Depressed patients talk differently when they are depressed compared to when they are well. But it is hard to actually measure what the differences are. The study team will record voice samples from patients with mood disturbances, like depression, over the course of their receiving an electroconvulsive therapy (ECT) series. The study team will try and measure or quantify exactly what has changed in their speech and voice. The study team will choose ECT as it is one of the most effective and rapid treatment for depression. The study team will use a service provided by a company, NeuroLex, who has complex computer programs (artificial intelligence, AI) to analyze the voice samples.
Study Type
OBSERVATIONAL
Enrollment
11
3 voice recording tasks and 1 text entry task will be performed.
Medical University of South Carolina
Charleston, South Carolina, United States
Acoustic feature: zero crossing rate
crossings per second
Time frame: Throughout a course of electroconvulsive therapy (ECT) which may last between 2 and 7 weeks.
Acoustic feature: energy and entropy
decibels
Time frame: Throughout a course of electroconvulsive therapy which may last between 2 and 7 weeks.
Acoustic feature: spectral centroid, spectral spread, spectral entropy, spectral flux, spectral rolloff
hertz
Time frame: Throughout a course of electroconvulsive therapy which may last between 2 and 7 weeks.
Acoustic feature: Mel-Frequency Cepstral Coefficients, Chroma Vectors, and Chroma Deviation
unitless
Time frame: Throughout a course of electroconvulsive therapy which may last between 2 and 7 weeks.
Linguistic features: question ratio, filler ratio, number ratio, type token ratio
unitless ratio
Time frame: Throughout a course of electroconvulsive therapy which may last between 2 and 7 weeks.
Linguistic features: verb frequency, noun frequency, pronoun frequency, adverb frequency, adjective frequency, particle frequency, conjunction frequency, pronoun frequency
percentage
Time frame: Throughout a course of electroconvulsive therapy which may last between 2 and 7 weeks.
Linguistic features: standardized word entropy
decibels/log(total word count)
Time frame: Throughout a course of electroconvulsive therapy which may last between 2 and 7 weeks.
Linguistic features: Brunets index
W (lexical richness)
Time frame: Throughout a course of electroconvulsive therapy which may last between 2 and 7 weeks.
Linguistic features: Honores statistic
R (lexical richness)
Time frame: Throughout a course of electroconvulsive therapy which may last between 2 and 7 weeks.
Linguistic features: rate of speech
words per minute
Time frame: Throughout a course of electroconvulsive therapy which may last between 2 and 7 weeks.
Meta-features: fatigue
Machine learning approach to evaluate binary outcome: fatigued or awake
Time frame: Throughout a course of electroconvulsive therapy which may last between 2 and 7 weeks.
Meta-features: audio quality
Machine learning approach to evaluate binary outcome: bad or good
Time frame: Throughout a course of electroconvulsive therapy which may last between 2 and 7 weeks.
Meta-features: sentiment
Machine learning approach to evaluate binary outcome: sad or happy
Time frame: Throughout a course of electroconvulsive therapy which may last between 2 and 7 weeks.
Meta-features: stress
Machine learning approach to evaluate binary outcome: stressed or not stressed
Time frame: Throughout a course of electroconvulsive therapy which may last between 2 and 7 weeks.
Meta-features: gender
Machine learning approach to evaluate binary outcome: male or female
Time frame: Throughout a course of electroconvulsive therapy which may last between 2 and 7 weeks.
Meta-features: accent
Machine learning approach to evaluate a categorical outcome of accent region: england, indian, australian, etc.
Time frame: Throughout a course of electroconvulsive therapy which may last between 2 and 7 weeks.
Meta-features: length
seconds
Time frame: Throughout a course of electroconvulsive therapy which may last between 2 and 7 weeks.
Meta-features: age
Machine learning approach to evaluate estimated decade-age: 10s, 20s, 30s, 40s, 50s, 60s, 70s, 80s, 90s, etc.
Time frame: Throughout a course of electroconvulsive therapy which may last between 2 and 7 weeks.
Comparing the voice feature(s) with greatest statistically significant change to Patient Health Questionnaire (PHQ)-9 scores
The voice feature(s) found to have changed most significantly will be compared to Patient Health Questionnaire-9 scores which approach a total score that is less than 8, indicative of reduced depressive symptoms throughout Electroconvulsive Therapy
Time frame: Throughout a course of electroconvulsive therapy which may last between 2 and 7 weeks.
Acoustic Feature Specific Changes within and across sessions
Generalized mixed linear model will be used to evaluate which acoustic features change with P value threshold of \<0.05
Time frame: Throughout a course of electroconvulsive therapy which may last between 2 and 7 weeks.
Linguistic Feature Specific Changes within and across sessions
Generalized mixed linear model will be used to evaluate which linguistic features change with P value threshold of \<0.05
Time frame: Throughout a course of electroconvulsive therapy which may last between 2 and 7 weeks.
Meta-Feature Specific Changes within and across sessions
Generalized mixed linear model will be used to evaluate which meta features change with P value threshold of \<0.05
Time frame: Throughout a course of electroconvulsive therapy which may last between 2 and 7 weeks.
Acoustic Feature Specific Changes between sessions
Acoustic voice features will be evaluated using continuous averages using one sample t-tests and we will be testing the difference over time to a null hypothesis of 0 (for no change) in the one sample t-test.
Time frame: Throughout a course of electroconvulsive therapy which may last between 2 and 7 weeks.
Linguistic Feature Specific Changes between sessions
Linguistic voice features will be evaluated using continuous averages using one sample t-tests and we will be testing the difference over time to a null hypothesis of 0 (for no change) in the one sample t-test.
Time frame: Throughout a course of electroconvulsive therapy which may last between 2 and 7 weeks.
Meta-Feature Specific Changes between sessions
Meta-voice features will be evaluated using continuous averages using one sample t-tests and we will be testing the difference over time to a null hypothesis of 0 (for no change) in the one sample t-test.
Time frame: Throughout a course of electroconvulsive therapy which may last between 2 and 7 weeks.
Patient Chart Review Data: Age
years
Time frame: Throughout a course of electroconvulsive therapy which may last between 2 and 7 weeks.
Patient Chart Review Data: inpatient/outpatient status
binary: inpatient or outpatient
Time frame: Throughout a course of electroconvulsive therapy which may last between 2 and 7 weeks.
Patient Chart Review Data: gender
male, female, unspecified
Time frame: Throughout a course of electroconvulsive therapy which may last between 2 and 7 weeks.
Patient Chart Review Data: race
african american, caucasian, hispanic, asian american, etc.
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Time frame: Throughout a course of electroconvulsive therapy which may last between 2 and 7 weeks.
Patient Chart Review Data: Psychiatric diagnosis for ECT
Diagnosis indicated for receiving ECT
Time frame: Throughout a course of electroconvulsive therapy which may last between 2 and 7 weeks.
Patient Chart Review Data: PHQ-9 score at each session
Total (0-27) on 9 question scale
Time frame: Throughout a course of electroconvulsive therapy which may last between 2 and 7 weeks.
Patient Chart Review Data: Psychiatric hospitalizations
Number
Time frame: Throughout a course of electroconvulsive therapy which may last between 2 and 7 weeks.
Patient Chart Review Data: Past Psychiatric Medication Trials
Number
Time frame: Throughout a course of electroconvulsive therapy which may last between 2 and 7 weeks.
Patient Chart Review Data: Current Psychiatric Medication Trials
Number
Time frame: Throughout a course of electroconvulsive therapy which may last between 2 and 7 weeks.
Patient Chart Review Data: Classes of Current Psychiatric Medications
Categorical: Sedative, Selective Serotonin Reuptake Inhibitor, etc.
Time frame: Throughout a course of electroconvulsive therapy which may last between 2 and 7 weeks.
Patient Chart Review Data: Suicidal Ideation at ECT consult
Binary: yes/no
Time frame: Throughout a course of electroconvulsive therapy which may last between 2 and 7 weeks.
Patient Chart Review Data: Past Suicide Attempts
Number
Time frame: Throughout a course of electroconvulsive therapy which may last between 2 and 7 weeks.
Patient Chart Review Data: Psychiatric Review of Systems
Descriptive, categorical
Time frame: Throughout a course of electroconvulsive therapy which may last between 2 and 7 weeks.
Patient Chart Review Data: Family Psychiatric History
Categorical regarding psychiatric diagnoses of family members
Time frame: Throughout a course of electroconvulsive therapy which may last between 2 and 7 weeks.
Patient Chart Review Data: Number of non-psychiatric medical diagnoses
Number
Time frame: Throughout a course of electroconvulsive therapy which may last between 2 and 7 weeks.
Patient Chart Review Data: Past non-psychiatric medication trials
Number
Time frame: Throughout a course of electroconvulsive therapy which may last between 2 and 7 weeks.
Patient Chart Review Data: Current non-psychiatric Medications
Number
Time frame: Throughout a course of electroconvulsive therapy which may last between 2 and 7 weeks.
Patient Chart Review Data: Tobacco use history
Binary (yes/no)
Time frame: Throughout a course of electroconvulsive therapy which may last between 2 and 7 weeks.
Patient Chart Review Data: Tobacco use pack year
pack-year (total years smoked\*average packs per day)
Time frame: Throughout a course of electroconvulsive therapy which may last between 2 and 7 weeks.
Patient Chart Review Data: Prior ECT treatment
Binary (yes/no)
Time frame: Throughout a course of electroconvulsive therapy which may last between 2 and 7 weeks.
Patient Chart Review Data: total # of prior ECT treatments for response in the past
Numerical
Time frame: Throughout a course of electroconvulsive therapy which may last between 2 and 7 weeks.
Patient Chart Review Data: Prior transcranial magnetic stimulation (TMS) treatment
Binary (yes/no)
Time frame: Throughout a course of electroconvulsive therapy which may last between 2 and 7 weeks.
Patient Chart Review Data: Prior response to ECT or TMS
Categorical
Time frame: Throughout a course of electroconvulsive therapy which may last between 2 and 7 weeks.
Patient Chart Review Data against voice features
We will evaluate voice features with statistically significant changes against respective participant chart review data using a general linear model with a clustering component for repeated measures which may also be applied to contrast statements evaluating overall change over time (from baseline to end).
Time frame: Throughout a course of electroconvulsive therapy which may last between 2 and 7 weeks.
Generating ROC curves: stress
Clinically determined stress level (mood) will be compared to meta-feature extractions of stressed vs. not stressed from voice recordings will be used to calculate an area under the receiver operating curve (AUOC)
Time frame: Throughout a course of electroconvulsive therapy which may last between 2 and 7 weeks.
Generating ROC curves: fatigue
Clinically determined fatigue (motor) will be compared to meta-feature extractions of fatigued vs. awake from voice recordings will be used to calculate an area under the receiver operating curve (AUOC)
Time frame: Throughout a course of electroconvulsive therapy which may last between 2 and 7 weeks.
Generating ROC curves: sentiment
Clinically determined sentiment (affect and mood) will be compared to meta-feature extractions of happy vs. sad from voice recordings will be used to calculate an area under the receiver operating curve (AUOC)
Time frame: Throughout a course of electroconvulsive therapy which may last between 2 and 7 weeks.