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相关概念视频

Disturbances in Heart Rhythm01:28

Disturbances in Heart Rhythm

857
Arrhythmia or dysrhythmia refers to an abnormal heart rhythm caused by a defect in the heart's conduction system. It can cause the heart to beat irregularly, too quickly, or too slowly, leading to symptoms like chest pain, shortness of breath, and fainting. Factors such as stress, caffeine, alcohol, nicotine, cocaine, certain drugs, congenital defects, diseases, and electrolyte abnormalities can trigger arrhythmias.
Arrhythmias are categorized by their speed, rhythm, and origin. A slow...
857
ECG Interpretation of Arrhythmias I: Sinus Arrhythmias01:16

ECG Interpretation of Arrhythmias I: Sinus Arrhythmias

164
Arrhythmias are disturbances in the heart's rhythm that lead to abnormal heartbeats. These irregularities can originate from different parts of the heart and are classified based on their origin and nature.
Types of Arrhythmias
Sinus Node Arrhythmias
Sinus Bradycardia: Originating from the sinoatrial (SA) node, sinus bradycardia involves slower impulses, resulting in a heart rate of less than 60 beats per minute (bpm). Causes include sleep, vagal stimulation, beta-blockers, hypothyroidism,...
164
Mechanism of Cardiac Arrhythmias01:28

Mechanism of Cardiac Arrhythmias

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Arrhythmias are irregular heart rhythms occurring when the heart's electrical impulses become abnormal. These disturbances can lead to various symptoms, depending on their severity and the underlying cause. Some common factors contributing to arrhythmias include hypoxia, ischemia, electrolyte imbalances, excessive catecholamine exposure, drug toxicity, and muscle overstretching. Arrhythmias can be classified into two main types based on the rate and site of origin of abnormal heart rhythms.
871
Pulse rhythm01:30

Pulse rhythm

750
Pulse rhythm refers to the pattern of pulsations within specific intervals, offering valuable insights into the regularity or irregularity of the heart's beats as observed through the pattern of pulsation within specific intervals. A regular pulse exhibits a consistent heart rate with uniform waveforms and pulsation force, variations of which can be classified as normal, weak, or bounding.
Conversely, an irregular pulse pattern is termed dysrhythmia, stemming from disruptions in cardiac...
750
Electrocardiogram Fundamentals01:28

Electrocardiogram Fundamentals

474
Introduction
An electrocardiogram (ECG) is a diagnostic tool for identifying cardiac conditions such as arrhythmias, conduction abnormalities, and myocardial ischemia.
Definition
An electrocardiogram (ECG) visualizes the heart's electrical activity by tracing the electrical movement associated with each heartbeat on a graph or monitor. As the heart beats, an electrical wave passes through it, correlating with the cardiac cycle events.
Parts of an ECG
An ECG utilizes electrodes on the skin...
474
Electrophysiology of Normal Cardiac Rhythm01:19

Electrophysiology of Normal Cardiac Rhythm

1.9K
The normal cardiac rhythm is a synchronized electrical activity that facilitates the regular and coordinated contraction of the heart muscle. This process is essential for efficient blood circulation throughout the body. The fundamental elements involved in establishing and maintaining this rhythm include the unique electrical properties of cardiac muscle cells, the sinoatrial (SA) node's pacemaker function, the specialized conducting system, and the ionic mechanisms underlying each phase...
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一个小的变压器用于微控制器上的低功率心律失常分类.

Paola Busia, Matteo Antonio Scrugli, Victor Jean-Baptiste Jung

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    此摘要是机器生成的。

    一个微小的变压器模型使用最小的参数准确地检测出来自心电图信号的常见心律失常. 这种高效的方法适用于实时可穿戴心血管监测.

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    科学领域:

    • 生物医学工程 生物医学工程
    • 机器学习 机器学习
    • 心血管健康 心血管健康

    背景情况:

    • 可穿戴系统对于持续监测心血管疾病至关重要.
    • 变压器模型对实时心电图 (ECG) 信号分析和心律失常检测有希望.
    • 在低功耗可穿戴设备上高效地实施这些模型带来了重大挑战.

    研究的目的:

    • 开发一个用于ECG分析的紧型变压器模型.
    • 为了在检测常见的心律失常类别中获得高精度.
    • 确保在可穿戴应用的低功耗微控制器上有效部署.

    主要方法:

    • 设计了一个具有6k参数的微小变压器模型用于ECG信号分析.
    • 该模型使用基于增强的方法进行训练,以增强对运动工件的强度.
    • 在MIT-BIH心律失常数据库上使用8位整数推断来评估性能,并部署在GAP9处理器上.

    主要成果:

    • 该模型在识别5种常见的心律失常类别时实现了98.97%的准确性.
    • 考虑到噪音,在最坏的情况下,部署后的准确性为98.36%.
    • 在GAP9处理器上推断需要4.28ms,消耗0.09mJ,证明了高效率.

    结论:

    • 拟议的微型变压器模型为可穿戴系统的实时心律失常检测提供了一个高度准确和高效的解决方案.
    • 基于增强的培训提高了模型的稳定性,这对于实际部署可穿戴设备至关重要.
    • 该模型的低复杂性和功耗使其非常适合集成到超低功率微控制器中,用于持续的心血管监测.