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Related Concept Videos

Heart Sounds01:15

Heart Sounds

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Heart sounds are generated by the turbulence in blood flow due to the closing of heart valves. These sounds are best perceived slightly away from the valves, where the blood flow disseminates the sound.
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The heart wall comprises three distinct layers: the epicardium, myocardium, and endocardium. The outermost layer, the epicardium, is the visceral layer of the serous pericardium, featuring a thin, transparent mesothelial surface and an inner layer of areolar connective tissue with fat deposits that increase with age.
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Location and Orientation of the Heart01:13

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The human heart, despite its modest size and weight, is an organ of remarkable strength and endurance. Roughly the size of a fist, the heart weighs between 250 and 350 grams and is nestled within the mediastinum, the medial cavity of the thorax. It extends obliquely for about 12 to 14 cm, resting on the superior surface of the diaphragm. The heart is positioned anterior to the vertebral column and posterior to the sternum, with two-thirds of its mass lying to the left of the midsternal line.
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Neural Control of Respiration01:18

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The neural regulation of respiration is a meticulously coordinated process primarily controlled by the respiratory centers located within the brainstem. These centers, composed of specialized neurons, transmit nerve impulses that control the contraction and relaxation of our respiratory muscles.
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Anatomy of the Heart01:27

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The human heart is made up of three layers of tissue that are surrounded by the pericardium, a membrane that protects and confines the heart. The outermost layer, closest to the pericardium, is the epicardium. The pericardial cavity separates the pericardium from the epicardium. Beneath the epicardium is the myocardium, the middle layer, and the endocardium, the innermost layer. There are four chambers of the heart: the right atrium, the right ventricle, the left atrium, and the left ventricle.
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Overview of the Heart01:07

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The heart, a muscular organ located in the chest, functions as the body's pump, circulating blood through the vascular system. It has four chambers: two atria on top and two ventricles below. The right atrium receives deoxygenated blood from the body and passes it to the right ventricle, which pumps it to the lungs for oxygenation. The left atrium receives oxygenated blood from the lungs and transfers it to the left ventricle, which pumps it to the rest of the body.
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Semi-automated Optical Heartbeat Analysis of Small Hearts
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Learning Representations from Heart Sound: A Comparative Study on Shallow and Deep Models.

Kun Qian1,2, Zhihao Bao1,2, Zhonghao Zhao1,2

  • 1Key Laboratory of Brain Health Intelligent Evaluation and Intervention, Ministry of Education (Beijing Institute of Technology), Beijing 100081, China.

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|March 5, 2024
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Summary
This summary is machine-generated.

This study benchmarks artificial intelligence models for analyzing heart sounds using the Heart Sounds Shenzhen corpus. It introduces new tasks and provides reproducible results for normal versus abnormal heart sound classification.

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Area of Science:

  • Cardiology
  • Biomedical Engineering
  • Artificial Intelligence

Background:

  • Automatic analysis of heart sounds is crucial for cardiovascular health monitoring.
  • Previous research faced challenges due to a lack of standardized, open-access datasets.
  • Inconsistent data collection and annotation hindered fair comparison of different studies.

Purpose of the Study:

  • To introduce and benchmark the Heart Sounds Shenzhen (HSS) corpus for heart sound analysis.
  • To redefine heart sound classification tasks for improved research comparability.
  • To comprehensively investigate shallow and deep learning models for heart sound analysis.

Main Methods:

  • Segmenting heart sound recordings into 10-second clips, mimicking human auscultation.
  • Implementing both 3-class (normal, moderate, mild/severe) and binary (normal, abnormal) classification tasks.
  • Benchmarking classic machine learning and state-of-the-art deep learning models using open-source toolkits.

Main Results:

  • Detailed benchmarks of various machine learning and deep learning models were established.
  • Reproducible results were achieved using open-source tools, facilitating further research.
  • Feature contributions for optimal model performance were analyzed for interpretability.

Conclusions:

  • The study provides a robust benchmark for heart sound analysis using the HSS corpus.
  • Redefined tasks and comprehensive model investigation advance the field of automated cardiac auscultation.
  • The findings contribute to more reliable and interpretable AI-driven cardiovascular diagnostics.