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Principal Investigator: Youngsun Kong
Organization: UNIVERSITY OF CONNECTICUT STORRS
Fiscal Year: 2024
Award: $80,556
Funding agency: National Institute of Dental and Craniofacial Research
Project Summary
We propose to diagnose and differentiate dental pain from stress using a wearable electrodermal
activity (EDA) device. Dental caries is one of the major causes of endodontic infection in the pulp or
soft tissues of the tooth, causing hypersensitivity, severe pain, and even death. It is estimated that at
least 2 billion adults and 520 million children suffer from dental caries worldwide. EDA has shown the
capability to objectively assess dental pain since it measures the electrical conductance of the skin due
to innervation of the sympathetic nervous system (SNS). However, EDA has two fundamental issues.
First, stress can also elicit an EDA response via the SNS, and stress commonly presents in dental
clinics due to pain anticipation and previous experience. Second, EDA analysis relies on knowing the
onset and end timepoints of a stimulus and when the subject feels pain on the affected teeth, which is
difficult to obtain from patients with communication issues. Skin nerve activity (SKNA), recorded via
ECG at high sampling rates (>4 kHz), has been shown to represent the dynamics of the SNS with
precise start and end times of nerve firing, which can be used to align events in the EDA signal for
subsequent EDA analysis. Therefore, we propose to use the skin nerve activity (SKNA) signal, derived
from the ECG, to precisely locate the onset and end of noxious stimuli. We will also apply and validate
a machine learning model to detect and differentiate EDA segments affected by stress from pain during
pulpal diagnosis. As both pain and stress increase amplitudes of EDA signals, albeit less with the latter,
it is crucial to develop a quantitative approach to differentiate stress from pain in EDA signals. Therefore,
we will investigate SKNA and EDA signals from datasets collected during dental examination to
separate stress from pain response so that more accurate assessment of pain due to noxious stimuli
during dental examination can be made. Our approach combining SKNA and EDA will allow
autonomous and more accurate segmentation of the data that are specific to pain and stress, which
will be especially useful for patients with communication issues, and ultimately will lead to more
accurate assessment of pain.
Terms: <0-11 years old><21+ years old><Address><Adult><Adult Human><Affect><Allergy><Amygdala><Amygdaloid Body><Amygdaloid Nucleus><Amygdaloid structure><Analgesia Tests><Analgesic Agents><Analgesic Drugs><Analgesic Preparation><Analgesics><Anodynes><Antinociceptive Agents><Antinociceptive Drugs><Assessment instrument><Assessment tool><Bacteria><Blood Vessels><Body Tissues><Canalis Radicis Dentis><Caries><Cell Communication and Signaling><Cell Signaling><Cessation of life><Child><Child Youth><Children (0-21)><Clinical><Communication><Communication challenge><Communication difficulty><Comprehension><Cotton Plant><Cues><Data><Data Set><Death><Dental><Dental Clinics><Dental Decay><Dental Pulp><Dental caries><Dentin><Detection><Devices><Diagnosis><E-stim><ECG><EKG><Electric Stimulation><Electrocardiogram><Electrocardiography><Electrodermal Response><Endodontic Inflammation><Endodontics><Esthesia><Event><Fingers><Galvanic Skin Response><Gossypium><Human><Hypersensitivity><Infection><Intracellular Communication and Signaling><Local anesthesia><Location><Measures><Modeling><Modern Man><Necrosis><Necrotic><Nerve><Nerve Endings><Nerve Fibers><Nervous System Physiology><Neurologic function><Neurological function><Nociception Tests><Odontalgia><Odontoblasts><Outcome><Pain><Pain Assessment><Pain Measurement><Pain measure><Painful><Parasympathetic Nervous System><Patients><Psychogalvanic Reflex><Pulp Canals><Pulpitis><Root Canal><Root Tip><Sampling><Sensation><Signal Transduction><Signal Transduction Systems><Signaling><Skin><Skin Electric Conductance><Stimulus><Stress><Swab><Sympathetic Nervous System><Testing><Time><Tissues><Tooth><Tooth Tissue><Tooth structure><Toothache><Training><Work><accurate diagnosis><adulthood><amygdaloid nuclear complex><assess effectiveness><biological adaptation to stress><biological signal transduction><cotton><dental pain><dentalgia><determine effectiveness><effective therapy><effective treatment><effectiveness assessment><effectiveness evaluation><electrostimulation><evaluate effectiveness><examine effectiveness><experience><improved><indexing><innervation><kids><machine learned algorithm><machine learning algorithm><machine learning based algorithm><machine learning based model><machine learning model><nerve supply><nervous system function><pain assay><pain killer><pain medication><pain reliever><pain sensation><painful sensation><painkiller><patient response><patient specific response><periapical><pulp><pulpal blood flow><pulpal neurogenic inflammation><reaction; crisis><response><responsive patient><sensor><skin conductance><soft tissue><stress response><stress; reaction><teeth><tooth decay><tooth pain><vascular><wearable><wearable device><wearable electronics><wearable system><wearable technology><wearable tool><wearables><youngster>