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Related Concept Videos

Pulse rhythm01:30

Pulse rhythm

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 muscle...
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Disturbances in Heart Rhythm

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 heart...
Holter Monitor: 24-Hour Monitoring01:23

Holter Monitor: 24-Hour Monitoring

Holter monitoring is a continuous electrocardiography (ECG) recording that tracks the heart's electrical activity over an extended period, generally 24 to 48 hours. This noninvasive diagnostic tool detects irregular heart rhythms that may not be captured during a standard ECG performed in a clinical setting.DeviceThe Holter monitor is a portable, small device connected to several electrodes on the patient's chest. These electrodes detect the heart's electrical signals and transmit them to the...
Dysrhythmias IV: Characteristics of Bradyarrhythmias01:18

Dysrhythmias IV: Characteristics of Bradyarrhythmias

Bradyarrhythmias are cardiac rhythm disorders characterized by a slower-than-normal heart rate, typically defined as fewer than 60 beats per minute. Some of which are discussed here:Sinus BradycardiaSinus bradycardia presents a heart rate lower than 60 beats per minute, with a regular rhythm originating from the SA node. The ECG typically shows normal P waves preceding each QRS complex, a normal PR interval (0.12 to 0.20 seconds), and a normal QRS duration (0.06 to 0.10 seconds).First-Degree AV...
Dysrhythmias V: Evaluating Dysrhythmias01:30

Dysrhythmias V: Evaluating Dysrhythmias

Dysrhythmias, also known as arrhythmias, are disturbances in the heart's rhythm that range from benign to life-threatening. A thorough evaluation is crucial for appropriate management and involves a comprehensive medical history, physical examination, and various diagnostic tests.Medical HistorySymptoms: Collect detailed information on palpitations, dizziness, syncope, chest pain, and fatigue. Note their onset, frequency, and triggers.Previous Cardiac Issues: Document any history of heart...
Dysrhythmias VI: Management of Dysrhythmias01:25

Dysrhythmias VI: Management of Dysrhythmias

Dysrhythmia management involves a multifaceted approach, incorporating pharmacological treatments, medical procedures, surgical interventions, lifestyle modifications, and patient education.Pharmacological ManagementAntiarrhythmic Drugs:Class I (Sodium Channel Blockers): This class includes quinidine and procainamide, which reduce the speed of impulse conduction in the heart, stabilize the cardiac membrane, and control arrhythmias. Quinidine and procainamide are Class IA agents that prolong the...

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Related Experiment Video

Updated: May 19, 2026

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Machine Learning- and Deep Learning-Based Myoelectric Control System for Upper Limb Rehabilitation Utilizing EEG and

Tala Zaim1, Sara Abdel-Hadi1, Rana Mahmoud1

  • 1Department of Electrical Engineering, Qatar University, Doha 2713, Qatar.

Bioengineering (Basel, Switzerland)
|February 26, 2025
PubMed
Summary
This summary is machine-generated.

Machine learning and deep learning enhance upper limb rehabilitation by improving myoelectric control systems. These advanced techniques decode motor intentions using electroencephalography and electromyography signals for better assistive device function.

Keywords:
EEGEMGarmdeep learningdisabilitiesdisabilitymachine learningupper limb

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Area of Science:

  • Biomedical Engineering
  • Neurorehabilitation
  • Artificial Intelligence in Healthcare

Background:

  • Upper limb disabilities significantly impair daily activities and quality of life.
  • Effective rehabilitation technologies are vital for restoring motor function.
  • Myoelectric-controlled systems offer a promising avenue for assistive rehabilitation.

Purpose of the Study:

  • To systematically review machine learning and deep learning applications in myoelectric upper limb rehabilitation.
  • To analyze the use of electroencephalography and electromyography signals in these systems.
  • To identify advancements and challenges in decoding motor intentions for assistive devices.

Main Methods:

  • Systematic literature review of studies published between January 2015 and July 2024.
  • Inclusion of fourteen studies focusing on myoelectric-controlled upper limb rehabilitation.
  • Analysis of machine learning and deep learning models, including LSTM, SVM, and CNN.

Main Results:

  • Machine learning and deep learning models demonstrate enhanced accuracy and efficiency in myoelectric control.
  • Specific models like Long Short-Term Memory Networks, Support Vector Machines, and Convolutional Neural Networks show improved control precision.
  • Non-invasive signal acquisition integrated with computational models boosts rehabilitation device performance.

Conclusions:

  • Advanced computational models show significant potential for improving upper limb rehabilitation outcomes.
  • Challenges in model robustness, computational complexity, and real-time application need further research.
  • Bridging the gap between experimental technologies and practical clinical applications is essential for future advancements.