The Aspirometer: A Noninvasive Tool for Detecting Aspiration
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The Aspirometer: A Noninvasive Tool for Detecting Aspiration Aim 3 
 
Cover Page 
05/23/2022 
NCT number: NCT06637774 
The date on which the document was most recently updated and, if needed, approved by a 
human subjects protection review board, was 05/23/2022.  
 

SPECIFIC AIMS 
 
 
 
Hundreds of thousands of people annually suffer from compromised nutrition, hydration, and quality of 
life due to impaired swallowing (oropharyngeal dysphagia or OPD), and aspiration of swallowed food into the 
respiratory system. OPD leads to aspiration pneumonia (AP) which increases the risk of death during 
hospitalization threefold, and adds 53,000 annual hospitalization days and nearly $550 million to US health 
care costs annually. Through our research, we seek to pre-emptively prevent OPD diagnoses from evolving 
into aspiration pneumonia, and mitigate these economic and human costs. Gold-standard videofluoroscopy 
(VF), used to objectively diagnose OPD, is not an appropriate screening tool due to its invasiveness, size and 
prohibitive cost, and it cannot be deployed immediately upon suspicion of OPD. Standard institutional 
screening (SIS) protocols are therefore used to predict OPD. But since they rely solely on subjective, non-
instrumented human judgment of OPD risk by simply watching the patient swallowing and observing for 
choking, a) their sensitivity is compromised  because half of aspirators do not choke due to silent aspiration, b) 
their specificity is compromised because up to 60% of nonaspirators cough during SIS, and c) in settings in 
which subsequent VF is not available or feasible, SIS provide no diagnostic information to guide intervention 
because they cannot identify the swallowing impairments leading to aspiration.  
In our current NIH-funded effort (for which the PI received the Presidential Early Career Award for 
Scientists and Engineers in 2016), we have developed an instrumental tool that can noninvasively identify 
patients with OPD with high agreement with gold standard VF, through innovative high-resolution cervical 
auscultation (HRCA) methods. Through collection of concurrent VF and HRCA data from 274 patients and 
using advanced data analytics methods, we showed that HRCA can accomplish two outcomes previously only 
possible with VF: a) differentiate between severe aspiration and unimpaired swallowing in patients with 
suspected OPD, and b) detect kinematic components of abnormal swallowing physiology that lead to 
aspiration. Our next major step is to translate these advances into an objective, precise instrumental screening 
tool that significantly increases accuracy of SIS in a convenient and easily adoptable form factor. There are 
three gaps we need to address to accomplish these goals. We need to finish characterizing the entire range of 
swallowing function from unimpaired through severe OPD using HRCA as our HRCA research has relied on 
data from patients with OPD. We then need to equate HRCA predictions to validated gold-standard derived 
cutoffs of OPD severity, and finally to combine these results into a deployable too that can be tested clinically.  
Therefore, we propose research leading to novel clinical and engineering advances that position our HRCA 
tool to accurately identify silent aspiration and OPD to enable patients to be immediately triaged for treatment 
to prevent AP and related adverse events, and prevent unnecessary treatment to unimpaired people.   
AIM 1: Complete collection of the entire continuum of HRCA swallowing signals from unimpaired to 
severely impaired, by collecting HRCA swallow data from healthy people. Hypothesis: HRCA signal 
signatures of swallowing in unimpaired people are distinguishable and significantly different discriminating from 
swallows from people with OPD. The aim will dramatically improve screening accuracy by completing 
characterization of the entire HRCA OPD severity continuum from unimpaired to severely impaired.   
AIM 2: Translate and equate HRCA swallow signal signatures to validated measures of swallowing 
impairment.  Hypothesis: HRCA cutoffs will be significantly associated with validated VF-derived OPD 
impairment cutoffs. Accomplishment of this aim positions us to establish independent HRCA cutoffs for levels 
of OPD severity that differentiate clinically significant OPD and aspiration from benign swallowing impairments.    
AIM 3: Prospectively assess the effectiveness of our HRCA system in predicting clinically significant 
OPD and aspiration in a randomized, controlled trial.  Hypothesis: HRCA, will detect significantly more 
OPD and significantly more non-OPD, compared to SIS alone. For this major step, we will deploy combinations 
of HRCA and SIS with consenting patients, and compare all screening predictions (SIS, HRCA, SIS + HRCA) 
to VF data from all participants. 
 The outcome of this research will be a clinically accurate, inexpensive, and noninvasive HRCA 
screening tool that will fundamentally advance efforts to reduce morbidity and mortality caused by OPD. The 
positive translational impact of this work is the melding of engineering (HRCA) with the clinical (SIS) screening 
processes.  This major technological leap will elevate the current standard of patient care by ensuring that 
patients with OPD are correctly identified before adverse events can occur, patients without OPD are not 
unnecessarily deprived of food more accurately than current methods, as well as lowering health care 
expenditures and lengths of stay and improving quality of lives. 
Contact PD/PI: Sejdic, Ervin
Specific Aims                                                                                                 
Page 36

SIGNIFICANCE  
Aspiration pneumonia (AP), caused by inhalation of food or liquids (aspiration) in people with oropharyngeal 
dysphagia (OPD), has been increasingly recognized as a 
significant cause of morbidity and mortality [1], raising its 
contribution to known overall pneumonia incidence five-fold 
(Figure 1) [2]. If patients with cardiovascular disease and ischemic 
stroke develop OPD during their illness, the chance of death can 
be doubled [3]. In acute care and nursing homes, where OPD has 
up to 75% prevalence, and for the half of the 800,000 US citizens 
who suffer strokes each year and develop OPD, late detection of 
OPD leads to AP, increasing mortality risk up to three-fold, adding 
nearly $500 million to US health care costs, and causing dramatic 
deterioration in quality of life [4-10].  Screening for OPD in these 
settings, a process based on human observation of a patient swallowing without the use of instrumentation 
(videofluoroscopy – “VF”), fails to identify many patients with silent OPD who remain unidentified until adverse 
events like AP occur. Thus, a quick, accurate method of predicting the presence and absence of silent and 
overt OPD is needed to prevent the inadvertent aspiration of food, liquids and colonized oral secretions that 
lead to pneumonia.  We are developing such an instrumental screening tool, the Aspirometer, which, when 
deployed at the time of admission or first suspicion of a disease that causes OPD, can identify OPD early, and 
prevent the consequences of silent OPD. 
Scientific Premise: OPD is caused by disease-related disruptions in swallowing kinematics (e.g., timely and 
complete movement of oropharyngeal structures) or swallow physiology (force and symmetry of muscular 
contractions, neuromuscular transmission, sensory function). These impairments lead to outcomes such as 
airway penetration and aspiration, and excessive amounts of post-swallow oral or pharyngeal residue that are 
aspirated. OPD patients are suspected of aspirating when they cough or choke while eating or drinking or fail a 
screening test. They are then referred for diagnostic testing with VF which is the gold standard for 
characterizing silent aspiration and OPD.  VF is never used for screening because it requires trained personnel 
to perform and interpret [13] and would add costs of over $320 million annually to screen just people with 
stroke. Many people who aspirate do not have reflexive airway protective responses to aspiration and do not 
cough or choke rendering this “silent aspiration” undetectable at the bedside, hence many patients develop 
aspiration pneumonia [11, 12] before its risk is known.  
 A major gap exists between the convenience, low cost and imprecision of screening, and the sophistication, 
expense, precision and preventive benefits of VF.  Inexpensive and convenient bedside standard institutional 
screening (SIS) protocols for OPD are present in many settings 
[14, 15]. Published, validated SIS’s [21-23] are based on human 
observations of patients swallowing water, observing for signs of 
OPD using standard procedures (Figure 2). SIS performed by 
ordinary observers (nurses, nursing assistants) relies on whether 
the patient coughs or chokes during or after wallowing to predict 
aspiration (Figure 3). Patients who “fail” SIS all receive 
interventions to mitigate pneumonia risk including gold standard 
testing using VF, to guide these interventions, whether or not they 
have OPD. Patients who “pass” SIS (no coughing or choking) 
either do not aspirate, or they have undetected silent aspiration 
(which occurs in up to 40% of aspirators), and resume unsafe oral 
intake without intervention [16]. Popular, but weak, SIS signs of 
aspiration such as “wet voice quality” after swallowing, have not 
proven sufficiently reliable as valid predictors of aspiration [17]. A 
“wet voice” is absent in up to 80% of thin-liquid aspiration events, 
and in up to 50% of thicker liquid aspiration events among people with Parkinson’s disease [18]. Moreover, 
judgments of wet voice are misleading and poor indicators of aspiration or dysphagia [17], exhibit poor 
sensitivity and inter-judge reliability [19], and even using acoustic analysis of voice production parameters to 
assess voice after swallows cannot reliably predict airway penetration [20]. Aside from their previously 
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1991 1992 1993 1994 1995 1996 1997 1998
All Pneumonia
Aspiration Pneumonia
Figure 1 – Increased pneumonia (22.6%) vs 
aspiration pneumonia (93.5%) incidence 1991-1998. 
Figure 2 – Comparing the current practices with 
data analytics of HRCA. Forms and patient 
observations are currently used by clinicians to 
subjectively judge the presence of dysphagia. The 
Aspirometer can provide an objective dysphagia 
assessment approach. 
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Research Strategy                                                                                             
Page 37

described weaknesses, adherence to the screens’ protocols varies [5, 24], reducing the validity and precision 
of SIS. No currently available SIS can identify silent aspiration 
or OPD-related risk of pneumonia, malnutrition, and dehydration 
[25, 26]. Thus, an inexpensive and accurate silent aspiration 
detection tool is needed and our research has produced strong 
lines of evidence toward that end. Our scientific premise is that the 
Aspirometer, an accurate, early-warning, easy to use, noninvasive 
tool to detect silent aspiration and OPD, can lead to pre-emptive 
identification of silent aspirators and significantly reduce morbidity, 
mortality, and health care expenditures.   
“Cervical auscultation” (CA) methods rely on use of a 
stethoscope (Figure 2) to listen to and subjectively interpret the 
sounds emanating from the throat during a swallow. Limited by the 
narrow detection capacity of the human auditory system and limited 
signal transmission by the stethoscope, research has repeatedly confirmed CA’s poor accuracy and 
reproducibility, and poor inter-examiner agreement as to the kinematic impairments producing the sounds 
observed, leaving its ability to identify at-risk patients with silent aspiration poor [27, 28]. Our previous work has 
shown promising results toward improving the objectivity and accuracy of CA [29-31], with high resolution 
instrumentation that can record the entire digital spectrum of these swallowing sounds that analog methods with 
stethoscopes and the human auditory system cannot transmit, perceive and process. The Aspirometer, relies 
on high resolution cervical auscultation (HRCA) that records swallowing vibrations and sounds with miniaturized 
high-resolution triaxial accelerometers and microphones placed over the anterior throat (Figure 2). Signals are 
then analyzed objectively with advanced data analytics far exceeding the abilities of stethoscope-human 
judgment.   
 The central challenge to deployment of the Aspirometer is that our current methods have been 
developed only with 274 patients with suspected OPD. Our preliminary results (see A1.c and A2.c) have 
shown that our algorithms have become increasingly accurate at identifying HRCA signals that correspond to 
many swallow kinematic events contributing to OPD and airway protection during swallowing [32-36]. To define 
the entire dysphagia severity continuum and a cutoff for silent 
OPD presence, we need to complete the severity continuum 
by analyzing swallows from completely healthy control 
participants and compare them to impaired swallows in 
patients with dysphagia including those with silent aspiration. 
Our overall hypothesis, formed on our strong preliminary 
results (see Approach), is that the HRCA-based screening 
can independently and accurately predict silent aspiration and 
OPD before it can cause harm in patients with OPD. Based 
on our strong preliminary data, we expect that HRCA-based screening with the Aspirometer will identify 
pneumonia risk earlier in people with new diagnoses that cause OPD and silent aspiration by detecting 
significantly more patients with clinically significant OPD than SIS alone, and identify those who fail screens but 
do not have OPD (Figure 4). The proposed approach will lead to HRCA-based characterization of the entire 
continuum of healthy and disordered swallow function, equation of this HRCA continuum with established 
measures of OPD severity, and development of the Aspirometer as a reliable dysphagia screening tool that 
can be implemented immediately upon the first suspicion that a patient has suspected OPD, regardless of the 
setting.  This will lead to pre-emptive prevention of healthcare associated, hospital and community acquired 
OPD-related pneumonias and other OPD-related adverse outcomes. 
INNOVATION 
Our approach represents a major leap forward in the inexpensive and noninvasive screening of OPD, 
through use of novel theoretical concepts and technologies and objective decision-making methods that will 
mitigate dysphagia-related adverse events in ordinary clinical scenarios, while at the same time directing 
resources to gold standard diagnostic testing only for those patients who need it [37]. Our goal is to develop a 
novel screening paradigm using the Aspirometer, as a universal OPD screening tool that can be deployed in 
ordinary and often chaotic clinical settings like hospital emergency rooms, nursing homes, and home care 
Figure 4 – The significance of the proposed work. 
Figure 3 – SIS pathways with and without HRCA. 
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Research Strategy                                                                                             
Page 38

situations [38, 39], where OPD is prevalent and imaging studies are unavailable or unacceptable to the patient. 
But that leap requires our further engineering and clinical developments to enable an HRCA system to predict 
which patients need protective interventions to mitigate their pneumonia risk.  
In our currently R01 funded research, we have established moderate but significant relationships between 
swallow kinematic events observed via VF and the HRCA signals they generated (see section A2.c) as well as 
correlations between HRCA signals and airway protection, 
a key predictor of aspiration pneumonia. Our findings have 
advanced the science of OPD early detection beyond that 
of previous studies that have not culminated into 
integration of these findings to characterize actual risk of 
OPD-related adverse events like AP [40-58]. These earlier 
studies attempted to match VF images to CA signals to 
associate swallow events to sounds [59, 60], or infer the 
relation between kinematic events and key features of the 
swallowing signal [27, 61-64].  But unlike our research, 
none of these studies has directly linked distinct waveform 
features to underlying swallow physiology or silent 
aspiration, and they have established only the presence of weak correlations between swallow events and 
signals [60, 64] (Figure 5).  
Our approach breaks new ground by equating swallow physiologic data collected in ordinary settings to 
accepted metrics of swallow impairment, and defining HRCA outputs that can identify patients in need of 
intervention without the 
use of VF imaging. We 
seek to further innovate 
and develop HRCA as a 
feasible method that can 
produce 
generalizable 
clinical results (Table 1). 
First, we will collect and 
analyze swallows from 
healthy 
participants 
in 
order to establish cutoffs 
for 
“healthy 
swallows” 
(see A1.c.). Second, we 
will equate HRCA signals 
to OPD impairment levels 
derived from a validated 
and widely used profile of 
OPD impairment using robust big-data analysis approaches, not previously used in this line of research. Third, 
we will test our HRCA-based Aspirometer system in a blinded, randomized controlled trial comparing the 
outcomes of SIS with and without our HRCA-based tool.  
APPROACH 
PROGRESS REPORT FOR RENEWAL: The project (July 2013-June 2018) revolved around the following aims: 
1) In order to accumulate an adequately-powered reference data set for training signal processing 
classifiers, we will collect swallowing accelerometry signals from a larger sample of individuals 
undergoing concurrent videofluoroscopy.  
2) In order to enhance the accuracy of signal processing classification algorithms to detect problems 
in swallowing safety and efficiency, we will add an additional physiological signal to the Aspirometer 
design (acoustic information, collected concurrently with a co-located microphone). We will explore 
the value of any non-overlapping information in this additional signal for improving the accuracy of 
the classifier.  
Figure 5 – Some established relationships between HRCA 
and swallowing kinematic events found in the literature [24].  
Table 1 – The innovation of the proposed project. SIS denotes standardized institutional screening. 
Open questions 
Current state-of-the-art 
Proposed research 
Define the HRCA 
range of “normal 
swallowing 
signals” from 
healthy people 
Our large data set from 4000 
swallows of people referred for 
dysphagia testing has been 
analyzed; no “healthy data”; prior 
studies of “normal” used different 
signal processing and data 
collection methods 
Collect and analyze swallows from 
healthy persons; repeat analyses 
performed on 4000 swallow data set 
from patients; completes the range of 
data collected from healthy  severely 
dysphagic 
Equate the 
HRCA signal 
results to 
accepted 
standard of 
swallow 
impairment 
Not understood; prior research has 
equated signals and signal features 
to specific events and not to 
accepted, clinically utilized and 
validated measures of swallow 
impairment 
Rate all swallows in both data sets 
(4000 swallows from patients + 
swallows from healthy people) with the 
“Modified Barium Swallow Impairment 
Profile” 
Can HRCA 
identify 
dysphagia more 
accurately than 
SIS? 
Can differentiate grossly abnormal 
from normal. Demonstrated only for 
small sample sizes. Not clinically 
validated. 
Deploy a HRCA system; blinded, 
randomized controlled trial comparing 
SIS to SIS + HRCA, using 
videofluoroscopy as the gold standard 
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Research Strategy                                                                                             
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3) In order to explore the potential of non-invasive swallowing signal processing classifiers to detect 
swallowing problems beyond the context of thin liquid swallows, we will expand our data collection 
protocol to include swallows of nectar-thick and honey-thick liquids.  
Aim 1 was successfully completed, as we collected HRCA data from 274 patients (24 more patients than 
initially proposed) undergoing a concurrent VF exam. As a result, we have a unique, large database of 
simultaneously acquired HRCA signals and VF images that is unparalleled in OPD research.  
Per Aim 2, we clearly demonstrated that multiple sound and vibrations signal features acquired 
simultaneously during VF exams contain complimentary information, and they should not be substituted for each 
other. Figure 6(A) summarizes our main results from [67]. The shaded columns and rows indicate microphone 
and accelerometer axis combinations with the greatest number of dissimilarities indicating the complementary 
nature of acoustic and vibratory signal features, while the red dots indicate signal features in which the vibratory 
signal features had significantly higher values than the acoustic signals.  Figure 6(B) summarizes our main 
results of [68]. Specifically, although the swallowing sounds and swallowing vibrations might have the same 
kinematic sources, swallowing signals recorded by the microphone (sounds) and the accelerometer (vibrations) 
differed from each other in the time and frequency domains. These findings shift the existing paradigm that 
swallowing sounds 
and 
vibration 
information 
are 
equivalent [78, 79], 
and support our two-
sensor method as 
superior to a single 
acoustic 
detector 
[79, 80]. In fact, our 
findings confirm that 
swallowing 
signals 
from 
bothsensors 
provide 
useful 
information 
about 
swallowing function. 
Hence, 
our 
subsequent 
research 
of 
swallowing 
physiology 
with 
these sensors using 
concurrent 
VF 
images 
advanced 
our understanding of 
the 
sources 
of 
swallowing-related 
vibrations 
and 
sounds.  
As per Aim 3, we clearly demonstrated that swallowed fluid viscosity strongly influences the swallowing 
assessment (Figure 7 summarizes the main results of [70]). We found that HRCA can detect several statistical 
differences between unsafe swallows of viscous fluid, in which clinically significant aspiration and laryngeal 
penetration occurred, and safe swallows that exhibited airway protection scores that fall within the normal range 
for healthy people [81]. This is of interest because aspiration of thicker liquids has been shown to produce higher 
rates of pneumonia and longer hospitalization durations than aspiration of thin liquids [82]. Past research has 
suggested that thickening agents used during videofluoroscopy exams exhibit non-Newtonian fluid properties, 
which lead to the reduced aspiration rate in dysphagic patients [83, 84].  
Based on this research by our team, Dr. Sejdic received the Presidential Early Career Award for 
Scientists and Engineers in 2016. Overall, the project was very successful as all three aims were successfully 
Figure 6 – Our results for Aim 2: (A) A summary of differences between swallowing sounds and vibrations 
among groups of study. Red/black circles indicate that the axis/mic in “Comparison couple” column has the 
higher value in a specific feature mentioned in “Features’ column than the mic/axis. The column highlighted 
in light green and five rows highlighted in light orange indicate group/features with the largest number of 
dissimilarities. (B) A summary of directional differences. All dots (red and black) indicated the existence of 
directional dissimilarities. Axes column demonstrates which two axes are compared to each other. Red dots 
indicate that the first axis in the axes column has a higher value of the computed feature in features column 
than the second axis, while black dots indicate the second axis has a higher value than the first axis. 
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Research Strategy                                                                                             
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completed and over 10 publications have been disseminated in major research journals [65-77], in addition to 
numerous presentations by Dr. Sejdic and Dr. Coyle 
at 
domestic 
and 
international 
institutions/conferences.  Our line of research has 
also begun investigating theoretical frameworks 
linking swallowing signals to other central and 
peripheral mechanisms of recent interest to 
researchers by investigating brain networks on a 
swallow-by-swallow basis using electrophysiologic 
monitoring [73-77]. One of our future mechanistic 
goals is to relate central neurological changes in 
disease states to features extractable from HRCA 
recordings to portray a complete picture of 
swallowing, from central neurological changes in the 
brain to peripheral kinematic events observed 
aerodigestive tract.  
PROPOSED WORK: Our strong preliminary results 
show that we are positioned to apply machine 
learning algorithms to extract swallowing events 
from HRCA recordings and relate those events to a 
score on standardized swallow impairment scales. 
Upon completion, we will be able to identify false-
negatives (patients who pass current SIS because 
they silently aspirate), as well as OPD and 
penetration-aspiration 
events 
that 
are 
either 
clinically insignificant or do not require costly 
intervention or VF studies. This will help to allocate VF and intervention resources to those most in need, while 
preventing unnecessary food restrictions to those who currently fail dysphagia screens but are not at risk (false 
positives). 
AIM 1: Complete the entire continuum of HRCA swallowing signals from normal to severely impaired, by 
collecting HRCA swallow data from healthy people.     
A1.a. Introduction: The goal of a useful HRCA swallowing screening tool is to accurately distinguish 
healthy/normal swallowing from OPD that can pose health threats to the patient. We have a large database of 
4000 swallows from patients referred for VF evaluation of dysphagia and have equated their VF-identified OPD 
characteristics with HRCA signal features, but we do not have data to complete the “normal” end of the 
impairment severity continuum from healthy people. The objective of this aim is to collect and analyze swallow 
VF data from healthy persons without dysphagia, extending our previous R01 project. This will complete a large, 
master set of swallows of all impairment severity levels, and our analysis will determine define the cutoff for 
“normal” (or “healthy/nonpathological”) swallows from HRCA signals. The working hypotheses for this aim is that 
pharyngeal swallowing events in HRCA signals from healthy people exhibit significantly different HRCA signal 
signature features than those from patients with dysphagia and that there is sufficient separation to allow 
discrimination (see A1.c.). We will use an approach that relies on modern machine learning methods [85]. We 
will collect a set of unimpaired swallows from healthy age-matched adults to represent the “normal” end of the 
OPD impairment continuum. This aim’s rationale is that our current data set’s least severely impaired swallows 
were obtained from patients with suspected dysphagia due to disease, and therefore are not valid 
representations of normal swallows from healthy people. We expect that completion of the entire continuum of 
impairment severity with HRCA will enable us to preemptively differentiate patients requiring more aggressive or 
immediate diagnostic intervention than is currently possible with SIS while more accurately identifying patients 
who do not require intervention. By achieving this aim we will accomplish an essential goal in healthcare 
associated pneumonia prevention: accurate and early detection of clinically significant OPD impairment from 
HRCA signals and expedited patient management in clinical and other naturalistic settings.   
A1.b. Justification and feasibility – Review of relevant literature: Following development of the penetration-
aspiration scale (PAS), it was determined that PAS scores differentiated healthy swallows from those obtained 
Figure 7 – Our results for Aim 3: (A) Feature values corresponding to 
anterior-posterior swallowing vibrations. (B) Feature values 
corresponding to swallowing sounds. (C) Feature values 
corresponding to swallowing sounds. These values represent the 
mean and interquartile range of each feature of our data set separated 
by bolus viscosity and whether it was a safe or unsafe swallow. 
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Research Strategy                                                                                             
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from people with two conditions that cause OPD: stroke and treatment for head and neck cancer [81]. 71 of 95 
normal participants produced PAS scores of “1” (no airway penetration) on the first and second swallows, with 
19 producing a score of “2” and a single participant producing a “3” with no scores observed above “3”. 
Meanwhile patients in both patient groups scored significantly “higher” (more severe) on the PAS than did the 
normals during both first and second swallows (Figure 8). Likewise, significant 
kinematic differences between healthy and disordered swallows have been the 
benchmark for defining OPD for more than 3 decades [32, 86-89].  Studies 
have shown significant differences between healthy swallows and those 
observed in patients with OPD swallows, in the swallow kinematic events that 
we are measuring in this project (e.g., the duration of stage transition, oral and 
pharyngeal transit durations, and duration of laryngeal closure among many 
others) [90-92].  Because of the disparity in PAS and other measures of 
swallow function between controls and people with suspected OPD whose 
swallowing is not clinically unsafe, completion of the HRCA continuum to include normal swallows is necessary 
to advance our innovative approach to noninvasive OPD classification. 
Past studies attempting to classify CA signals have had various limitations. First, many studies utilized 
less than optimal sample sizes and did not clearly differentiate independent judge training and testing groups, 
which limits the generalizability of the results [37, 58, 93-104]. Second, linear classifiers have possibly reduced 
the maximum potential accuracy of the classification method [37, 95, 96, 100-102, 105]. Finally, a number of 
studies incorporated measurements other than cervical auscultation (e.g., nasal airflow) [58, 93, 94, 100, 103], 
which introduced additional confounding variables. Our literature search found that techniques that utilize neural 
network-based classifiers have some of the highest reported accuracies for a given task [93, 94, 97-100, 105], 
but there are open problems still unresolved. These studies 
acknowledge that they are investigating a limited selection of 
mathematical signal features and that alternate choices may offer 
benefits to classification. From these previous attempts, the field 
would benefit from a technique that was both able to analyze 
higher-order signal features and could self-select features to 
analyze through use of unsupervised learning methods. One 
relatively new classification technique, deep learning, possess 
these desirable traits, thereby combining much of the past 
research on the topic into a single method.  
A1.c. Justification and feasibility – Preliminary studies: In our 
preliminary research using an artificial intelligence method was 
used to differentiate swallows made by healthy subjects and the 
least impaired swallows produced by patients with OPD [70]. A 
total of 55 healthy participants (28 men, 27 women, mean age 
39) with no history of swallowing disorders, head or neck trauma 
or major surgery, chronic smoking, or other conditions which 
impair swallowing performance, completed a total of 30 
independent swallows of several liquid viscosities while their 
head was in a neutral position. The non-healthy participants 
consisted of a total of 53 patients (34 men, 19 women, mean age 
63) with suspected dysphagia who were undergoing a VF 
evaluation at the discretion of the examining clinicians and based 
on patient needs, rather than an artificially controlled, 
standardized data collection procedure. Presentation order, head position, and other environmental factors were 
unique for each patient. The materials swallowed during the examination were of comparable consistencies (thin 
(<5 cps), `nectar' (300 cps),`honey' (2000 cps) consistency liquids) to those provided to healthy subjects based 
on available product information and qualitative guidelines. Using all of these swallows from both groups, we 
built an artificial intelligence method [70] based on deep belief networks [107], one of the newest forms of artificial 
neural networks [108], which combines multiple neural networks in different layers into a single decision process. 
Figure 9 summarizes our results. Our combined deep belief network configuration also demonstrated generally 
Figure 9 – Preliminary results for Aim 1. Using 
different configurations of artificial neural networks, 
we can achieve very high accuracies. In this case, a 
1-layer combination of two large 2-layer networks for 
each axis provided the best results (bolded above). 
Figure 8 – PAS scores for healthy 
controls, stroke and cancer patients. 
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Research Strategy                                                                                             
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greater sensitivity than our single-axis networks. But we do not have concurrent VF validation of the data 
obtained from the healthy people. Collection and analysis of these data will complete the range of swallow 
function and enable completion of our 2nd and 3rd aims, which seek to develop an HRCA impairment severity 
continuum.   
A1.d. Experimental approach: STEP 1: We will collect concurrent VF and HRCA recordings from 70 healthy 
subjects (35 females and 35 males) with no previous history of swallowing difficulties who are age matched to 
our database of swallows from patients with OPD. Each subject will complete five swallows of 3mL thin liquid 
and five swallows of self-selected, comfortable volume of thin liquid from a cup, in a head-neutral position. STEP 
2: We will first measure nine kinematic measures with VF (onset, offset, and time to peak hyolaryngeal excursion; 
laryngeal closure and reopening; UES closure and reopening; onset and offset of tongue base to posterior wall 
contact), penetration-aspiration scale scores [109], and pharyngeal retention/residue scores [89, 110, 111, 112]. 
STEP 3: Pre-process all HRCA 
recordings per steps taken in previous 
publications [29, 30, 71], to remove 
any disturbances and diminish noise in 
these recordings.  STEP 4: VF results 
(STEP 2) will be equated with HRCA 
signal features and categorized by 
bolus 
volume 
of 
the 
swallowed 
material, these data will be combined 
with the data from over 4000 patient 
swallows from our previous R01 grant. 
STEP 5: We will develop an algorithm based on the state-of-the-art machine learning concepts that will 
differentiate between healthy controls and people with suspected OPD whose swallows did not exhibit OPD (see 
Figure 10). Specifically, we will analyze a random 70% training subsample and use the deep belief machine 
learning approach as in our preliminary work [70]. Once modeling is complete, the remaining 30% validation 
subsample will be used to obtain accuracy estimates devoid of model overfitting.   
A1.e. Data interpretation: In STEP 2, Dr. Coyle will train doctoral students to evaluate all VF dependent 
variables and establish their inter- and intra-rater reliability (intraclass correlation coefficient above 0.9 for 
continuous variables and kappa above 0.75 or percent exact agreement above 80% for categorical variables), 
in the measurement of kinematic event timing, and assignment of P-A and pharyngeal retention/residue scores. 
They will use ImageJ image processing software, to measure penetration-aspiration scores, and kinematic and 
pharyngeal retention/residue measures from all swallow recordings in this new set of healthy swallows, and 
complete the same measurements from the remaining 3900 swallows in our database that did not undergo the 
complete kinematic analysis that we performed to produce the preliminary data presented in section A2.c. To 
ensure their judgments do not drift over time, we will perform ongoing reliability measures using the same criteria, 
through a rolling re-analysis of a randomly selected 10% of analyzed swallow recordings. Then, to understand 
the accuracy of the developed HRCA analysis algorithm using only the training subsample in STEP 5, we will 
begin by calculating a confusion matrix [85]. The confusion matrix will be calculated by comparing the algorithm’s 
output with scores from STEP 2. The confusion matrix will be then used to calculate false positives, false 
negatives, true positives and true negatives, and lastly sensitivity, specificity, predictive values and likelihood 
ratios for the algorithm. These values will be initially calculated using the leave-one-out approach, but we will 
then repeat the analysis with 10-fold cross-validation. Then in STEP 5, we will use the number of correctly and 
incorrectly HRCA-detected swallows to calculate the sensitivity and specificity values, again using the leave-
one-out approach (the most exhaustive and computationally expensive cross-validation approach) and 10-fold 
cross-validation. We have proposed state-of-the-art machine learning methods based on deep learning as our 
primary analysis, but we will also consider other techniques such as classification tree methods [113], and 
generalized linear models due to their ability to produce simpler prediction rules for ease of use, cognizant of the 
potential loss of some accuracy compared to deep learning. Finally, we will use the validation subsample to 
obtain authoritative measures of accuracy devoid of risks of model overfitting.     
A1.f. Expected outcomes: The benchmark for success of Aim 1 will be creation of a machine learning algorithm 
that completes the continuum of of HRCA signal characteristics from normal to the most severely impaired 
swallowing function that will then be available for future research. Specifically, we anticipate these algorithms 
Figure 10 – Our proposed experimental and data analysis approach for Aim 1. 
Contact PD/PI: Sejdic, Ervin
Research Strategy                                                                                             
Page 43

will correctly identify normal airway protection (PAS scores of 1-2) in healthy controls and patients with dysphagia 
with over 90% accuracy compared to the gold standard VF ratings for each swallow [70]. These outcomes will 
establish the evidence that the HRCA signals and advanced analytics form a robust and reliable swallowing 
screening system with comparable accuracy to VF, while also demonstrating the system’s ability to identify 
additional swallowing diagnostic features that are currently only possible with VF, potentially advancing HRCA 
as not a screening tool with diagnostic capabilities far beyond SIS.   
A1.g. Challenges and contingency plans: The preliminary results (see A1.c.) strongly support our hypothesis 
(see A1.a.). In the highly unlikely event that our VF judges’ ratings drift unacceptably, we will increase 
surveillance on inter- and intra-rater reliability and pull judges from analysis and retrain them to criterion, retest 
their reliability before placing them back online.   
A1.h. Sample size justification: We currently have data on 4000 swallows from 274 dysphagic persons.  We 
plan to add 700 swallows from 70 healthy persons to this data set.  Allowing for some refusals, we anticipate 
obtaining 650 swallows from 65 healthy persons. Assuming the sensitivity and specificity will be approximately 
90%, intra-class correlation of 0.7 from our prior data and a resulting design effect of 3.8 [114], and using 
published methods [115, 116] implemented in commercially available software (PASS 2012, Number Cruncher 
Statistical Systems, Kaysville, UT), conservatively we will be able to estimate sensitivity with a margin of error 
as small as ±2% and specificity with ±8% in the training sample, and ±3% and ±13% in the validation samples, 
at the 95% confidence level.  
AIM 2: Develop tools for translating HRCA to equivalent validated measures of swallowing impairment.     
A2.a. Introduction:  We have successfully demonstrated HRCA’s capabilities in grossly identifying pathological 
airway protection. However, HRCA’s predictive capabilities lie in its ability to analyze the summation of all 
concurrent events occurring during swallowing. The objective of this aim is to equate, or elicit means of 
translating, HRCA signal analysis results to a validated clinical measure of swallowing impairment, namely, the 
Modified Barium Swallow Impairment Profile (MBSImP) [117]. To attain the objective of this aim, we will test the 
working hypothesis that HRCA can noninvasively quantify levels of dysphagia severity and airway protection 
comparably to accepted clinical OPD severity scales derived from gold standard VF data analysis. We will use 
an approach that relies on scoring each swallow in our large dataset according to the MBSImP component 
scales. Next, through advanced data analytics tools, we will establish relationships between HRCA signal 
features and the MBSImP scores for each swallow. The rationale for this aim is that HRCA signal analysis results 
must be equated with and translatable to an acceptable gold standard of swallowing impairment to validate its 
efficacy in identifying clinically significant dysphagia. We expect that achievement of this aim will substantially 
advance dysphagia screening and diagnostics by producing a readily deployable surrogate determination of 
swallowing disability in the absence or inaccessibility of gold standard VF testing.  
A2.b. Justification and feasibility – Review of relevant literature: To accomplish our goal, it is necessary to 
equate HRCA signals to standardized and validated measures of swallow impairment and airway protection. As 
discussed in A1.b, the PAS differentiates normal vs. disordered airway protection [81]. More recently, the 
MBSImP has become widely accepted in both clinical and research domains as the gold standard for interpreting 
VF data to determine the degree of swallowing impairment in people with OPD [118]. MBSImP measures 17 
components of swallowing impairment in oral, pharyngeal, and esophageal regions through analysis of VF data 
using ordinal severity scales of 17 components: six oral, ten pharyngeal, and one esophageal.  Each component 
is rated based on specific criteria that were established and validated through expert consensus and repeated 
testing. Establishing HRCA signal features that predict MBSImP ratings can demonstrate the value of HRCA in 
the screening and diagnostic processes and potentially obviating the need for VF in some cases. Furthermore, 
combining MBSImP and PAS [81, 109, 119] leads to a complete multidimensional profile of swallow impairment 
and potential dysphagia-related risk to which HRCA can be equated. MBSImP has been used concurrently in 
several studies with PAS and both measures have shown their complementary value in the comprehensive 
quantification of swallow impairment and the airway protection outcomes that these impairments produce [118, 
120-124].   
A2.c. Justification and feasibility – Preliminary studies: Our pilot study has identified strong differences in 
clusters of HRCA signal features that clearly separated less- and more-severely impaired swallows.  We 
analyzed 100 swallows from our 274-patient data set, to determine if HRCA signal features can detect swallow 
kinematic events. All 100 swallows were simultaneously observed with VF and HRCA sensors, and 13 swallow 
Contact PD/PI: Sejdic, Ervin
Research Strategy                                                                                             
Page 44

kinematic events (onset of barium entering pharynx, onset, offset and arrival at maximum displacement of the 
hyoid bone, onset and offset of laryngeal closure, onset of laryngeal reopening, onset and offset of UES opening, 
onset of UES reclosure, onset and offset of contact of the tongue base to the posterior pharyngeal wall, and first 
re-opening of tongue base to posterior pharyngeal wall contact) were measured by judges who were trained in 
VF image processing methods, blinded to patient diagnosis, and whose inter- and intra-rater reliability were 
acceptable. The results were compared to HRCA signals to determine the frequency of occurrence of 7 separate 
HRCA signal feature peaks (maxima) with each of the 13 
swallow kinematic events, to a tolerance of within 0.1 
seconds of the actual swallow kinematic events, the 
standard tolerance for temporal VF analysis [89], (Table 
2). Fifty-two to 88% of the kinematic events were detected 
within this tolerance by HRCA sensors more than half of 
the time. Since the probability is <8% that any continuous 
vibratory or acoustic signal would exhibit a strongly 
systematic change that co-occurs with specific swallow 
kinematic events, these findings indicate that HRCA signal 
features are strongly associated with swallow kinematic 
events and may show promise in identifying abnormal vs. 
normal swallow function. We then stratified the data into 
two PAS score ranges: 1-2 (normal airway protection) and 
3-8 (abnormal airway protection).  Microphone and accelerometer signal feature peaks co-occurred with normal 
airway protection 40-49% of the time, but these peaks co-occurred only 11-14% of the time during abnormal 
airway protection swallows. Moreover, the ranges of these probabilities did not overlap for any of the four data 
collection channels, and the results were statistically significant as expected at p=0.018.  These results indicate 
that HRCA can detect co-occurring swallow kinematic events while at the same time differentiating between 
normal and abnormal airway protection.  Though these results indicate HRCA’s ability to detect simultaneously 
occurring swallowing events and airway compromise in the presence of other noise, the necessary next step is 
to equate this capability to standard measures of overall swallow impairment. 
A2.d. Experimental approach: STEP 1: All swallows from patients and healthy participants (Aim 1) will be rated 
using the MBSImP. Judges will undergo standardized training in visual inspection and MBSimP ratings of 
swallow kinematic functions and impairments, and establishment of inter- and intra-rater reliability before 
beginning scoring. STEP 2: In addition to MBSImP component 16 (pharyngeal residue), pharyngeal residue 
(valleculae, pyriform sinuses) will be measured using the Normalized Residue Rating Scale which standardizes 
areas of barium residue retained in 
pharyngeal 
recesses 
(valleculae, 
pyriform sinuses) using conversion 
factors based on individual patient 
height [112]. STEP 3: We will measure 
airway compromise using the PAS [109].  
STEP 4: Pre-process HRCA recordings 
per steps taken in previous publications 
[29, 30, 71] to remove any disturbances 
and diminish noise in these recordings. 
STEP 5: We will develop an algorithm 
based on the state-of-the-art machine learning concepts whose output will be the differentiation between healthy 
controls and people with suspected dysphagia whose swallows did not exhibit dysphagia, healthy dysphagic 
(see Figure 11). Specifically, we will utilize a random 70% training subsample for the deep belief machine 
learning approach as in our preliminary works [70], and once modeling is complete, use the remaining 30% 
validation subsample to obtain accuracy estimates devoid of model overfitting.   
A2.e. Data interpretation:  We will employ the same deep learning methods as in Aim 1 for our primary analysis, 
but instead will employ techniques suitable for categorical, ordinal dependent variables such as MBSImP and 
PAS.  In addition, we will employ regression tree and linear mixed model methods in an effort to obtain simpler 
Table 2 – Frequency of signal maxima co-occurring with 
swallow kinematic events; separated by PAS ranges PAS 1-2 
vs. PAS 3-8. NUME denotes the number of signal maxima co-
occurred with swallow kinematic events > 50% of the time. MIC 
denotes a microphone, while S-I, A-P, and M-L denote 
vibrations in superior-inferior, anterior-posterior, and medial-
lateral directions captured by an accelerometer. 
  
 Sensor 
NUME 
PAS 1-2 
PAS 3-8 
overall % 
range 
overall % 
range 
MIC 
80/91 
49 
37-65 
13.6 
10-18 
S-I 
64/91 
45 
29-62 
11 
7-16 
A-P 
55/91 
43 
26-62 
11 
6-17 
M-L 
48/91 
40 
25-61 
10 
3-16 
Figure 11 – Our proposed experimental and data analysis approach for Aim 2. 
Contact PD/PI: Sejdic, Ervin
Research Strategy                                                                                             
Page 45

means of converting HRCA signals.  We will examine performance metrics such as root mean square error and 
multiple coefficient of determination (R2) to evaluate our score equating models. 
A2.f. Expected outcomes: The benchmark for success of Aim 2 will be creation of a set of HRCA signal 
signatures that can be translated to MBSImP, PAS, and pharyngeal retention measures. We anticipate a model 
can be built with sufficiently high predictive ability so that R2 and/or root mean square error of score translation 
is no greater than 5% of the range of the swallowing impairment measure. The major outcome of this aim is that 
HRCA signal signatures will reflect swallow kinematic and airway protection function and equate to standard 
measures of swallow impairment which will help us develop the HRCA based Aspirometer as a tool that 
characterizes and predicts risk of adverse outcomes associated with dysphagia, without imaging.  
A2.g. Challenges and contingency plans: As in Aim 1, in the highly unlikely event that our VF judges’ ratings 
drift unacceptably, we will increase surveillance on inter- and intra-rater reliability and pull judges from analysis 
and retrain them to criterion, retest their reliability before placing them back online.   
A2.h. Sample size justification: The proposed approach is based on nonlinear modeling for which classical 
sample size calculation procedures are not suitable [125, 126]. As our augmented dataset is expected to contain 
more than 4000 (existing recordings)+700 (Aim 1) swallows, we strongly believe that it is a sufficient number of 
swallows as previous publications using similar nonlinear modeling techniques used data sets with a significantly 
lower number of samples [127-129].  Nonetheless, accounting for an intra-class correlation of 0.7 and a resulting 
design effect of 3.8 as in Aim 1, and assuming as many as 20 predictors will be needed in a linear model, we 
will be able to detect statistical significance of an R2 as small as 0.03 in the training sample and 0.06 in the 
validation sample with 80% power at α=0.05. 
AIM 3: Prospectively assess the accuracy of our HRCA system in predicting clinically significant 
dysphagia and aspiration in a randomized, controlled trial.    
A3.a. Introduction: For Aspirometer-based swallowing screening to be useful in clinical settings, it needs to 
outperform SIS in identifying patients at risk of silent aspiration and clinically significant dysphagia before they 
are placed at risk of aspiration pneumonia, or its further development is not warranted. The objective of this aim 
is to establish the superiority of dysphagia screening with the Aspirometer to the existing non-instrumental SIS 
protocols. To attain the objective of this aim, we will test the working hypothesis that the Aspirometer combined 
with, and independent of, SIS will identify VF-confirmed clinically significant OPD more accurately than SIS 
alone. We will use an approach that screens consenting patients with ordinary SIS and HRCA, and compare 
four levels of screening predictions (SIS human judgment criterion only, HRCA criterion only, SIS and HRCA 
criteria, SIS or HRCA criteria) for identifying dysphagia as measured from gold standard VF testing of all 
participants performed within 24 hours of screening. The rationale for this aim is that a clinically useful and 
accurate HRCA-based instrumental screening method not only needs to be rigorously tested against current 
practices to establish its validity and accuracy, but also needs to be examined for possible incorporation into 
current clinical practices. The acquisition of such knowledge is critical to the improved mitigation of unnecessary 
and adverse health sequelae associated with OPD. Based on our preliminary data, we expect that HRCA-based 
screening will be able to identify (sensitivity=) 90% or more of patients with OPD needing intervention or follow-
up with gold standard VF testing and (specificity=) 90% of those not needing VF testing using one of the 3 
strategies incorporating HRCA. This would validate the development of HRCA-based swallow screening tools 
that pre-emptively identify patients with a high 
pneumonia risk due to OPD, expedite 
intervention or gold standard swallowing 
diagnostic tests when necessary, and to reject 
the need for VF testing when unnecessary. 
A3.b. Justification and feasibility – Review 
of relevant literature: SIS should rely on well-
tested 
protocols 
that 
can 
detect 
true 
occurrences of OPD during screening, in 
comparison to subsequent gold-standard instrumental assessments [22, 130, 131, 132]. Though several SIS 
have been proposed and standardized in the literature (Table 3), previous SIS research contained confusing 
evidence [16, 21] by using different thresholds to define the presence of OPD. The most commonly reported 
screening metrics are sensitivity (the percentage of patients with an underlying problem, who are correctly 
Table 3 – Sensitivity and specificity of screening protocols for aspiration. 
SENS = Sensitivity; SPEC = Specificity; FPR = False Positive Rate; FNR = 
False Negative Rate.  
Test 
SENS 
SPEC 
FPR 
FNR 
Volume-Viscosity Screening Test 
100% 
29% 
72% 
0% 
Daniels Swallow Screen  
92% 
66% 
33% 
8% 
Gugging Swallow Screen  
100% 
50% 
50% 
0% 
Yale Swallow Protocol 
100% 
44-64% 
36-56% 
0% 
Contact PD/PI: Sejdic, Ervin
Research Strategy                                                                                             
Page 46

classified) and specificity (the percentage of patients without an underlying problem, who are correctly classified). 
When compared to blinded ratings of nonconcurrent instrumental exams, swallow screenings err on the side of 
over‐identifying aspiration risk, i.e., good sensitivity but poor specificity (see Table 3). While it may be argued 
that this bias in favor of sensitivity constitutes a conservative “safety net” for finding swallowing difficulties, it also 
results in unnecessary interventions for patients without significant OPD (Figure 3) including withholding oral 
dietary intake and diet texture restrictions, placement of feeding tubes, and unnecessary referrals for costly 
assessments, all of which affect quality of life and place financial and other burdens.  A screening protocol should 
be performed in ordinary settings by typical screening staff, and detect OPD accurately while incurring minimum 
risk for the patient. Our approach to this aim will produce results that can demonstrate superior identification of 
both people with and without OPD compared to prior OPD screening research which exhibits zero identification 
of silent aspiration and only about 60% identification of those 
without OPD.   
A3.c. Justification and feasibility – Preliminary studies: In our 
initial study [31], we collected time-linked HRCA signals and VF, 
using a protocol of three sips of thin liquid barium (5 cc each). The 
classification algorithms were trained to discriminate swallows 
with PAS scores of 1 and 2 (safe) from those with scores ≥ 3 
(penetration of material into the airway, above the level of the 
vocal cords, without clearance) and succeeded in this task at the 
level of the single bolus with 90% sensitivity and 77% specificity. 
When all the swallowing tasks performed by each patient were 
considered in aggregate, sensitivity for the detection of aspiration (i.e., anywhere in the protocol) improved to 
100%, but specificity fell to 54%. These details are summarized in Table 4. Next, Dr. Sejdic has used HRCA 
signals collected from 30 older adults [101] to develop a support vector machine classifier to distinguish between 
grossly safe and unsafe swallowing with high sensitivity (97.1 ± 2%) and modest specificity (64 ± 8.8%). Also, 
we used HRCA signals from 29 pediatric participants along with a support vector machine classifier to achieve 
an adjusted accuracy of 89.6% ± 0.9 for the discrimination between swallows with and without airway entry [102]. 
Most recently, Dr. Sejdic has used a Bayes’ machine learning approach to infer 
about the airway protection in 40 patients with dysphagia [105]. This was the first 
study that demonstrated that we can grossly differentiate healthy swallows (P-A 
scores < 3) from penetration-aspiration swallows (P-A scores 4-8) with an accuracy 
greater than 90%. These preliminary results demonstrate that we can reliably 
differentiate safe and unsafe swallowing events.  
A3.d. Experimental approach: The main goal of this aim is to demonstrate that the 
proposed HRCA tool is objective and can improve upon the accuracy of SIS alone 
in identifying participants who have OPD and need to undergo a VF exam or need 
immediate intervention (Figure 11). To achieve this goal, we will carry out the 
following experimental steps: STEP 1: Enroll 50 consenting patients eligible for SIS 
in the experiment, and perform combined SIS screening and an HRCA screening. 
STEP 2: The HRCA system will be applied to all patients. The dysphagia SIS screen 
employed in the University of Pittsburgh Medical Center Presbyterian University 
Hospital will be performed, which consists of two or three 5mL water swallows and 
drinking 90mL of water at a self-selected pace. STEP 3: Nurses or other staff (non-
SLPs or physicians) administering the screens and blinded to the HRCA output, will 
score the screens on the basis of the institutional SIS algorithm, denoting any signs 
of OPD or penetration/aspiration (e.g., wet voice, coughs) for each swallow that is 
observed. For the concurrent HRCA screening, we will record and denote the output 
of the algorithm for each observed swallow with findings from Aims 1 & 2. STEP 4: We will administer a VF exam 
for all patients using clinical protocols that include the same bolus conditions (5mL thin liquid, 30mL thin liquid) 
of swallowed material used in the SIS, within a 24-hour period to confirm the presence or absence of 
dysphagia/aspiration. STEP 5: The MBSImP, the PAS and the Normalized Residue Ratio Scale will be used to 
measure swallow impairment, airway protection and post-swallow pharyngeal residue. These measures will be 
made by our trained laboratory associates. STEP 6: HRCA, SIS, and VF results will be compared.           
Table 4 – HRCA-based screening can detect 
aspiration, validated against concurrent VF. SENS = 
Sensitivity; SPEC = Specificity; FPR = False Positive 
Rate; FNR = False Negative Rate; PB = Per bolus; 
PP = Per participant.  
Parameter 
Statistic 
PB 
PP 
Impaired 
Swallowing  
Safety 
(13/40 patients; 
55/154 swallows) 
SENS 
90% 
100% 
SPEC 
77% 
54% 
FPR 
23% 
48% 
FNR 
10% 
0% 
Figure 12 – Our proposed 
experimental and data 
analysis approach for Aim 3. 
Contact PD/PI: Sejdic, Ervin
Research Strategy                                                                                             
Page 47

A3.e. Data interpretation: We will fit a series of logistic regression models with presence/absence of clinically 
significant OPD/aspiration as the dichotomous dependent variable, and HRCA signatures and SIS, both 
individually and in combination, as the independent variables.  We will use areas under ROC curves (c-statistics) 
as measures of predictive accuracy, and compare them using DeLong’s method [133]. We anticipate that the c-
statistics for the 3 models involving HRCA will be significantly greater than that using SIS alone.     
A3.f. Expected outcomes: The benchmark for success of Aim 3 will be a successful completion of proposed 
testing of the HRCA-based screening in a clinical setting yielding accurate identifications of patients at risk of 
silent aspiration. Specifically, we will: (1) test the HRCA-based screening in clinical settings and denote its 
accuracy; (2) understand if confounding clinical factors can affect the deployment of the HRCA-based screening 
(not expected from our preliminary work). Successful completion of this aim will demonstrate that the Aspirometer 
(HRCA and advanced data analytics) can conveniently, noninvasively, and inexpensively produce robust 
predictions of OPD presence and absence that are measurably superior to SIS, and produce some diagnostic 
results currently possible only with VF testing.   
A3.g. Challenges and contingency plans: The preliminary results (see A3.c.) strongly support our hypothesis 
(see A3.a.). In the highly unlikely event that our judges’ PA or MBSImP score ratings drift unacceptably, we will 
increase surveillance on inter- and intra-rater reliability and pull judges from analysis and retrain them to criterion, 
retest their reliability before placing them back online.    
A3.h. Sample size justification: We plan to recruit 50 patients, with an anticipated effective sample size of 46 
after refusals. Assuming that approximately 50% of recruits will have clinically significant dysphagia/aspiration, 
a correlation of approximately 0.3 between SIS and HRCA, and an area under ROC curve of c=0.600 for SIS 
alone, with the proposed sample size, we will have 80% power to detect an increase in area under ROC curve 
as small as 0.240 at α=0.05 with one of the 3 strategies that involve HRCA [134, 135]. 
Project coordination and management: Dr. Sejdić will direct the study and will lead the analysis and algorithm 
testing components of 
the study. Dr. Coyle 
will lead the analysis of 
the VF images and 
clinical aspects of the 
study 
and 
be 
responsible for clinical 
dissemination of our 
results. 
Drs. 
Sejdić 
and Coyle have jointly 
or 
separately 
published over 25 publications describing how HRCA can be used as a reliable dysphagia tool. Dr. Subashan 
Perera, who has had a long successful collaboration with Dr. Sejdić, will support our proposal as a biostatistician. 
Hence, we have a unique set of skills and experiences to translate HRCA into a clinically useful tool for identifying 
patients in danger of imminent aspiration-related adverse events who require referral for comprehensive 
swallowing assessment (see Figure 13). Our results will be disseminated at international research meetings 
(e.g., Dysphagia Research Society) and manuscripts will be submitted to leading peer‐reviewed journals (e.g., 
Dysphagia). We also plan to make relevant portions of the data (e.g., tables reporting key signal parameters by 
etiological group) available to the dysphagia clinical and research community via the internet and publications. 
Sample signals and/or sample code may also be appropriate for sharing via our website (www.imedlab.org). 
Concluding remarks: Our engineering‐clinical partnership will successfully demonstrate that the HRCA-based 
tools can be used for timely detection and monitoring of silent aspiration and swallowing impairment, and reduce 
the morbidity and mortality associated with silent aspiration. Our future studies will investigate the potential use 
of the device for a preliminary diagnosis of swallowing difficulties, and as a biofeedback instrument for the 
treatment of OPD and airway protection disorders. Our team has the necessary expertise and knowledge to 
tackle any obstacles regarding advanced signal analysis and/or clinical aspects of the study as demonstrated 
through numerous dysphagia-related publications (e.g., [65-77]). Effectively, we are likely the only group of 
researchers in the world that can deliver an accurate OPD screening device within the next 5 years. 
Figure 13 – The timeline for the study. 
Contact PD/PI: Sejdic, Ervin
Research Strategy                                                                                             
Page 48

PROGRESS REPORT PUBLICATION LIST 
1. F. Movahedi, J. L. Coyle, E. Sejdić, "Deep belief networks for electroencephalography: A review of 
recent contributions and future outlooks" IEEE Journal of Biomedical and Health Informatics, accepted, 
2017. PMC Number - In Process 
2. F. Movahedi, A. Kurosu, J. L. Coyle, S. Perera, E. Sejdić “A comparison between swallowing sounds 
and vibrations in patients with dysphagia,” Computer Methods and Programs in Biomedicine, vol. 144, 
pp. 179–187, June 2017. PMCID: PMC5455149 
3. F. Movahedi, A. Kurosu, J. L. Coyle, S. Perera, E. Sejdić “Anatomical directional dissimilarities in tri-
axial swallowing accelerometry signals,” IEEE Transactions on Neural Systems and Rehabilitation 
Engineering, vol. 25, no. 5, pp. 447-458, May 2017. PMID: 27295677, DOI: 
10.1109/TNSRE.2016.2577882, PMC Number - In Process 
4. I. Jestrović, J. L. Coyle, E. Sejdić, “Differences in brain networks during consecutive swallows detected 
using an optimized vertex-frequency algorithm,” Neuroscience, vol. 344, pp. 113-123, Mar. 2017. 
PMCID: PMC5303679 
5. I. Jestrović, J. L. Coyle, E. Sejdić, “A fast algorithm for vertex-frequency representations of signals on 
graphs,” Signal Processing, vol. 131, pp. 438-491, Feb. 2017. PMCID: PMC5417719 
6. I. Jestrović, J. L. Coyle, S. Perera, E. Sejdić, “Functional connectivity patterns of normal human 
swallowing: difference among various viscosity swallows in normal and chin-tuck head positions,” Brain 
Research, vol. 1652, pp. 158-169, Dec. 2016. PMCID: PMC5102805 
7. J. M. Dudik, J. L. Coyle, A. El Jaroudi, M. Sun, E. Sejdić “A matched dual-tree wavelet denoising for tri-
axial swallowing vibrations,” Biomedical Signal Processing and Control, vol. 27, pp. 112-121, May 
2016. PMCID: PMC4853171 
8. E. Sejdić, F. Movahedi, Z. Zhang, A. Kurosu, J. L. Coyle, “The effects of compressive sensing on 
extracted features from tri-axial swallowing accelerometry signals,” in Proc. of SPIE – Compressive 
Sensing V: From Diverse Modalities to Big Data Analytics, vol. 9857, Baltimore, MD, USA, Apr 20-21, 
2016, pp. 985703-1- 985703-5. PMCID: PMC5042204 
9. J. M. Dudik, A. Kurosu, J. L. Coyle, E. Sejdić, “A statistical analysis of cervical auscultation signals from 
adults with unsafe airway protection,” Journal of NeuroEngineering and Rehabilitation, vol. 13, pp. 7-1-
10, 2016. PMCID: PMC4722771 
10. I. Jestrović, J. L. Coyle, E. Sejdić, “Decoding human swallowing via electroencephalography: a state-of-
the-art review,” Journal of Neural Engineering, vol. 12, no. 5, pp. 051001-1-15, October 2015. PMCID: 
PMC4596245 
11. J. M. Dudik, J. L. Coyle, E. Sejdić, “Dysphagia screening: Contributions of cervical auscultation signals 
and modern signal processing techniques,” IEEE Transactions on Human-Machine Systems, vol. 45, 
no. 4, pp. 465-477, August 2015. PMCID: PMC4511276 
12. I. Jestrović, J. L. Coyle, E. Sejdić, “Characterizing functional connectivity patterns during saliva 
swallows in different head positions,” Journal of NeuroEngineering and Rehabilitation, vol. 12, pp. 61-1-
11, July 2015. PMCID: PMC4513710 
13. J. M. Dudik, A. Kurosu, J. L. Coyle, E. Sejdić, “A comparative analysis of DBSCAN, k-means, and 
quadratic variation algorithms for automatic identification of swallowing from swallowing acceleromtry 
signals,” Computers in Biology and Medicine, vol. 59, pp. 10-18, Apr. 2015. PMCID: PMC4363248 
14. I. Jestrović, J. L. Coyle and E. Sejdić, “The effects of increased fluid viscosity on stationary 
characteristics of EEG signal in healthy adults” Brain Research, vol. 1589, pp. 45-53, Nov. 2014. 
PMCID: PMC4253861 
15. E. Sejdić, J. M. Dudik, A. Kurosu, I. Jestrović, J. L. Coyle, “Understanding differences between healthy 
swallows and penetration-aspiration swallows via compressive sensing of tri-axial swallowing 
accelerometry signals,” in Proc. of SPIE – Compressive Sensing III, vol. 9109, Baltimore, MD, USA, 
May 7-9, 2014, pp. 91090M-1-91090M-6. PMCID: PMC4201057 
 
Contact PD/PI: Sejdic, Ervin
Progress Report Publication List                                                                              
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