Jove
Visualize
お問い合わせ
JoVE
x logofacebook logolinkedin logoyoutube logo
JoVEについて
概要リーダーシップブログJoVEヘルプセンター
著者向け
出版プロセス編集委員会範囲と方針査読よくある質問投稿
図書館員向け
推薦の声購読アクセスリソース図書館諮問委員会よくある質問
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experimentsアーカイブ
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教員リソースセンター教員サイト
利用規約
プライバシーポリシー
ポリシー

関連する概念動画

ECG Interpretation of Arrhythmias II: Atrial, Junctional and Ventricular Arrhythmias01:25

ECG Interpretation of Arrhythmias II: Atrial, Junctional and Ventricular Arrhythmias

574
Arrhythmia is a condition characterized by an irregular heart rhythm, with ECG changes that differ based on its origin and nature. The types of arrhythmias discussed below include atrial, junctional, and ventricular arrhythmias.Atrial ArrhythmiasPremature Atrial Complexes (PACs): PACs are early atrial beats caused by stress, caffeine, alcohol, electrolyte imbalances, hypoxia, hyperthyroidism, or certain medications (e.g., bronchodilators and decongestants). The ECG shows early P waves with an...
574
ECG Interpretation of Arrhythmias I: Sinus Arrhythmias01:16

ECG Interpretation of Arrhythmias I: Sinus Arrhythmias

840
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,...
840
Mechanism of Cardiac Arrhythmias01:28

Mechanism of Cardiac Arrhythmias

2.0K
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.
2.0K
Machines01:19

Machines

579
Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. One example of a machine is the cutting plier, which is used to cut wires by applying forces to its handles. When equal and opposite forces are exerted on the handles of the cutting plier, they cause the cutting edges to come together and apply equal and opposite reaction forces on the wire, which are greater than the applied forces.
A free-body diagram of the...
579
Predicting Molecular Geometry02:27

Predicting Molecular Geometry

46.0K
VSEPR Theory for Determination of Electron Pair Geometries
46.0K
Machines: Problem Solving II01:30

Machines: Problem Solving II

672
Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
672

こちらも読む

関連記事

共著者、ジャーナル、引用グラフによってこの研究に関連する記事。

並び替え
Same author

Clinical Patterns and Appropriateness of Apixaban Dosing in Patients With Atrial Fibrillation.

JACC. Advances·2026
Same author

Predictors of ischemic stroke and major bleeding among patients with atrial fibrillation in clinical practice.

American heart journal·2026
Same author

Self-supervised contrastive learning enables robust electrocardiogram-based cardiac classification.

Heart rhythm O2·2026
Same author

Uncertainty quantification of conduction velocity in models of cardiac spread of activation.

Medical & biological engineering & computing·2026
Same author

Hemodynamic Consequences and Clinical Outcomes With Intravenous Lidocaine Infusion in Patients With Atrial Fibrillation.

Journal of cardiovascular electrophysiology·2026
Same author

Institutional Factors and Shared Decision-Making for Atrial Fibrillation.

JAMA network open·2026

関連する実験動画

Updated: Feb 6, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.8K

心筋虚血における心室性不整脈の機械学習を用いた予測

Anna Busatto1,2,3, Jake A Bergquist1,2,3, Tolga Tasdizen1,4

  • 1Scientific Computing and Imaging Institute, University of Utah, SLC, UT, USA.

Computing in cardiology
|February 5, 2026
PubMed
まとめ

心筋梗塞後の心室性不整脈の予測は極めて重要である。本研究では、Long Short-Term Memory (LSTM)ネットワークを用いて心室性期外収縮 (PVC) を予測し、患者の転帰改善に有望であることを示している。

キーワード:
心室性不整脈心筋虚血機械学習Long Short-Term Memory (LSTM) network心電図予測モデリング

さらに関連する動画

A Research Method For Detecting Transient Myocardial Ischemia In Patients With Suspected Acute Coronary Syndrome Using Continuous ST-segment Analysis
18:11

A Research Method For Detecting Transient Myocardial Ischemia In Patients With Suspected Acute Coronary Syndrome Using Continuous ST-segment Analysis

Published on: December 28, 2012

24.8K
Improved Rodent Model of Myocardial Ischemia and Reperfusion Injury
07:23

Improved Rodent Model of Myocardial Ischemia and Reperfusion Injury

Published on: March 7, 2022

7.3K

関連する実験動画

Last Updated: Feb 6, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.8K
A Research Method For Detecting Transient Myocardial Ischemia In Patients With Suspected Acute Coronary Syndrome Using Continuous ST-segment Analysis
18:11

A Research Method For Detecting Transient Myocardial Ischemia In Patients With Suspected Acute Coronary Syndrome Using Continuous ST-segment Analysis

Published on: December 28, 2012

24.8K
Improved Rodent Model of Myocardial Ischemia and Reperfusion Injury
07:23

Improved Rodent Model of Myocardial Ischemia and Reperfusion Injury

Published on: March 7, 2022

7.3K

科学分野:

  • 心臓病学
  • 計算生物学
  • 機械学習

背景:

  • 心室性不整脈は心筋虚血の重篤な合併症です。
  • 従来の予測モデルは、複雑な時間的データに課題を抱えています。
  • 不整脈の正確な予測は、患者の転帰を大幅に改善する可能性があります。

研究 の 目的:

  • 次の心室性期外収縮 (PVC) までの時間を予測するためのLong Short-Term Memory (LSTM)ネットワークの開発と評価。
  • 不整脈予測のための高解像度心電図データを処理する上でのLSTMの有効性の評価。

主な方法:

  • 11件の大型動物実験から得られた高解像度心電図の解析。
  • 1832件の心室性期外収縮 (PVC) の同定とPVCまでの時間の計算。
  • 10件の実験でLSTMモデル (入力247、隠れユニット1024) をトレーニングし、保持された1件の実験でテストしました。

主要な成果:

  • LSTMモデルは、検証データにおいて8.6秒の平均絶対誤差 (MAE) を達成しました。
  • モデルは、68.5の損失で135秒のテストMAEを示しました。
  • 散布図は、強い検証相関とテスト結果における正の傾向を示しました。

結論:

  • Long Short-Term Memory (LSTM)ネットワークは、心筋虚血の文脈における心室性期外収縮 (PVC) の予測に可能性を示しています。
  • このアプローチは、従来のモデルと比較して不整脈管理のためのより効果的な方法を提供する可能性があります。
  • 臨床応用に向けて、この予測モデルを検証および改良するためのさらなる研究が必要です。