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Related Concept Videos

Pulse rhythm01:30

Pulse rhythm

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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.
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Holter Monitor: 24-Hour Monitoring01:23

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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...
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Dysrhythmias V: Evaluating Dysrhythmias01:30

Dysrhythmias V: Evaluating Dysrhythmias

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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...
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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.
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Disturbances in Heart Rhythm01:29

Disturbances in Heart Rhythm

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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.
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Cardiomyopathy III: Hypertrophic Cardiomyopathy01:29

Cardiomyopathy III: Hypertrophic Cardiomyopathy

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Hypertrophic cardiomyopathy, or HCM, is an autosomal dominant genetic disorder characterized by asymmetric left ventricular hypertrophy without ventricular dilation. It is more common in men and is typically diagnosed in young, athletic adults.EtiologyHCM is primarily genetic and is caused by mutations in genes encoding sarcomeric proteins. Researchers have identified over 1400 mutations across at least 11 different genes. Among these, the most frequently occurring mutations are found in the...
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Related Experiment Video

Updated: Apr 15, 2026

Patient Directed Recording of a Bipolar Three-Lead Electrocardiogram using a Smartwatch with ECG Function
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AI-Enabled Sensor Technologies for Remote Arrhythmic Monitoring in High-Risk Cardiomyopathy Genotypes.

Nardi Tetaj1,2, Andrea Segreti1,2, Francesco Piccirillo1,2

  • 1Cardiology Unit, Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo 200, 00128 Rome, Italy.

Sensors (Basel, Switzerland)
|April 14, 2026
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) sensor technology offers remote arrhythmic monitoring for inherited cardiomyopathies. This approach promises proactive prevention of sudden cardiac death by analyzing continuous data, but requires genotype-specific validation.

Keywords:
FLNCLMNAPLNRBM20arrhythmiasartificial intelligencedesmosomal genesgenetic cardiomyopathyimplantable devicesremote monitoringwearable sensors

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Area of Science:

  • Cardiology
  • Biomedical Engineering
  • Artificial Intelligence

Background:

  • Inherited cardiomyopathies carry high risk for malignant ventricular arrhythmias and sudden cardiac death, often unrelated to traditional markers.
  • Current surveillance methods are inadequate for detecting silent electrical instability in these high-risk patients.
  • Genotype-specific risk stratification is crucial for understanding disease progression.

Purpose of the Study:

  • To review the role of artificial intelligence (AI)-enabled sensor technologies in remote arrhythmic monitoring for inherited cardiomyopathy patients.
  • To evaluate the potential of continuous electrophysiological and hemodynamic data acquisition for early detection of arrhythmic vulnerability.
  • To identify challenges and future directions for implementing AI-driven sensing in genotype-specific cohorts.

Main Methods:

  • Narrative review of emerging AI-enabled sensor technologies, including wearable ECGs, implantable monitors, and multisensor devices.
  • Analysis of how AI enhances signal processing, automated event detection, and remote data triage.
  • Examination of digital biomarkers derived from continuous data streams for early detection of arrhythmias and decompensation.

Main Results:

  • AI-enabled sensors provide continuous electrophysiological and hemodynamic data, generating digital biomarkers for early arrhythmic risk.
  • AI analytics can improve signal processing and automated event detection, potentially reducing clinical workload.
  • Current evidence is limited, with most studies in general heart failure or arrhythmia populations, necessitating genotype-specific validation.

Conclusions:

  • AI-enabled sensing combined with genotype information offers a promising shift towards proactive, precision-guided arrhythmic prevention in inherited cardiomyopathies.
  • Further research, including genotype-focused studies and standardized digital endpoints, is essential for safe and effective clinical implementation.
  • Addressing challenges like algorithm generalizability and data integration is key to realizing the full potential of these technologies.