WO2018152635A1 - Systems and methods of automatic cough identification - Google Patents
Systems and methods of automatic cough identification Download PDFInfo
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- WO2018152635A1 WO2018152635A1 PCT/CA2018/050203 CA2018050203W WO2018152635A1 WO 2018152635 A1 WO2018152635 A1 WO 2018152635A1 CA 2018050203 W CA2018050203 W CA 2018050203W WO 2018152635 A1 WO2018152635 A1 WO 2018152635A1
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/42—Detecting, measuring or recording for evaluating the gastrointestinal, the endocrine or the exocrine systems
- A61B5/4205—Evaluating swallowing
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- A61B5/0002—Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network
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- A61B5/08—Detecting, measuring or recording devices for evaluating the respiratory organs
- A61B5/0823—Detecting or evaluating cough events
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- A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
- A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
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- A61B5/1107—Measuring contraction of parts of the body, e.g. organ, muscle
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- A61B5/6801—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be attached to or worn on the body surface
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- G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
Definitions
- the present disclosure generally relates to identifying a cough. More specifically, the present disclosure relates to an automatic cough detection and monitoring system that discriminates cough accelerometry signals from other artifacts such as rest state, swallowing, head movements, and speech.
- a cough is a protective mechanical response in which rapid contractions of the thoracic cavity generate a forceful and rapid expulsion of air that clears the airway of foreign material, fluid or mucus.
- Cough can be symptomatic of various respiratory conditions such as asthma, rhinitis and gastro-oesophageal reflux disease in adults and protracted bronchitis in children.
- Cough is also a normal reflexive response to aspiration, which is the entry of foreign material into the airway seen in people with swallowing difficulties.
- knowledge of cough severity including intensity and frequency, may inform clinical decision-making in terms of appropriate treatment of the underlying issue.
- clinical assessments of cough often involve subjective judgment of symptoms and symptom severity, leading to inconsistent symptom reports between patients and caregivers.
- Cough scores, diaries, symptom questionnaires and visual analogue scales generally lack validation as tools for evaluating cough severity.
- the present inventors recognized that multi-transducer approaches have produced promising results but nevertheless require careful sensor positioning and attachment. Further, most of these approaches still retain a microphone, precluding their use in noisy environments.
- the present inventors recognized that an alternative approach may be to exclusively deploy a sensor, such as an accelerometer, that is insensitive to ambient acoustic noise.
- a sensor such as an accelerometer
- disclosed herein are embodiments of a framework for detection of cough and non-cough events, preferably using dual-axis accelerometry signals from a single accelerometer on the patient's neck.
- the present disclosure provides a method of identifying a cough.
- the method comprises: receiving, on a processing module, dual-axis accelerometry signals obtained by a sensor positioned externally on an anterior-posterior (A-P) axis and a superior-inferior axis (S-l) of the throat of a subject; representing segments of the dual-axis accelerometry signals as meta-features comprising salient meta-features, the processing module performs the representing of the segments; and classifying the segments as one of a plurality of classifications comprising at least one classification that is a cough and at least one classification that is a rest state, the processing module performs the classifying based on the salient meta-features.
- A-P anterior-posterior
- S-l superior-inferior axis
- At least one of the salient meta-features for each of the A-P axis and the S-l axis is selected from the group consisting of time domain characteristics of the accelerometry signals, information theoretic domain characteristics of the accelerometry signals, frequency domain characteristics of the accelerometry signals, and time-frequency domain characteristics of the accelerometry signals.
- at least one of the salient meta-features is selected from the group consisting of mean S-l, Lempel-Ziv complexity S-l, maximum energy A-P, variance A-P, and skewness A-P.
- the classifying of the segments comprises applying at least one of an artificial neural network (ANN) or a support vector machine (SVM) to the salient meta-features.
- ANN artificial neural network
- SVM support vector machine
- the plurality of classifications comprises an additional classification that is at least one non-cough artifact selected from the group consisting of a swallow, a tongue movement, and speech.
- the at least one non-cough artifact preferably comprises a swallow.
- the senor is a single dual-axis accelerometer, and the method is performed without using a microphone, a video recorder, or another accelerometer.
- the method comprises pre-processing of the dual-axis accelerometry signals before the representing of the segments of the dual-axis accelerometry signals as the meta-features, the pre-processing comprising at least one step selected from the group consisting of de-noising, head movement suppression, and high frequency noise filtering by wavelet packet decomposition.
- the plurality of classifications comprise at least one classification that is a voluntary cough and at least one classification that is an involuntary cough, and the method comprises discriminating between voluntary cough and involuntary cough.
- the present disclosure provides an apparatus for identifying a cough.
- the apparatus comprises: a sensor configured to be positioned on the throat of a patient and acquire vibrational data for an anterior-posterior axis and a superior-inferior axis; and a processing module operatively connected to the sensor and configured to represent segments of the dual-axis accelerometry signals as meta-features comprising salient meta-features used by the processing module to classify the segments as one of a plurality of classifications comprising at least one classification that is a cough and at least one classification that is a rest state.
- the apparatus comprises an output component selected from a display, a speaker, and a combination thereof, the processing module configured to use the output component to indicate the classification of the segments visually and/or audibly.
- the processing module is operatively connected to the sensor by at least one of a wired connection or a wireless connection.
- the present disclosure provides a method of diagnosing the presence or absence of coughing in a patient.
- the method comprises: positioning a sensor externally on the throat of the patient, the sensor acquiring vibrational data for at least one axis selected from the group consisting of an anterior-posterior axis and a superior-inferior axis, the sensor operatively connected to a processing module configured to represent segments of the dual-axis accelerometry signals as meta-features comprising salient meta-features used by the processing module to classify the segments as one of a plurality of classifications comprising at least one classification that is a cough and at least one classification that is a rest state; and treating the patient based on the classification of the segments.
- the method comprises determining a cough frequency based at least partially on the classification of the segments, and the treating of the patient is based at least partially on comparison of the cough frequency to a threshold.
- the patient is being evaluated for at least one medical condition selected from the group consisting of asthma, rhinitis, gastro-oesophageal reflux disease, bronchitis, and dysphagia.
- the present disclosure provides a method of diagnosing or treating dysphagia in a patient.
- the method comprises: positioning a sensor externally on the throat of the patient, the sensor acquiring vibrational data for at least one axis selected from the group consisting of an anterior-posterior axis and a superior-inferior axis, the sensor operatively connected to a processing module configured to represent segments of the dual-axis accelerometry signals as meta-features comprising salient meta-features used by the processing module to classify the segments as one of a plurality of classifications comprising at least one classification that is a cough and at least one classification that is a swallow.
- the patient has dysphagia
- the method further comprises adjusting treatment of the patient based at least partially on the classification of the segments.
- the adjusting of the treatment can comprise adjusting a feeding administered to the patient, and the adjusting of the feeding is selected from the group consisting of: changing a consistency of the feeding, changing a type of food in the feeding, changing a size of a portion of the feeding administered to the patient, changing a frequency at which portions of the feeding are administered to the patient, and combinations thereof.
- the present disclosure provides an apparatus for diagnosing or treating dysphagia.
- the apparatus comprises: a sensor configured to be positioned on the throat of a patient and acquire vibrational data for an anterior-posterior axis and a superior-inferior axis; and a processing module operatively connected to the sensor and configured to represent segments of the dual-axis accelerometry signals as meta-features comprising salient meta-features used by the processing module to classify the segments as one of a plurality of classifications comprising at least one classification that is a cough and at least one classification that is a swallow.
- An advantage of one or more embodiments provided by the present disclosure is a fully automated, accurate cough monitoring system.
- Another advantage of one or more embodiments provided by the present disclosure is to overcome drawbacks of known techniques for cough detection.
- a further advantage of one or more embodiments provided by the present disclosure is to reject ambient noise, accommodate variation in the characteristics of coughs across individuals and conditions, and provide the capability to monitor the patient over a long period of time, especially during the night when self-reporting is not feasible.
- Yet another advantage of one or more embodiments provided by the present disclosure is to consider both voluntary and reflexive coughs.
- Another advantage of one or more embodiments provided by the present disclosure is a cough detection method that requires only a single accelerometer, in contrast to current cough monitoring systems which require combinations of microphones, accelerometers, and video recorders.
- a further advantage of one or more embodiments provided by the present disclosure is to detect coughs without involving subjective judgment.
- Yet another advantage of one or more embodiments provided by the present disclosure is a cough detection system that can be used in any patient population, including healthy individuals.
- Another advantage of one or more embodiments provided by the present disclosure is a cough detection system having operation that is not affected by ambient noise, therefore suitable for day-to-day monitoring in noisy environments, and having simplicity by using only a single accelerometer, and thus the system is usable in a variety of applications such as cough frequency monitoring during sleep studies and veterinary medicine applications.
- FIG. 1 is diagram showing the location and orientation of a dual-axis accelerometer sensor on a human's neck.
- FIG. 2 is a schematic diagram of an embodiment of a cough detection device in operation.
- FIG. 3 is a flowchart of an embodiment of a method according to the present disclosure.
- FIG. 4 shows graphs of A-P and S-l signals containing three swallows (dotted black rectangles) and one involuntary cough (solid red rectangles) in Example 1 disclosed herein.
- FIGS. 5A-5D are graphs comparing voluntary cough vs. artifact accuracy between pairs of classifiers and feature reduction algorithms from Example 1 disclosed herein (elastic net does not converge for feature sizes less than four and hence the incomplete trend for some pairs).
- FIG. 6 is the Wilcoxon ranksum p-value heat-map for voluntary cough vs. non-cough artifacts (N/A: Not Applicable and N/S: Not Significant) from Example 1 disclosed herein.
- FIG. 7 are graphs showing a participant's voluntary coughs (solid red) and swallows (dotted black), with A-P and S-l signals in the top panels, and 2D trajectories for swallowing (lower left panel) and cough (lower right panel) after smoothing in Example 1 disclosed herein.
- FIGS. 8A-8D are graphs comparing involuntary cough vs. artifact accuracy between pairs of classifiers and feature reduction algorithms from Example 1 disclosed herein (elastic net does not converge for feature sizes less than four and hence the incomplete trend for some pairs).
- FIG. 9 are graphs showing a participant's involuntary coughs (solid red) and swallows (dotted black), with A-P and S-l signals in the top panels, and 2D trajectories for swallowing (lower left panel) and cough (lower right panel) after smoothing in Example 1 disclosed herein.
- FIG. 10 is a table showing the number of participants and boluses (thin consistency) in the study set forth in Example 2 disclosed herein.
- FIG. 11 are graphs showing noise-floor annotated A-P and S-l signals of a bolus in the study set forth in Example 2 disclosed herein.
- the signal portion that is above the noise-floor threshold is marked in light green.
- FIG. 12 is a graph showing scalar analysis over different object function penalty values ( ⁇ ) in the study set forth in Example 2 disclosed herein.
- the vertical line denotes the optimal value of a.
- FIG. 13 is a graph showing instance selection on the basis of proximity to the posterior classification probability threshold in the study set forth in Example 2 disclosed herein.
- FIG. 14 includes histograms of VFSS-determined and algorithmically estimated bolus lengths for different scalars (a) in the study set forth in Example 2 disclosed herein.
- FIG. 15 is a table of a comparison of the classification performance using the proposed instance selection approaches in the study set forth in Example 2 disclosed herein.
- FIG. 16 is a box plot of AUC values for classification with instance selection by posterior probability bands for different removal caps in the study set forth in Example 2 disclosed herein.
- the x-axis labels indicate the removal cap as a % of the test set.
- the actual number of test cases removed follows in parentheses.
- the actual width of the probability margin ( ⁇ ) is shown above the box plots.
- FIG. 17 is a graph of PCA components of selected (red) and non-selected
- FIG. 18 is a graph of parallel features of selected and non-selected instances in the study set forth in Example 2 disclosed herein.
- the devices disclosed herein may lack any element that is not specifically disclosed.
- a disclosure of an embodiment using the term “comprising” includes a disclosure of embodiments “consisting essentially of” and “consisting of” the components identified.
- the methods disclosed herein may lack any step that is not specifically disclosed herein.
- a disclosure of an embodiment using the term “comprising” includes a disclosure of embodiments “consisting essentially of” and “consisting of” the steps identified.
- the term "individual,” “subject” or “patient” means any animal, including humans, that could experience coughing. Indeed, every mammalian species studied to date displays a cough reflex or some similar forceful expiratory reflex evoked by airway irritation. Generally, the individual is a human or an avian, bovine, canine, equine, feline, hircine, lupine, murine, ovine or porcine animal.
- a "companion animal” is any domesticated animal, and includes, without limitation, cats, dogs, rabbits, guinea pigs, ferrets, hamsters, mice, gerbils, horses, cows, goats, sheep, donkeys, pigs, and the like.
- the patient is a mammal, such as a human or a companion animal, e.g., a dog or cat.
- compositions mean a product or composition that is intended for ingestion by an individual such as a human and provides at least one nutrient to the individual. These terms include beverages.
- compositions of the present disclosure can comprise, consist of, or consist essentially of the elements disclosed herein, as well as any additional or optional ingredients, components, or elements described herein or otherwise useful in a diet.
- a "bolus” is a single sip or mouthful of a food.
- Prevention includes reduction of risk and/or severity of a condition or disorder.
- treatment include both prophylactic or preventive treatment (that prevent and/or slow the development of a targeted pathologic condition or disorder) and curative, therapeutic or disease-modifying treatment, including therapeutic measures that cure, slow down, lessen symptoms of, and/or halt progression of a diagnosed pathologic condition or disorder, and include treatment of patients at risk of contracting a disease or suspected to have contracted a disease, as well as patients who are ill or have been diagnosed as suffering from a disease or medical condition.
- the term does not necessarily imply that a subject is treated until total recovery.
- These terms also refer to the maintenance and/or promotion of health in an individual not suffering from a disease but who may be susceptible to the development of an unhealthy condition.
- treatment is also intended to include the potentiation or otherwise enhancement of one or more primary prophylactic or therapeutic measure.
- treatment also intended to include the potentiation or otherwise enhancement of one or more primary prophylactic or therapeutic measure.
- treatment also intended to include the potentiation or otherwise enhancement of one or more primary prophylactic or therapeutic measure.
- treatment also intended to include the potentiation or otherwise enhancement of one or more primary prophylactic or therapeutic measure.
- treatment also intended to include the potentiation or otherwise enhancement of one or more primary prophylactic or therapeutic measure.
- treatment can be patient- or doctor-related.
- Cervical accelerometry is a non-invasive and non-radiographic assessment technique where the patient wears a dual-axis accelerometer midline, below the laryngeal prominence (commonly known as the Adam's apple).
- the accelerometer captures epidermal vibrations in the anterior-posterior (AP) and superior-inferior (SI) directions, thus facilitating day-to-day monitoring of pharyngeal vibrations.
- An aspect of the present disclosure is an algorithmic approach to accurately differentiate coughs from a resting state, swallowing, head movements and speech on the basis of dual-axis accelerometry signals.
- An aspect of the present disclosure is a method of processing dual-axis accelerometry signals to classify one or more of the signals as a cough or a non-cough (e.g., a rest state, a swallow, a tongue movement, or speech).
- a non-cough e.g., a rest state, a swallow, a tongue movement, or speech.
- Another aspect of the present disclosure is a device that implements one or more steps of the method.
- the method can further comprise diagnosing and/or treating the patient based on the classification of each of the dual-axis accelerometry signals (e.g., determining a clinical assessment of the patient). For example, a patient can be diagnosed as having a medical condition such as asthma, rhinitis, gastro-oesophageal reflux disease, bronchitis and/or dysphagia if the frequency of the coughs exceeds a threshold. Treatment of the patient can be adjusted based at least partially on the classification of each of the dual-axis accelerometry signals.
- a medical condition such as asthma, rhinitis, gastro-oesophageal reflux disease, bronchitis and/or dysphagia if the frequency of the coughs exceeds a threshold.
- Treatment of the patient can be adjusted based at least partially on the classification of each of the dual-axis accelerometry signals.
- the method and the device can be employed in the apparatuses and/or the methods disclosed in U.S. Patent No. 7,749,177 to Chau et al., the methods and/or the systems disclosed in U.S. Patent No. 8,267,875 to Chau et al., the systems and/or the methods disclosed in U.S. Patent No. 9, 138, 171 to Chau et al., or the methods and/or the devices disclosed in U.S. Patent App. Publ. No. 2014/0228714 to Chau et al., each of which is incorporated herein by reference in its entirety.
- the device may include a sensor configured to produce cervical accelerometry signals, preferably a dual axis accelerometer.
- the sensor may be positioned externally on the neck of a human, preferably anterior to the cricoid cartilage of the neck.
- a variety of means may be applied to position the sensor and to hold the sensor in such position, for example double-sided tape.
- the positioning of the sensor is such that the axes of acceleration are aligned to the anterior-posterior and super-inferior directions, as shown in FIG. 1.
- FIG. 2 generally illustrates a non-limiting example of a device 100 for use in cough detection.
- the device 100 can comprise a sensor 102 (e.g., a dual axis accelerometer) to be attached in a throat area of a candidate for acquiring dual axis accelerometry data and/or signals, for example illustrative S-l acceleration signal 104.
- Accelerometry data may include, but is not limited to, throat vibration signals acquired along the anterior-posterior axis (A-P) and/or the superior-inferior axis (S-l).
- the sensor 102 can be any accelerometer known to one of skill in this art, for example a single axis accelerometer (which can be rotated on the patient to obtain dual-axis vibrational data) such as an EMT 25-C single axis accelerometer or a dual axis accelerometer such as an ADXL322 or ADXL327 dual axis accelerometer, and the present disclosure is not limited to a specific embodiment of the sensor 102.
- a single axis accelerometer which can be rotated on the patient to obtain dual-axis vibrational data
- EMT 25-C single axis accelerometer or a dual axis accelerometer such as an ADXL322 or ADXL327 dual axis accelerometer
- ADXL322 or ADXL327 dual axis accelerometer an ADXL322 or ADXL327 dual axis accelerometer
- the sensor 102 can be operatively coupled to a processing module 106 configured to process the acquired data for cough detection, for example discrimination between cough and non-cough events such as a rest state, a swallow, a tongue movement, and speech.
- the processing module 106 can be a distinctly implemented device operatively coupled to the sensor 102 for communication of data thereto, for example, by one or more data communication media such as wires, cables, optical fibers, and the like and/or by one or more wireless data transfer protocols.
- the processing module 106 may be implemented integrally with the sensor 102.
- the processing of the dual-axis accelerometry signals comprises representation of the signal segments in meta-features and then classification of each segment based on the meta-features.
- the classification is automatic such that no user input is needed for the dual-axis accelerometry signals to be processed and used for classification of the signal.
- dual-axis accelerometry data for both the S-l axis and the A-P axis is acquired or provided, for example dual-axis accelerometry data from the sensor 102.
- the dual-axis accelerometry data for both the S-l axis and the A-P axis can be acquired or provided a time period that is at least 10 minutes, preferably at least 30 minutes, more preferably at least 45 minutes, most preferably at least one hour, and in some embodiments at least two, three or four hours).
- the method is performed without using a microphone, a video recorder, or another accelerometer, i.e., the dual-axis accelerometry data is acquired without using a microphone, a video recorder, or another accelerometer during the time period.
- the dual-axis accelerometry data can optionally be processed to condition the accelerometry data and thus facilitate further processing thereof.
- the dual-axis accelerometry data may be filtered, denoised, and/or processed for signal artifact removal ("preprocessed data").
- the dual-axis accelerometry data is subjected to one or more of de-noising, head movement suppression, or high frequency noise filtering (e.g., wavelet packet decomposition).
- the accelerometry data can then be automatically or manually segmented into distinct events.
- the accelerometry data is automatically segmented.
- the segmentation is automatic and energy-based.
- the accelerometry data is automatically segmented as disclosed in U.S. Patent No. 8,267,875 to Chau et al., the entirety of which is incorporated herein by reference as noted above.
- the automatic segmentation can comprise applying fuzzy c-means optimization to the data determine the time boundaries for each of the cough and non-cough segments.
- manual segmentation may be applied, for example by visual inspection of the data.
- meta-feature based representation of the signals is performed.
- one or more time features, frequency features, time-frequency features, or information-theoretic features for each segment i.e., cough, speech, swallow, tongue movement, rest
- suitable time domain features include: mean, mean absolute deviation, median, variance, skewness, kurtosis, and memory.
- suitable information-theoretic domain features include entropy and entropy rate.
- suitable frequency domain features include peak frequency, bandwidth, Lempel-Ziv complexity, and centroid frequency.
- suitable time-frequency domain features include maximum energy, wave energy, and discrete wavelet transform (DWT) coefficients.
- the meta-feature representation of the dual-axis accelerometry signals can then be used as the input along with respective labels in subsequent feature-selection and/or classification.
- a subset of the meta-features may be selected as salient meta-features for classification, preferably predetermined salient meta-features identified by analysis of previous data.
- the meta-features preferably comprise at least one of (i) mean S-l, (ii) Lempel-Ziv complexity S-l, (iii) maximum energy A-P, (iv) variance A-P, and (v) skewness A-P.
- the meta-features can be any number of these features (i)-(v), for example one, two, three, four or even all five of these features, and optionally with one or more of the other features.
- the salient meta-features can be used to classify segments of the dual-axis accelerometry signals (e.g., each of the segments not removed by pre-processing) as a cough or a rest state and/or as a cough or a non-cough (i.e., rest state, swallow, tongue movement, or speech).
- a non-cough i.e., rest state, swallow, tongue movement, or speech.
- an artificial neural network (ANN) and/or a support vector machine (SVM) is applied as a classification algorithm to the salient meta-features of the segment to classify the segment.
- the classification can be used to output for a user of the device 100, such as a clinician or a patient.
- the processing module 106 and/or a device associated with the processing module 106 can comprise a display that identifies the classification using images such as text, icons, colors, lights turned on and off, and the like.
- the processing module 106 and/or a device associated with the processing module 106 can comprise a speaker that identifies classification using auditory signals.
- the present disclosure is not limited to a specific embodiment of the output, and the output can be any means by which the classification of the segment is identified to a user of the device 100.
- the output may then be utilized in screening/diagnosing the tested candidate and providing appropriate treatment, further testing, and/or proposed dietary or other related restrictions thereto until further assessment and/or treatment may be applied.
- adjustments to feedings can be based on changing consistency or type of food and/or the size and/or frequency of mouthfuls being offered to the patient.
- the method can optionally comprise a validation subroutine in which a data set representative is processed such that each data set ultimately experiences the preprocessing, feature extraction and classification disclosed herein. After all events have been classified and validated, output criteria may be generated for future classification without necessarily applying further validation to the classification criteria.
- routine validation may be implemented to either refine the statistical significance of classification criteria, or again as a measure to accommodate specific equipment and/or protocol changes (e.g. recalibration of specific equipment, for example, upon replacing accelerometer with same or different accelerometer type/model, changing operating conditions, new processing modules such as further preprocessing subroutines, artifact removal, additional feature extraction/reduction, etc.).
- Another aspect of the present disclosure is a method of treating dysphagia.
- the method of treating dysphagia comprises using any embodiment of the device 100 disclosed herein and/or performing any embodiment of the method disclosed herein.
- the method can further comprise adjusting a feeding administered to the patient based on the classification, for example by changing a consistency of the feeding, changing a type of food in the feeding, changing a size of a portion of the feeding administered to the patient, changing a frequency at which portions of the feeding are administered to the patient, or combinations thereof.
- the dysphagia is oral pharyngeal dysphagia associated with a condition selected from the group consisting of cancer, cancer chemotherapy, cancer radiotherapy, surgery for oral cancer, surgery for throat cancer, a stroke, a brain injury, a progressive neuromuscular disease, neurodegenerative diseases, an elderly age of the patient, and combinations thereof.
- an "elderly" human is a person with a chronological age of 65 years or older.
- the method and the devices disclosed herein can use instance selection and/or noise-floor bolus length estimation, for example in methods disclosed by U.S. Patent No. 9,687,191 entitled “Method and Device for Swallowing Impairment Detection," incorporated herein by reference in its entirety.
- instance selection and/or noise-floor bolus length estimation can be employed in a method of classifying the vibrational data (e.g., dual-axis accelerometry data) as indicative of one of normal swallowing and possibly impaired swallowing, preferably by classifying one or more swallowing events as indicative of a safe event or an unsafe event.
- vibrational data e.g., dual-axis accelerometry data
- Non-limiting examples of instance selection and noise-floor bolus length estimation are set forth in Example 2 later herein.
- Instance selection refers to a family of methods in machine learning that aims to reduce the volume of a given data set to accelerate the training and testing processes while maintaining or surpassing the classification accuracies obtained with the full data set.
- instance selection algorithms extract a subset of instances from data sets that are suspected of containing ambiguous, superfluous, or noisy data points. The intent is that the extracted subset optimizes classification performance.
- Ambiguous data points are the instances with classification posteriors close to the classification threshold, while superfluous data points bring no additional value to classification and noisy data points lead to false classification predictions.
- the choice of instance selection algorithms is problem-specific and no one algorithm is superior over others in all contexts.
- Instance selection algorithms can be categorized according to the process of deriving the data subset (i.e. incremental, decremental, batch, mixed, and fixed), the type of discarded instances (i.e., boundary, central, or both), and the selection criterion (i.e., classification performance or feature values). Based on the process of deriving the data subset, instance selection algorithms can be organized into five categories:
- Incremental Instance selection begins with an empty subset and incrementally adds data points by analyzing the instances in the training set.
- Decremental The decremental algorithm begins with the entire training set and removes data points that are suspected of being unnecessary or superfluous; these data points meet the predefined selection criterion.
- Batch The batch instance selection algorithm does not remove instances until all data points have been analyzed. Instances that meet the selection criterion are marked but not removed until all data points have been considered, at which time, all the marked instances are discarded.
- Mixed instance selection starts with a preselected subset of data points and either adds instances to or removes instances from this subset.
- Fixed instance selection algorithms constitute a subfamily of the mixed algorithm, where a predetermined subset size is maintained while adding instances to or removing instances from the subset.
- Instance selection algorithms can also be classified according to the type of discarded data, namely, points from the decision boundary, "central" points within the boundaries, or combinations thereof:
- Hybrid instance selection algorithms combine condensation and edition approaches to select both boundary and central instances to maintain or improve classification accuracies.
- instance selection algorithms can be understood in terms of their selection criterion.
- Wrapper algorithms embed instance selection in the process of classifier evaluation. Generally, instances with negligible contribution to model prediction are discarded from the training set. The majority of the wrapper algorithms are based on some measure of misclassification of the instances.
- filter-based instance selection rejects instances based on a selection criterion which is independent of the training algorithm but usually relating to the feature values of the instances. The filter approaches either find representative instances from different subspaces of the data set, or base selection on the similarities between pairs of instances.
- the instance selection preferably comprises a wrapper approach in which the classification posterior threshold is deployed in a selection criterion, and an instance is selected for removal if the corresponding classification posterior falls within the vicinity of the tuned threshold.
- this process comprises estimating the onset and offset of the bolus signals based on the noise-floor distribution of both the A-P and S-l channels. These processes can achieve improved bolus-level AUC.
- FIG. 3 generally illustrates a preferred embodiment of a method 200 that can be performed by the device 100 according to the present disclosure.
- the method 200 can comprise the device 100 performing one or more of a pre-processing step 202, a swallow-level analysis process 300, an automatic cough identification process 400 that can discriminate between cough and non-cough artefacts (e.g., for both instructed and reflexive coughs), a bolus length estimation process 500, and a classification process 600.
- the classification process 600 can use bolus-level features from the bolus length estimation process 500 and/or swallow-level features from the swallow-level analysis process 300 to provide high sensitivity and specificity discrimination between safe and unsafe swallowing (e.g., in a patient with dysphagia).
- the pre-processing step 202 can comprise the device 100 performing one or more of de-noising (e.g., 10-level wavelet decomposition with Daubechies-8 mother wavelets), head movement removal (e.g., B-spline approximation of low frequency ( ⁇ 5 Hz) signal components), speech removal (e.g., eliminating signal segments with periodic behaviors as detected by pitch tracking), or suppression of high frequency noise (e.g., wavelet packet decomposition with a 4-level discrete Meyer wavelet and Shannon entropy).
- de-noising e.g., 10-level wavelet decomposition with Daubechies-8 mother wavelets
- head movement removal e.g., B-spline approximation of low frequency ( ⁇ 5 Hz) signal components
- speech removal e.g., eliminating signal segments with periodic behaviors as detected by pitch tracking
- suppression of high frequency noise e.g., wavelet packet decomposition with a 4-level discrete Meyer wavelet and Shannon entropy
- the swallow-level analysis process 300 is performed by the device 100 as disclosed in WO 2017/137844 entitled "Signal Trimming and False Positive Reduction of Post-Segmentation Swallowing Accelerometry Data" to Mohammadi et al., the entirety of which is incorporated by reference.
- the swallow-level analysis process 300 can comprise one or more of automatic segmentation on the dual-axis accelerometry data at Step 302, false positive reduction at Step 304, logical combination at Step 306, swallow trimming at Step 308, or swallow signal characterization at Step 310.
- Automatic segmentation on the dual-axis accelerometry data at Step 302 can comprise applying a sequential fuzzy c-means algorithm to the segmented dual-axis accelerometry data.
- False positive reduction at Step 304 can use one or both of energy-based false positive reduction or noise floor-based false positive reduction.
- the energy and noise-floor false positive reduction methods can be applied in parallel on segmented, pre-processed data; and candidate segments identified as valid by at least one of the two false positive reduction methods can be admitted in the logical combination at Step 306.
- Swallow trimming at Step 308 can trim the data so that it includes only the portion of the signal corresponding to the physiological vibrations associated with swallowing while excluding the pre- and post-swallow signal fluctuations. For example, the location of the peak amplitude can be found, overlapping windows of size w can be shifted to the left and to the right of the peak by increments of size s, and the energy difference can be calculated within each window. Bilaterally, windowed segments with energy difference below the threshold can be removed from the candidate swallow segment. In an embodiment, this technique employs a kernel density estimation-based algorithm.
- Swallow signal characterization at Step 310 can comprise characterization of the dual-axis accelerometry data that has been subjected to segmentation, trimming and false positive reduction.
- the swallow signal characterization can determine a number of swallows, a duration of swallows, and/or a time of swallows and can identify swallow-level features that can then be subjected to the classification process 600.
- the automatic cough identification process 400 can comprise analysis of one or more data sets of dual-axis accelerometry signals by the device 100, and a non-limiting embodiment of the automatic cough identification process 400 is set forth in Example 1 later herein.
- an "instructed" data set can be provided at Step 402
- a "reflexive" data set can be provided at Step 404, and preferably at least one of these data sets comprises data from the pre-processing at Step 202.
- the data can optionally be pre-processed at Step 406.
- meta-features of the data can be represented, preferably from the A-P signals and the S-l signals separately.
- Non-limiting examples of such meta-features include temporal, time-frequency, frequency, information-theoretic features for each segment (i.e., cough, speech, swallow, rest) for one or both of the A-P data or the S-l data.
- information-theoretic features for each segment (i.e., cough, speech, swallow, rest) for one or both of the A-P data or the S-l data.
- one or more of mutual information, cross-entropy rate, and cross-correlation between the corresponding A-P and S-l signals can be calculated.
- these meta-features can be reduced to a set of salient meta-features, for example meta-features identified as salient by one or more of binary genetic algorithm (BGA), filter-based feature selection, elastic net, or principal component analysis (PCA).
- BGA binary genetic algorithm
- PCA principal component analysis
- salient meta-features include mean S-l, Lempel-Ziv S-l, maximum energy A-P, variance A-P, and skewness A-P.
- the device 100 can identify salient features from the information theoretic domain (e.g., entropy and entropy rate) and/or the combination of two axis (e.g., mutual information and cross-correlation).
- the device can use the salient meta-features to classify cough segments versus rest states and artefacts, for example by artificial neural network (ANN) or support vector machines (SVM).
- ANN artificial neural network
- SVM support vector machines
- the bolus length estimation process 500 and the classification process 600 can comprise analysis of dual-axis accelerometry signals by the device 100, for example data from the pre-processing at Step 202; and a non-limiting embodiment of the bolus length estimation process 500 and the classification process 600 is set forth in Example 2 later herein.
- the device 100 can perform bolus length estimation, preferably by noise-floor bolus length estimation to estimate the onset and offset of the bolus signals based on the noise-floor distribution of both the A-P and S-l channels.
- the device 100 can perform feature selection and extraction, such as by calculation of time, frequency, time-frequency, information theoretic domain features for both A-P and S-l axis and optionally channel combination features as well, and preferably at both bolus- and swallow-level.
- feature selection and extraction such as by calculation of time, frequency, time-frequency, information theoretic domain features for both A-P and S-l axis and optionally channel combination features as well, and preferably at both bolus- and swallow-level.
- These features can be provided at Step 602, and a reduced feature set can be identified at Step 604, for example by applying the elastic net as a regularized binary logistic regression used to select a subset of features.
- the device 100 can perform threshold tuning, for example by calculating a receiver operating characteristic (ROC) curve using the posteriors of a training data set.
- the device 100 can perform instance selection to identify and remove uncertain boluses from the dual-axis accelerometry signals.
- a preferred embodiment of the instance selection employs the classification probability threshold band, e.g., an instance is selected for removal if the corresponding classification posterior falls within the vicinity of the tuned threshold.
- the device 100 can perform classification to determine a safe or unsafe swallow, for example by applying a linear Discriminant Analysis (LDA) classifier, e.g., an LDA classifier evaluated over 1000 runs of a random hold-out cross-validation test.
- LDA linear Discriminant Analysis
- the device 100 preferably outputs the classification, e.g., by a visual output, such as text, lights, or icons, and/or by an audio output.
- the proposed framework that was tested included pre-processing to remove noise and head movements from the acceleration signals. Meta-feature-based representation of the pre-processed signals was then computed followed by feature selection/extraction to identify the most salient features. The salient features were then classified over ten runs of 5-fold cross-validation. The following sections elaborate upon the tested framework in detail.
- Pre-processing included de-noising (Sejdic et al., "A procedure for denoising dual-axis swallowing accelerometry signals," Physiol. Meas. 31 :N1-N9 ((2010)) and head movement suppression (Sejdic et al., "A method for removal of low frequency components associated with head movements from dual-axis swallowing accelerometry signals," PloS ONE 7(3) (2012) e33464; Sejdic et al., “The effects of head movement on dual-axis cervical accelerometry signals,” BMC Res. Notes.3:269 (2010)).
- Meta-feature-based representation of signals [00117] Time, frequency, information-theoretic features for each segment (i.e., cough, speech, swallow, rest) were computed from the A-P and S-l axes separately. For salient feature identification, three selection algorithms to determine parsimonious and discriminatory feature vectors were considered: BGA (binary genetic algorithm) (Mitchell, "An introduction to genetic algorithms," MIT press, 1998), elastic net (Friedman et al., "Regularization paths for generalized linear models via coordinate descent,” J. Stat. Softw.
- PCA principal component analysis
- candidate feature vectors were coded as a chromosome of Boolean values, each gene indicating whether the corresponding feature is selected.
- a population size of fifty was selected along with a tournament size of two. Optimization proceeded for a maximum of 100 generations. Crossover and mutation rates of 0.8 and 0.1 were selected respectively. Additionally, in order to keep the best solutions in the population pool, elitism of size two was selected. The entire optimization was iterated 30 times.
- the elastic net is a regularized binomial logistic regression which is used to select a subset of features.
- the elastic-net penalty of Zou & Hastie (2005) a set of 10 equally spaced ridge-l_ASSO penalty (a) values in the range of [0.1 , 1] and 100 values of the penalty parameter ⁇ were tested.
- a pair of a and ⁇ values yielding the minimum 5-fold cross-validated squared-error on the training data was selected using a generalized binomial logistic regression models toolbox (Qian et al., "Glmnet for matlab 2010").
- PCA principal component analysis
- ANN artificial neural networks
- SVM support vector machines
- Neural networks with a single hidden layer of twenty units and two output units were implemented. This configuration was selected empirically based on the training performance. The inputs were feature values from the reduced feature subsets described above. Networks were trained using Bayesian regularized back-propagation with a mean-squared error criterion function and evaluated via five-fold cross-validation with a 80-20 split into training and validation on the training folds.
- Feature selection and classifier pairings were evaluated using ten runs of five-fold cross-validation. The model performance was evaluated based on the meanistandard deviation of the pair's accuracy, as well as true positive and true negative rates over ten runs of five-fold cross-validation of the test cases. In each run, the data set was divided into five folds. Each fold was considered as the test set while the feature selection and classifier pair was trained using the remaining four folds and blind to the test cases. This process was repeated ten times.
- 'voluntary' and 'involuntary' cough data sets Two different data sets of dual-axis accelerometry signals (herein referred to as the 'voluntary' and 'involuntary' cough data sets) were used to validate the cough detection algorithm. Fifteen subjects participated in the voluntary cough data collection. Each participant attended two data collection sessions, each lasting approximately 45 minutes. The protocol was approved by the research ethics board of the participating hospital. Each participant provided written, informed consent.
- the first session consisted of only tongue motions (tongue protruding out of the mouth with lips pursed, tongue contacting the inside of the left and right cheeks separately, and tongue at rest).
- the second session comprised coughing, swallowing water, and saying "on” or "off out loud.
- the experimenter demonstrated the required tasks and provided participants with five minutes to practice the tasks.
- participants were cued to perform the tasks in a pseudo-random order through a LabVIEW interface. Participants were instructed to perform the task within the 4 seconds immediately following the presentation of each cue. Each task was repeated 20 times for every participant. In total, 300 examples of each task were obtained (15 participants x 20 examples of each task/participant).
- the experimenter noted when the participant performed the incorrect task.
- the data set thus included accelerometry signals pertaining to tongue movements, coughs, swallows and speech. All signals were trimmed automatically by identifying the one second segment with maximum energy within the 4 second recording. In particular, the trimmed signal was derived by centering a one second window around the location of the signal peak in the maximum energy segment.
- Involuntary reflexive coughs were derived from a previously reported dataset (Mohammadi et al., "Post-segmentation swallowing accelerometry signal trimming and false positive reduction,” IEEE Signal Processing Letters 23(9): 1221-1225 (2016), cited above).
- coughs are associated with swallowing activity, reflecting aspiration events, as opposed to coughs elicited in a cough reflex test (where an irritant like citric acid is infused through a nebulizer to observe the expected cough reflex response).
- Dual-axis accelerometry signals were collected from 196 consenting adults living with the effects of stroke or brain injury, or with otherwise unrelated suspicion of dysphagia. Each participant performed a series of 6 discrete sips of thin liquid barium (Bracco
- FIG. 4 exemplifies manually annotated coughs and swallows for a participant in the involuntary cough data set. This recording contained three swallows, outlined by the dotted black rectangles, and one cough event, indicated by the solid red rectangles.
- a more complex classification problem is to discriminate between cough segments and other non-cough artifacts (combination of swallow, speech, and head movement segments).
- the leading classification and feature selection pair for involuntary cough vs. non-cough artifacts was SVM and BGA with TPR, TRN, and accuracy of 80.9 ⁇ 15.8%, 79.8 ⁇ 18.6%, and 80.3 ⁇ 10.5%, respectively.
- FIGS. 5A-5D demonstrate the accuracy values of discriminating voluntary cough segments from non-cough artifacts. As shown in the bottom right plot, the error rate of the training and test data diverged after 15 features in the case of SVM. This divergence is attributed to over-fitting. For the ANN, as shown in FIG. 5A, the accuracy results saturate after eleven features.
- FIG. 6 is a heat-map of the p-values calculated using right-tailed Wilcoxon rank sum test for the optimal number of features of each pair for the voluntary data set.
- the right-tailed p-values examines whether the algorithm pairs on the y-axis has a greater median compared to the algorithm pairs on the x-axis.
- FIG. 6 shows that the leading pair is SVM and elastic net (p ⁇ 0:001).
- trajectories of cough segments appear to be qualitatively more complex than swallowing segments.
- FIGS. 8A-8D present the results of classifying involuntary coughs versus non-cough artifact segments, over different feature subsets using different feature selection/reduction and classification methods. Although elastic net demonstrated a more regular and steady performance over different subsets of features, the leading feature reduction and classification pair is SVM and BGA (p ⁇ 0:03).
- the unique salient features for the involuntary classification were selected from the information theoretic domain (e.g. entropy and entropy rate) and the combination of two axis (e.g. mutual information and cross-correlation), while the majority of salient features for the voluntary classification were from the time domain (e.g. memory and kurtosis).
- the information theoretic domain e.g. entropy and entropy rate
- two axis e.g. mutual information and cross-correlation
- the majority of salient features for the voluntary classification were from the time domain (e.g. memory and kurtosis).
- the entropy rate characterizes a stochastic process and measures the regularity of the signal and is used in deemed suitable for swallowing accelerometry analysis (Lee et al., "Effects of liquid stimuli on dual-axis swallowing accelerometry signals in a healthy population," Biomedical Engineering OnLine 9(1 ): 1 (2010), cited above). Entropy and mutual information measure the amount and redundancy of information within the signal, respectively. Additionally, appearance of cross-correlation among salient features shows that the correlation between the two A-P and S-l axis is more distinctive for involuntary signals compared to the voluntary tasks.
- the memory of a signal measures the temporal extent of the correlation of the neighboring data samples.
- the kurtosis of a signal measures the peakedness of the amplitude distribution (Lee et al., "Time and time-frequency characterization of dual-axis swallowing accelerometry signals," Physiol. Meas. 29(9):1 105 (2008), cited above). Selection of these features as top salient features for classification of voluntary signals shows that the time domain features are more distinctive when discriminating voluntary tasks compared to involuntary signals.
- SVM gave better performance compared to ANN in the majority of comparisons (Wilcoxon ranksum p ⁇ 0:05). This performance may be due to one of the advantages of SVM classifiers that they find the global minimum, while ANN classifiers may suffer from multiple local minimum solutions (Taylor, "Kernel methods for pattern analysis," Cambridge university press (2004)). On the other hand, SVM was trained faster than ANN, which makes SVM a more suitable candidate for online analysis and classifications.
- One of the advantages of the proposed system is its simplicity, deploying only a single accelerometer. Additionally, the proposed system is not affected by ambient noise, therefore suitable for day to day monitoring in noisy environments. Consequently, potential applications such as cough frequency monitoring during sleep studies and veterinary medicine applications may benefit from this algorithm.
- An automatic cough detection and monitoring system discriminated cough accelerometry signals from other artifacts such as rest state, swallowing, head movements, and speech. Both voluntary and involuntary coughs were considered.
- the proposed system discriminated between coughs and rest state with accuracies of 99.64% and 90% for voluntary and involuntary coughs, respectively. Additionally, the cough segments were discriminated from the non-cough artifacts with accuracy values of 90.2% and 80.3% for voluntary and involuntary data sets.
- A-P and S-l signals were de-noised using 10-level wavelet decomposition with Daubechies-8 mother wavelets. Signal artefacts relating to head movement were removed by subtracting a B-spline approximation of low frequency ( ⁇ 5 Hz) signal components while vocalizations were suppressed by eliminating signal segments with periodic behaviors, as detected by pitch tracking. Channel-specific normalization was applied to the bivariate bolus signals to scale the signals to [0, 1].
- A-P and S-l variance signals were computed by estimating the sample variance within windows of size 200 data points, shifted along each of the A-P and S-l signals with 50% overlap.
- the swallows were then segmented by subjecting the variance signals to a sequential fuzzy c-means algorithm.
- the aforementioned segmentation algorithm was too liberal, admitting pre- and post-swallowing activity while also giving rise to non-swallow segments or false positives.
- a kernel density estimation-based algorithm was used to adaptively trim the swallow segments, while energy and noise floor algorithms reduced the number of false positive swallow segments.
- Feature Selection and Extraction [00165] Time, frequency, time-frequency, information theoretic domain features for both A-P and S-l axis and channel combination features at both bolus- and swallow-level were calculated.
- the elastic net is a regularized binary logistic regression which is used to select a subset of features. It linearly combines the penalties of the LASSO (Least Absolute Shrinkage and Selection Operator) and ridge regularization methods.
- the majority of the existing studies are dependent on VFSS to demarcate the bolus onset and offset of the acquired acceleration signals.
- the existing systems are not completely automated and rely on an external point of reference to segment the signal portions of interest.
- the proposed noise-floor bolus length estimation reduces the level of VFSS-dependency of the acquired acceleration signals by adding a cushion of 5,000 samples before and after the VFSS annotated boluses and subsequently re-estimating the bolus boundaries. This is possible since the recordings of the accelerometer were continuous.
- the noise-floor algorithm then automatically estimates the bolus length to be as close as possible to the VFSS annotated onset and offsets.
- ⁇ and ⁇ ' 2 are the new estimated bolus onset and offset, respectively, and ⁇ and ⁇ 2 are the VFSS onset and offset respectively, expressed as a function of the threshold scalar a.
- the parameter 0 ⁇ ⁇ ⁇ 1 is used to tune the objective function. Larger values of ⁇ yield more liberal estimates of onsets and offsets, i.e., further away from VFSS values, whereas smaller values of ⁇ provides more conservative estimates.
- the optimal scalar is given by:
- FIG. 12 depicts the objective function values at different values of ⁇ .
- a ⁇ of 0.35 provided an objective function that yielded the lowest error (i.e., boluses closest in length to those annotated by VFSS) in the neighborhood of the optimal a value.
- An alternative, wrapper-based approach to instance selection is to deploy the classification posterior threshold in a selection criterion.
- a receiver operating characteristic (ROC) curve was calculated using the posteriors of the training data set where each point on this curve results defines a sensitivity and specificity pairing.
- class imbalance in this case, minority positive class
- the classification posterior threshold was tuned, using only the training set in each cross-validation run, to maximize sensitivity while maintaining 60% classification specificity.
- T is the tuned threshold
- ⁇ is the margin based on the instance removal cap
- C(.v ) ⁇ ' safe '
- unsafe " ⁇ is the bolus target class label.
- LDA Linear Discriminant Analysis
- ANN artificial neural network
- SVM support vector machine
- a kernel density estimate of the VFSS bolus lengths provided the null hypothesis cumulative distribution function against which each distribution of bolus lengths for a given a were tested using the Kolmogorov-Smirnoff goodness-of-fit test.
- FIG. 15 compares classification performance with and without the different instance selection algorithms after 1 ,000 runs of hold-out cross-validation. As shown, a maximum AUC of 83.6% was achieved for the discrimination of safe and unsafe boluses of thin consistency. There is an improvement in AUC (p ⁇ 0:001 , Wilcoxon rank sum test) over the no instance selection case when applying the threshold band algorithm with either a 5 or 10% removal cap. This is further elucidated in FIG. 16 where the notches of the box plots for 5 and 10% instance removal do not intersect the notches of the boxplot of the default case.
- Instance selection using the classification probability threshold band demonstrated very promising results.
- This approach leveraged classifier uncertainty as expressed through posterior probabilities. In the cases where the classification probability of the data points were close to the tuned threshold, there was uncertainty in the discrimination between the two classes. By removing instances within the uncertain band enveloping the tuned threshold, the overall performance of the classification algorithm increased significantly, even when only a modest fraction of instances were discarded (5-10%).
- FIG. 17 shows the first two components (derived using PCA) of these instances. As shown, the majority of the selected instances reside inside the class clusters.
- FIG. 18 illustrates the parallel coordinate plot of the 10 salient features for the selected instances, again corroborating the observation that the selected instances are interior to the feature clusters rather than outlying observations.
- the origin of the selected instances was as follows: 28.6% were drawn from unhealthy participants while 71.4% came from healthy participants. Notably the original data set was imbalanced with 17.6% and 82.4% of unhealthy and healthy participants, respectively. Despite this class imbalance, the instance selection algorithm disproportionately oversampled the unhealthy participants, suggesting a tendency for indeterminate cases to stem from unhealthy participants. In the original data set, 7% of boluses were unsafe while 92.9% were safe. Of the instances identified by the probability threshold band instance selection algorithm, 4.9% were unsafe boluses and 95.1 % were safe boluses. Considering the algorithm's oversampling of unhealthy participants, this latter finding indicates that many safe boluses of unhealthy participants were selected as uncertain.
- Classification Performance The safe and unsafe bolus-level classification performance achieved in this study is competitive when considering clinical detection rates reported in the literature. According to a recent study, sensitivity and specificity of clinical evaluations are reported to be 39% and 80% respectively, for penetration and 55.6% and 80.5% for aspiration. In other studies, detection sensitivity and specificity have been cited as 88 ⁇ 8% and 50 ⁇ 13%, respectively and 93 ⁇ 21 % and 56 ⁇ 20%.
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US (1) | US20200060604A1 (en) |
EP (1) | EP3585264A4 (en) |
JP (1) | JP2020511206A (en) |
AU (1) | AU2018225827A1 (en) |
CA (1) | CA3053958A1 (en) |
WO (1) | WO2018152635A1 (en) |
Cited By (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN109259732A (en) * | 2018-10-09 | 2019-01-25 | 广东数相智能科技有限公司 | A kind of identification model construction method and the illness method for early warning based on lingual diagnosis |
CN110638463A (en) * | 2018-12-24 | 2020-01-03 | 曾乐朋 | Method, apparatus, computer device and medium for detecting characteristic information of motion signal |
US11311201B2 (en) | 2018-11-02 | 2022-04-26 | Samsung Electronics Co., Ltd. | Feature selection for cardiac arrhythmia classification and screening |
Families Citing this family (9)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US10750976B1 (en) * | 2019-10-21 | 2020-08-25 | Sonavi Labs, Inc. | Digital stethoscope for counting coughs, and applications thereof |
CN111166336B (en) * | 2019-11-22 | 2023-02-17 | 智纤至康(上海)智能科技有限公司 | Device and method for detecting and monitoring cough |
JP7477358B2 (en) * | 2020-05-08 | 2024-05-01 | マクセル株式会社 | Biological testing device and biological information analysis method |
CN111653273A (en) * | 2020-06-09 | 2020-09-11 | 杭州叙简科技股份有限公司 | Out-hospital pneumonia preliminary identification method based on smart phone |
US11134354B1 (en) | 2020-06-15 | 2021-09-28 | Cirrus Logic, Inc. | Wear detection |
US11219386B2 (en) | 2020-06-15 | 2022-01-11 | Cirrus Logic, Inc. | Cough detection |
US11862188B2 (en) | 2020-10-22 | 2024-01-02 | Google Llc | Method for detecting and classifying coughs or other non-semantic sounds using audio feature set learned from speech |
US11793423B2 (en) * | 2021-05-03 | 2023-10-24 | Medtronic, Inc. | Cough detection using frontal accelerometer |
CN114209302B (en) * | 2021-12-08 | 2024-03-26 | 赵永源 | Cough detection method based on data uncertainty learning |
Citations (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CA2570202C (en) * | 2004-06-17 | 2014-02-11 | Bloorview Macmillan Children's Centre | System and method for detecting swallowing activity |
US20140249378A1 (en) * | 2013-03-02 | 2014-09-04 | Isonea Limited | Systems, methods and kits for measuring cough and respiratory rate using an accelerometer |
Family Cites Families (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JPH0998964A (en) * | 1995-10-03 | 1997-04-15 | Chiesuto M I Kk | Coughing measurement apparatus |
JP2009060936A (en) * | 2007-09-04 | 2009-03-26 | Konica Minolta Medical & Graphic Inc | Biological signal analysis apparatus and program for biological signal analysis apparatus |
EP2665408B1 (en) * | 2011-01-18 | 2021-08-11 | University Health Network (UHN) | Device for swallowing impairment detection |
CA2938789A1 (en) * | 2014-04-09 | 2015-10-15 | Nestec S.A. | Technique for determining a swallowing deficiency |
-
2018
- 2018-02-22 US US16/487,180 patent/US20200060604A1/en not_active Abandoned
- 2018-02-22 CA CA3053958A patent/CA3053958A1/en not_active Abandoned
- 2018-02-22 AU AU2018225827A patent/AU2018225827A1/en not_active Abandoned
- 2018-02-22 JP JP2019546237A patent/JP2020511206A/en active Pending
- 2018-02-22 EP EP18756813.4A patent/EP3585264A4/en not_active Withdrawn
- 2018-02-22 WO PCT/CA2018/050203 patent/WO2018152635A1/en unknown
Patent Citations (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CA2570202C (en) * | 2004-06-17 | 2014-02-11 | Bloorview Macmillan Children's Centre | System and method for detecting swallowing activity |
US20140249378A1 (en) * | 2013-03-02 | 2014-09-04 | Isonea Limited | Systems, methods and kits for measuring cough and respiratory rate using an accelerometer |
Non-Patent Citations (5)
Title |
---|
ELFARAMAWY ET AL.: "Wireless Respiratory Monitoring and Coughing Detection Using a Wearable Patch Sensor Network", 2017 15TH IEEE INTERNATIONAL NEW CIRCUITS AND SYSTEM: CONFERENCE (NEWCAS), 15 August 2017 (2017-08-15), pages 197 - 200, XP033141935, Retrieved from the Internet <URL:https://ieeexplore.ieee.org/document/8010139> * |
FALK ET AL.: "Augmentative Communication Based on Realtime Vocal Cord Vibration Detection", IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, vol. 18, no. 2, April 2010 (2010-04-01), pages 159 - 163, XP011328422, Retrieved from the Internet <URL:https://ieeexplore.ieee.org/document/5378634> * |
MAHMOUDI ET AL.: "Sensor-based System for Automatic Cough Detection and Classification", 8TH ICT INNOVATIONS CONFERENCE 2016. ELEMENT 2015 - ENHANCED LIVING ENVIRONMENTS, October 2015 (2015-10-01), Macedonia, pages I - X, XP055539521, Retrieved from the Internet <URL:https://www.researchgate.net/publication/293769331_Sensor-based_System_for_Automatic_Cough_Detection_and_Classification> * |
MOHAMMADI ET AL.: "Post-Segmentation Swallowing Accelerometry Signal Trimming and False Positive Reduction", IEEE SIGNAL PROCESSING LETTERS, vol. 23, no. 9, September 2016 (2016-09-01), pages 1221 - 1225, XP011618483, Retrieved from the Internet <URL:https://ieeexplore.ieee.org/document/7467441> * |
See also references of EP3585264A4 * |
Cited By (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN109259732A (en) * | 2018-10-09 | 2019-01-25 | 广东数相智能科技有限公司 | A kind of identification model construction method and the illness method for early warning based on lingual diagnosis |
US11311201B2 (en) | 2018-11-02 | 2022-04-26 | Samsung Electronics Co., Ltd. | Feature selection for cardiac arrhythmia classification and screening |
CN110638463A (en) * | 2018-12-24 | 2020-01-03 | 曾乐朋 | Method, apparatus, computer device and medium for detecting characteristic information of motion signal |
CN110638463B (en) * | 2018-12-24 | 2022-07-19 | 曾乐朋 | Method, apparatus, computer device and medium for detecting characteristic information of motion signal |
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CA3053958A1 (en) | 2018-08-30 |
EP3585264A4 (en) | 2021-03-03 |
EP3585264A1 (en) | 2020-01-01 |
JP2020511206A (en) | 2020-04-16 |
AU2018225827A1 (en) | 2019-08-15 |
US20200060604A1 (en) | 2020-02-27 |
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