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Related Concept Videos

Heart Failure IV: Classification and Diagnostic Evaluation01:30

Heart Failure IV: Classification and Diagnostic Evaluation

Heart failure can be classified in various ways, with the most common classifications based on physical activity limitations, disease progression, severity, and treatment strategies.The Functional Classification of Heart Failure divides patients into four categories based on physical activity limitation due to symptom burden.Class I: Patients in this class have cardiac disease but no physical activity limitations. Ordinary activities like walking, climbing stairs, or routine tasks do not cause...
Heart Failure V: Medical Management01:30

Heart Failure V: Medical Management

Medical Management of Acute Decompensated Heart Failure (ADHF)The primary goals of therapy for patients hospitalized with acute decompensated heart failure (ADHF) include:Relieving symptomsOptimizing volume statusSupporting oxygenation and ventilationMaintaining cardiac output (CO) and end-organ perfusionIdentifying and addressing the cause of ADHFPreventing complicationsProviding patient education on factors precipitating HF exacerbationPlanning for dischargeOngoing monitoring and assessment...
Heart Failure III: Clinical Manifestations01:26

Heart Failure III: Clinical Manifestations

Heart failure (HF) manifests primarily as dyspnea, fatigue, and fluid retention, resulting in peripheral and pulmonary edema. Symptoms may vary depending on which ventricle is more affected, left or right.Left-Sided Heart FailureAlso known as left ventricular failure, this condition results from the left ventricle's inability to fill or eject sufficient blood into the systemic circulation. It leads to pulmonary congestion, which occurs when the left ventricle fails to eject blood effectively...
Heart Failure I: Introduction01:27

Heart Failure I: Introduction

Heart failure refers to a clinical syndrome caused by structural or functional cardiac disorders that prevent the heart from pumping an adequate amount of blood to meet the body's metabolic needs. This condition often arises from myocardial infarction or ischemia, leading to decreased cardiac output, reduced tissue perfusion, impaired gas exchange, fluid volume imbalance, and decreased functional ability.Heart failure can result from disruptions in the mechanisms that regulate cardiac output...
Heart Failure II: Pathophysiology01:29

Heart Failure II: Pathophysiology

Systolic Heart Failure and Compensatory MechanismsSystolic heart failure (also termed HFrEF, Heart Failure with Reduced Ejection Fraction) is the most prevalent type of heart filure. It results in a decreased volume of blood being pumped from the ventricle. The aortic arch and carotid sinuses have baroreceptors that detect reduced blood pressure, triggering the sympathetic nervous system (SNS) to release epinephrine and norepinephrine. Initially, this response aims to boost heart rate and...
Pathophysiology of Heart Failure01:17

Pathophysiology of Heart Failure

Heart failure (HF) is a progressive syndrome involving ventricles that leads to inadequate cardiac output. It can be classified based on location and output or ejection fraction. Ejection fraction (EF) is an essential measurement in the diagnosis and surveillance of HF. Reduced EF corresponds to systolic heart failure (HFrEF). However, HF with preserved ejection fraction (HFpEF) is becoming increasingly prevalent. Also known as diastolic HF, this form of HF is related to aging. The...

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Related Experiment Videos

Voice features and deep learning models for identifying acute decompensated heart failure.

Jieun Lee1, Gwantae Kim2, Insung Ham2

  • 1Division of Cardiology, Department of Internal Medicine, Korea University Guro Hospital, Seoul, Republic of Korea.

Digital Health
|June 1, 2026
PubMed
Summary

Voice analysis can detect acute decompensated heart failure (ADHF) by identifying changes in vocal characteristics between illness and recovery. This non-invasive method shows promise as a new biomarker for ADHF detection.

Keywords:
acute decompensationbiomarkerdeep learningheart failurevoice analysis

Related Experiment Videos

Area of Science:

  • Cardiology
  • Biomedical Engineering
  • Artificial Intelligence

Background:

  • Acute decompensated heart failure (ADHF) presents as systemic congestion requiring hospitalization.
  • Early, non-invasive detection of ADHF decompensation is a significant clinical challenge.
  • Voice characteristics may serve as a potential non-invasive biomarker for ADHF.

Purpose of the Study:

  • To investigate differences in voice characteristics between ADHF patients during hospitalization and after recovery.
  • To assess the feasibility of using voice analysis and deep learning for ADHF detection.

Main Methods:

  • Prospective enrollment of 100 ADHF patients requiring hospitalization.
  • Exclusion of patients with confounding conditions (e.g., infection, vocal cord disease).
  • Voice recordings at admission (ADHF state) and discharge (recovered state).
  • Extraction of low-level audio features and Mel-spectrograms for deep learning models.
  • Evaluation using area under the receiver operating characteristic curve (AUC).

Main Results:

  • Harmonics-to-noise ratio and Shimmer showed potential in distinguishing between decompensated and recovered states.
  • The ConvNeXt-large deep learning model achieved a mean AUC of 0.83 at the patient-state level.
  • Segment-level analysis yielded higher discriminative performance with AUC up to 0.87.

Conclusions:

  • Significant differences in voice characteristics exist between ADHF admission and discharge states.
  • Deep learning-based voice analysis can effectively differentiate between acute decompensated and recovered states in ADHF patients.
  • Voice analysis offers a promising novel, non-invasive approach for ADHF detection.