comprising a processor and memory, the memory comprising computer executable instructions for performing the method.
Embodiments will now be described by way of example only with reference to the appended drawings wherein:
It will be appreciated that for simplicity and clarity of illustration, where considered appropriate, reference numerals may be repeated among the figures to indicate corresponding or analogous elements. In addition, numerous specific details are set forth in order to provide a thorough understanding of the examples described herein. However, it will be understood by those of ordinary skill in the art that the examples described herein may be practiced without these specific details. In other instances, well-known methods, procedures and components have not been described in detail so as not to obscure the examples described herein. Also, the description is not to be considered as limiting the scope of the examples described herein.
It will be appreciated that the examples and corresponding diagrams used herein are for illustrative purposes only. Different configurations and terminology can be used without departing from the principles expressed herein. For instance, components and modules can be added, deleted, modified, or arranged with differing connections without departing from these principles.
Physiological waveforms are now harvested at the bedside and manipulated to provide informational and decisional data points for clinicians and caregivers. For example, the study of heart rate variability (HRV) which is derived from the electrocardiogram (ECG) has benefited from nearly two decades of research and its applications in clinical practice are wide ranging. HRV is widely studied and used as a marker of illness severity.
Variability analysis measures the complexity of a time series of event occurrences, such as heart beats or breaths. It has been recognized that assessing the quality of the events, and the underlying waveform from which the events are derived is important to validate the subsequent interpretation of the variability measurements. The quality of the variability measurements themselves is also important in providing confidence in the reported values.
It can be appreciated that the components in
The present quality assessment therefore includes a modular framework for the analysis of a generic physiological waveform, and may also include event and stationarity assessments to prepare a high quality event time series for a variability analysis, and to measure the quality of the reported variability measures. The overall quality of the window can be reported as an index which summarizes the quality of the data at each step in processing. The framework described herein is also applied to the capnogram which is one embodiment of the method.
The following provides a quality assessment, addressing specific concerns for variability analysis. One embodiment uses the end tidal CO2 signal as an input waveform presented in section III.
The quality stages shown in
In a variability analysis, variability is calculated over time on the high quality event time series, usually on a plurality of windows, which may overlap. A quality assessment for variability may also be provided for variability measures calculated in time periods surrounding a clinical event. Therefore combining the waveform and event quality measures over a window provides a more complete quality assessment. The diagram of the assessment is presented in
As illustrated in
The quality index 34 is implemented optimally combining the quality measures and the stationarity information using a machine learning model (e.g. using decision trees). The quality index 34 is used to summarize the information from the quality measures into a simple metric which can be used by those clinicians uninterested in the finer details of the quality analysis. The quality report 30, derived from the quality assessment is linked, through a time stamp to the waveform, event and variability information and displayed on the display 14. In addition to the quality report 30, the quality of individual variability calculations 38 can also be displayed as shown in
It can be appreciated that the framework described herein may be applied to any physiological waveforms including sets of multi organ waveforms such as the ECG and capnography waveforms which are produced by different organ systems yet are intrinsically related as measure by the cardiopulmonary synchrony (P. Z. Zhang, W. N. Tapp, S. S. Reisman and B. H. Natelson, “Respiration response curve analysis of heart rate variability,” IEEE Transactions on Biomedical Engineering, vol. 44, pp. 321, April 1997). Amongst the two signals, only the ECG has a clearly defined physiological model and morphology and has been extensively studied (Electrophysiology, Task Force of the European Society of Cardiology the North American Society of Pacing, “Heart Rate Variability Standards of Measurement, Physiological Interpretation, and Clinical Use,” Circulation, vol. 93, pp. 1043-1065, March 1996), and (S. Cerutti, A. L. Goldberger and Y. Yamamoto, “Recent Advances in Heart Rate Variability Signal Processing and Interpretation,” IEEE Transactions on Biomedical Engineering, vol. 53, pp. 1, January 2006).
The capnogram has benefited from extensive documentation of tracings (B. Smalhout and Z. Kalenda, An Atlas of Capnography., 2nd ed. The Netherlands: Kerckebosche Zeist, 1981). Prior to the widespread of powerful computers, analysis and measurements were done by hand (measuring angles, visual inspection of shape, and selection of individual breaths for classifiers and detectors), see (B. Smalhout and Z. Kalenda, An Atlas of Capnography., 2nd ed. The Netherlands: Kerckebosche Zeist, 1981), and see (J. M. Goldman and B. H. Dietrich, “Neural network analysis of physiologic waveforms,” in Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Vol. 13, 1991, pp. 1660).
Limitations of this method may include reproducibility, a reliance on experts with limited availability, and a limit to the number of analyses which may be conducted. To overcome this, the system described herein extends the knowledge gained from HRV and address the limitations in traditional capnograph processing to provide a complete quality assessment for generic physiological waveform inputs. The quality of the signal is ascertained at multiple levels of processing (waveform, events, stationarity), which are specific to variability analysis. The quality process applied to the end tidal CO2 signal as an example of use in section III, and an example of quality report on the ECG is presented in section IV.
As discussed above, the quality module 10 may be considered an extension of individual variability measures and analyses to provide quality data 18 with variability waveforms. As such, the generation of a quality report 30 (and other quality data 18) can be applied in any context in which variability measures, obtained from a variability analysis component 12′, can be applied. For example, quality measures can be generated using data obtained in real-time, older data, data obtained in an intensive care unit (ICU), data obtained using portable monitoring devices recording variability, etc. For example, data can be summarized in a mathematical model, which is then used for the computation of quality. A quality assessment of variability therefore is not dependent on any particular mechanism for obtaining the variability data, so long as a set of variability measures is available, and a quality measure can be obtained, as explained in greater detail below. The following illustrates three exemplary monitoring sites 111 (e.g., 111a, 111b, 111c) to demonstrate the various ways in which the variability measures can be obtained in order to generate a quality assessment. Further detail concerning an underlying software framework for obtaining and distributing variability data can be found in applicant's co-pending U.S. patent application Ser. No. 12/752,902, published under US 2010/0261977 and issued as U.S. Pat. No. 8,473,306 to Seely, the entire contents of which are incorporated herein by reference.
An example of a hospital monitoring site 111a is shown in
The patient interfaces 134 monitor physiological parameters of the patient 133 using one or more sensors 135. The data or patient parameters can include any variable that can be accurately measured in real time or intermittently. The data may be obtained from a continuous waveform (at a certain frequency level, e.g. 100 Hz for a CO2 capnograph or 500 Hz for an EKG), or taken as absolute measurements at certain intervals, e.g. temperature measurements. The sensors 135 and patient interfaces 134 may include, for example, an electrocardiogram (ECG), a CO2 capnograph, a temperature sensor, a proportional assist ventilator, an optoelectronic plethymography, a urometer, a pulmonary arterial catheter, an arterial line, an O2 saturation device and others. To provide more meaning to the data acquired through the sensors 135, clinical events are associated with the data, through an act of recording time stamped events 136, which are typically entered by a heath care worker 137 in the hospital (bedside) environment. Clinical (time stamped) events can be physical activity, administration of medication, diagnoses, life support, washing, rolling over, blood aspiration etc. The clinical events are associated with a specific time, which is then also associated with the data that is acquired at the same specific time using the sensors 315. It will be appreciated that the clinical events can also be recorded in an automated fashion, e.g. by utilizing algorithms which detect events electronically and process such events to designate them as clinical events or noise. In this example, the patient interface 134 is configured to gather the time stamped event data 136 concurrently with the sensor data 135, further detail being provided below. It may be noted that additional non-time-stamped information (e.g. demographics) can also be recorded for each patient.
As can be seen in
The variability analysis server 12′ can also interact with a bedside monitor 140, which may be made available to or otherwise represent a nurse or other personnel that monitors the patient 133 at the bedside. Similarly, the variability analysis server 12′ can also interact with sensor displays 144, which are associated with other medical equipment such as ECGs, blood pressure sensors, temperature sensors etc. As noted above, the variability analysis server 12′ can be a separate, stand-alone unit but may also be integrated as a plug-in or additional module that in this case could be used or integrated with existing bedside monitoring equipment, displays and sensors.
Turning now to
A mobile site 111c is shown in
In the example shown in
As noted above, each monitoring site 111 may include a variability analysis server 12′. Details of various embodiments of existing variability analysis apparatus and configurations can be found in U.S. Reissue Pat. No. RE41,236 E to Seely, the entire contents of which are incorporated herein by reference.
An example of a quality assessment for the purpose of variability monitoring is now provided.
i) The assessment diagrammed in
ii) The analysis of the waveform quality 20 can comprise an analysis for a) disconnections; data segments identified as being outside monitor range (i.e. negative value on a breathing rate monitor), b) saturations in the signal and gross amplitude changes and c) wandering baselines.
i) The modular quality framework described herein accepts at this point data provided as a time series of events (if waveform 32 is not available due, for example, to data storage constraints). However quality analyses relying on the waveform interpretation may not be feasible in such a situation.
ii) Waveform portions not identified as disconnections represent useable portions of the signal for the purpose of segmentation. Unusable portions create gaps, or interruptions in the signal. Waveforms 32 are segmented into events using methods appropriate to the input waveform 32. For example R-peak detectors may be appropriate for ECG signals, and zero-crossing detections may be appropriate for breathing signals centered on zero.
iii) Following the segmentation into events, the method comprises comparing event measurements to literature standards to determine at stage 22, if each event meets appropriate physiological limits. The criteria can include boundaries on event duration and event amplitude.
i) Further stratification of events creating three event categories: 1) non-physiological, 2) physiological, and 3) physiological and high quality.
ii) The event filtering stage 24 of the assessment is variable in scope and modular and comprises any of the three following methods. Some methods of event quality discrimination may be accomplished at this stage comprise:
a) Event quality discrimination through segmentation involving a comparison with a template or population norms,
b) Event quality discrimination involving multichannel signal comparison (e.g. see Q. Li, R. G. Mark and G. D. Clifford, “Robust heart rate estimation from multiple asynchronous noisy sources using signal quality indices and a Kalman filter,” Physiol. Meas., vol. 29, pp. 15-32, January, 2008 and B. Krauss, A. Deykin, A. Lam, J. J. Ryoo, D. R. Hampton, P. W. Schmitt and J. L. Falk, “Capnogram Shape in Obstructive Lung Disease,”Anesthesia & Analgesia, vol. 100, pp. 884, March, 2005), and
c) Event quality discrimination comprising of ectopic filtering which can involve statistical rules or outlier detection algorithms (e.g. see Q. Li, R. G. Mark and G. D. Clifford, “Robust heart rate estimation from multiple asynchronous noisy sources using signal quality indices and a Kalman filter,” Physiol. Meas., vol. 29, pp. 15-32, January 2008; and S. Nemati, A. Malhotra and G. Clifford, “Data Fusion for Improved Respiration Rate Estimation,” EURASIP} Journal on Advances in Signal Processing, vol. 2010, pp. 926305, 2010).
i) Variability analysis at stage 36 comprising variability measures chosen from a plethora of over 100 measures from four domains, and calculations are performed on the high quality event time series.
ii) The present modular framework can allow selectable variability measures for organ specific or signal specific quality assessment report for variability analyses.
iii) These calculations, representing the complexity of an event time series, are performed over a set window length.
iv) A variability analysis comprising of multiple windows to report the changes in variability over time.
v) An event-based variability analysis applied to portions of signal before and after an event, and selected for a window based analysis.
i) The windowed high quality event time series signal is subject to a stationarity assessment stage 28 which comprises the use of models applied to windows of high quality event time series. Data models to assess the stationarity of the high quality event time series in a window may include linear trend, spike and step models.
ii) The models in the stationary assessment stage 28 may make use of customizable thresholds. One method to select thresholds is by analysis of the histogram and cumulative distribution function of the stationarity models applied to a dataset. For example, thresholds may be selected to retain 95% of data points.
iii) In cases of non-stationary windows, the quality index, Q, may be automatically downgraded to zero or “Low” quality, which removes or alters the display of the variability for that window, to ensure inappropriate measurements are interpreted appropriately.
i) Quality measures 26, Q, are derived from the waveform quality stage 20, physiological filtering stage 22, event filtering stage 24, and stationarity assessment stage 28 to represent the quality of the waveform and events within a window as a single number.
ii) Certain variability measures are more sensitive to the quality of the input waveform, therefore to the quality of the variability measurements is also assessed using intrinsic and extrinsic parameters to produce a confidence interval about the variability measurements. Intrinsic parameters may be goodness of fit values embedded in the algorithms themselves while extrinsic factors may include statistics from the waveform and event quality processing such as percentage of removed data.
iii) Quality indices 34 are derived from quality measures 26 and stationary assessment stage 28 to represent the quality of the variability measures for a window, as a single number.
iv) Quality indices can comprise of measure of the signal that are within the range [0,1] such as:
v) Quality indices 34 can comprise a measure of the signal that are not necessarily in the range [0,1] such as:
vi) The window-based quality indices may be combined using equal or unequal weights to produce a value between [0,1]. For example, the use of weights, {A,B,C,D,E,F}, where the sum of the weights is 1 can be combined with quality indices 34 {a,b,c,d,e,f} in the following manner:
Q=a*A+b*B+c*C+d*D+e*E+f*F
vii) Qv may be obtained in the same manner as Q, using quality indices relevant to the variability measures (i.e. intrinsic and extrinsic quality indices 34).
viii) Qv may comprise of an array of values, representing one Qv for each individual quality measure.
ix) The indices Q and Qv represents a summative value of the quality for a window.
i) The transformation of Q to a quality index 34 QI comprises thresholding or the use of machine learning models to create distinct quality levels. The quality index 34 can comprise any number of categories, for example “Low”, “Moderate” and “High”.
ii) The QI 34 provides an ‘at a glance’ summary of the quality of the waveform, event time series and stationary of a window.
iii) The quality system for variability analysis comprises the integration of the waveform, events and quality index 34 with the variability measurements for viewing waveform and for the continuous monitoring of variability.
i) QI and Qv form a per-window quality report 30 which may be displayed by clicking on a point in the variability graph display.
ii) The quality index QI 34 and Qv 26 are displayed (either numerically, or as a waveform), and used to transform the displayed variability. Instances of “Low” quality for a window or time period, removes the display of the variability for that window, to ensure inappropriate measurements are interpreted appropriately. When Qv is applied to individual variability measures, low values of (numerically or as a waveform), Qv remove the display for the offending windows, so as to not display measures for which the interpretation could be problematic.
iii) The quality report 30 (quality indices and quality index) per window serve as alerts that link to using a time stamp, to the waveform, the event time series, and the variability.
iv) The quality report 30 can be used to flag problems in data and has other uses, such as linking to the waveform where the events are delineated and their quality (non-physiological, physiological or physiological and high quality is indicated).
v) Providing quality of results provides benefits over viewing the variability to aid in interpretation of analysis and indicator of data quality in raw, event, high quality event and window.
vi) The variability is displayed at the same time as the waveform and event time series, either numerically or graphically.
vii) Variability results displayed numerically and graphically, and low quality for QI or Qv removes display for those widows. This assures clinicians that displayed information is at least of intermediate quality.
viii) The quality report, derived from the quality assessment is linked, through a time stamp, to the waveform, event and variability information. If one variability measurement is selected on a display, the quality report shown for that window can be used to call the waveform and event time series for that window.
ix) The physiological and event filtering stages are used to annotate each event in that time series as one of the three categories mentioned above, allowing the clinicians to inspect the waveform and event annotations.
x) The number of quality levels and threshold values on the quality measure to create is modular and can be changed for specific applications. For example, different stationarity requirements could be enforced for certain input waveforms types.
xi) Input feedback 19 can be provided by the user in order to enhance the quality assessment.
As an example, the quality framework can be applied to capnography data. A monitor that can collect the capnography signal is the Philips Intellivue which records the end tidal CO2 signal in millimeters of mercury (mmHg) at a sampling frequency of 125 Hz.
The waveform quality is measured by an analysis for disconnections and saturation events of duration ≧1 s. The sampling rate of the signal can be an input to the system or derived from the signal timestamps. Disconnections may be found by looking for consecutive values outside the monitor range. These ranges may be monitor specific, and are generally outside normal physiological values.
The waveform is segmented into events with a fixed threshold at 10 mmHg. Crossings preceded by a smaller value than the threshold level are considered to be indicative of expiration, otherwise they are inspirations. The distance between two consecutive events is the interbreath interval. The expiration or inspiration time may be used to determine the interval, as can any other signal feature such as time of peak pressure.
Each event's duration and maximal amplitude are measured. Events were classified as physiological or non-physiological using duration and amplitude measurements (between 1 and 15 seconds and equal or above 20 mmHg at the highest point of the event) and non-physiological events (outside duration range or below 20 mmHg at the highest point of the event).
In the absence of a gold standard for the identification of high quality breaths, an expert analysis was used to determine normal versus abnormal events. Six experts annotated over 10 000 individual breaths. For each of these breaths, 15 parameters were measured and used as input for a single class classifier which is then applicable to new capnogram signals in a reproducible way. The events classified as normal by the classifier are labeled ‘high quality’, because in addition to meeting physiological requirements, their morphology is classified as normal. Confidence about the quality of the event is increased.
Variability metrics appropriate to the calculation of reparatory parameter variability were selected. Variability may be calculated on the event time series of event duration, or other parameters measures from the waveform such as are under the curve. Parameters for the variability metrics were selected appropriately for this time series, as was the window length over which the variability were calculated.
The linear trend, spike and step model were applied to 2000 windows of high quality event time series which produced the histograms seen in
Three quality measures 26 were used to form a composite quality measure. They are 1) percentage time in normal-to normal event intervals (% tNN), 2) percentage time high quality (% tHQ) and 3) percentage time uninterrupted (% t uniterrup) were used to create the quality index. Other measures such as percentage time in non-physiological intervals (% t non-phys.) and percentage time of disconnection or saturation in the waveform (% t disc/sat) are calculated, reported in the quality report but not used in the quality index calculation.
It is highly desirable to provide a quality assessment of the waveform and quality measures 26 to provide a confidence interval about the resulting measures (output variability matrix), along with the variability measures to ensure the correct interpretation of the measure. The presented framework assesses quality at the waveform, event, and variability measurement level, in addition to assessing stationarity. Together, these components form a complete quality assessment for the purpose of variability analysis.
The quality index may then be computed at 212 using the quality measures and the results of the stationarity assessment 210 as explained above. The quality index 212, the waveforms themselves, and the variability data output from the variability calculations may then be displayed for the user at 216.
It will be appreciated that any module or component exemplified herein that executes instructions may include or otherwise have access to computer readable media such as storage media, computer storage media, or data storage devices (removable and/or non-removable) such as, for example, magnetic disks, optical disks, or tape. Computer storage media may include volatile and non-volatile, removable and non-removable media implemented in any method or technology for storage of information, such as computer readable instructions, data structures, program modules, or other data. Examples of computer storage media include RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by an application, module, or both. Any such computer storage media may be part of the system, any component of or related to the system, etc., or accessible or connectable thereto. Any application or module herein described may be implemented using computer readable/executable instructions that may be stored or otherwise held by such computer readable media.
The steps or operations in the flow charts and diagrams described herein are just for example. There may be many variations to these steps or operations without departing from the principles discussed above. For instance, the steps may be performed in a differing order, or steps may be added, deleted, or modified.
Although the above principles have been described with reference to certain specific examples, various modifications thereof will be apparent to those skilled in the art as outlined in the appended claims.
This application is a continuation of PCT Application No. PCT/CA2013/050681 filed on Sep. 5, 2013, which claims priority to U.S. Provisional Application No. 61/697,075 filed on Sep. 5, 2012, both incorporated herein by reference.
| Number | Date | Country | |
|---|---|---|---|
| 61697075 | Sep 2012 | US |
| Number | Date | Country | |
|---|---|---|---|
| Parent | PCT/CA2013/050681 | Sep 2013 | US |
| Child | 14632679 | US |
