<i>b</i>-Value: A Potential Biomarker for Assessing Knee-Joint Health Using Acoustical Emission Sensing

Jeong, Hyeon; Whittingslow, Daniel C.; Inan, Omer T.

doi:10.1109/lsens.2018.2871981

Cited by 9 publications

(13 citation statements)

References 10 publications

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“…More work is needed to determine the precise mechanistic origin of these high frequency clicks, but we hypothesized that they occur due to increased internal friction in the joint, caused by the characteristic inflammation of the synovial membrane, breakdown of cartilage, and reduced joint space in JIA ( 3 , 22 ). Of note, similar clicks are apparent in the case of acute injury as was recently discovered by our work in a cadaver model of knee injury ( 19 ) and a similar study in an injured athlete model ( 23 ). Rather than relying strictly on one or even a few characteristics of these JAEs as was done in previous work, in this study we attempt to more thoroughly quantify the differences between the recorded JAEs.…”

Section: Discussionsupporting

confidence: 82%

Knee Acoustic Emissions as a Digital Biomarker of Disease Status in Juvenile Idiopathic Arthritis

Whittingslow

Zia

Gharehbaghi

et al. 2020

Front. Digit. Health

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In this paper, we quantify the joint acoustic emissions (JAEs) from the knees of children with juvenile idiopathic arthritis (JIA) and support their use as a novel biomarker of the disease. JIA is the most common rheumatic disease of childhood; it has a highly variable presentation, and few reliable biomarkers which makes diagnosis and personalization of care difficult. The knee is the most commonly affected joint with hallmark synovitis and inflammation that can extend to damage the underlying cartilage and bone. During movement of the knee, internal friction creates JAEs that can be non-invasively measured. We hypothesize that these JAEs contain clinically relevant information that could be used for the diagnosis and personalization of treatment of JIA. In this study, we record and compare the JAEs from 25 patients with JIA−10 of whom were recorded a second time 3–6 months later—and 18 healthy age- and sex-matched controls. We compute signal features from each of those record cycles of flexion/extension and train a logistic regression classification model. The model classified each cycle as having JIA or being healthy with 84.4% accuracy using leave-one-subject-out cross validation (LOSO-CV). When assessing the full JAE recording of a subject (which contained at least 8 cycles of flexion/extension), a majority vote of the cycle labels accurately classified the subjects as having JIA or being healthy 100% of the time. Using the output probabilities of a JIA class as a basis for a joint health score and test it on the follow-up patient recordings. In all 10 of our 6-week follow-up recordings, the score accurately tracked with successful treatment of the condition. Our proposed JAE-based classification model of JIA presents a compelling case for incorporating this novel joint health assessment technique into the clinical work-up and monitoring of JIA.

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Section: Discussionsupporting

confidence: 82%

Knee Acoustic Emissions as a Digital Biomarker of Disease Status in Juvenile Idiopathic Arthritis

Whittingslow

Zia

Gharehbaghi

et al. 2020

Front. Digit. Health

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“…The b-value of the acoustic emissions was calculated at each stage of testing. This metric represents the scaling of the amplitude distribution of the signal and was previously shown to be able to differentiate between knee statuses (17). This paper represents the first time that an analysis of knee acoustic emissions has been performed on a controlled, cadaver model with incorporation of anatomical complexity, confounding physiological factors that occur in an injured state (i.e.…”

Section: Introductionmentioning

confidence: 99%

Acoustic Emissions as a Non-invasive Biomarker of the Structural Health of the Knee

et al. 2019

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The longitudinal assessment of joint health is a long-standing issue in the management of musculoskeletal injuries. The acoustic emissions (AEs) produced by joint articulation could serve as a biomarker for joint health assessment, but their use has been limited by a lack of mechanistic understanding of their creation. In this paper, we investigate that mechanism using an injury model in human lower-limb cadavers, and relate AEs to joint kinematics. Using our custom joint sound recording system, we recorded the AEs from 9 cadaver legs in four stages: at baseline, after a sham surgery, after a meniscus tear, and post-meniscectomy. We compare the resulting AEs using their b-values. We then compare joint anatomy/kinematics to the AEs using the x-ray reconstruction of moving morphology (XROMM) technique. After the meniscus tear the number and amplitude of the AE peaks greatly increased from baseline and sham (b-value =1.33±0.15; p<0.05). The XROMM analysis showed a close correlation between the minimal inter-joint distances (0.251±0.082 cm during extension, 0.265±.003 during flexion, at 145°) and a large increase in the AEs. This work provides key insight into the nature of joint AEs, and details a novel technique and analysis for recording and interpreting these biosignals.

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“…Frequency content below 10 Hz was removed to account for baseline wander of the accelerometer signal caused by coarse movement of the limb during the leg press task. This filtering approach is distinct from that of other work such as Reference [16], in which the primary goal was to capture large-amplitude, high-bandwidth peaks (“clicks” of the joint) in the acoustic signal. In those studies, air microphones offset from the skin surface were used to record the joint sounds instead of contact microphones placed against tissue; in such a scenario, the low-frequency, low-energy acoustic waves would be greatly attenuated at the skin–air interface, so their contribution was not considered.…”

Section: Methodsmentioning

confidence: 99%

A Glove-Based Form Factor for Collecting Joint Acoustic Emissions: Design and Validation

Bolus

Jeong

Whittingslow

et al. 2019

Sensors

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Sounds produced by the articulation of joints have been shown to contain information characteristic of underlying joint health, morphology, and loading. In this work, we explore the use of a novel form factor for non-invasively acquiring acoustic/vibrational signals from the knee joint: an instrumented glove with a fingertip-mounted accelerometer. We validated the glove-based approach by comparing it to conventional mounting techniques (tape and foam microphone pads) in an experimental framework previously shown to reliably alter healthy knee joint sounds (vertical leg press). Measurements from healthy subjects (N = 11) in this proof-of-concept study demonstrated a highly consistent, monotonic, and significant (p < 0.01) increase in low-frequency signal root-mean-squared (RMS) amplitude—a straightforward metric relating to joint grinding loudness—with increasing vertical load across all three techniques. This finding suggests that a glove-based approach is a suitable alternative for collecting joint sounds that eliminates the need for consumables like tape and the interface noise associated with them.

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b-Value: A Potential Biomarker for Assessing Knee-Joint Health Using Acoustical Emission Sensing

Cited by 9 publications

References 10 publications

Knee Acoustic Emissions as a Digital Biomarker of Disease Status in Juvenile Idiopathic Arthritis

Knee Acoustic Emissions as a Digital Biomarker of Disease Status in Juvenile Idiopathic Arthritis

Acoustic Emissions as a Non-invasive Biomarker of the Structural Health of the Knee

A Glove-Based Form Factor for Collecting Joint Acoustic Emissions: Design and Validation

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