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Electrocardiogram

Electrocardiogram

An electrocardiogram (ECG or EKG) is a critical diagnostic tool that records the electrical signals produced by the heart during each heartbeat. This recording is achieved through electrodes placed strategically on the arms, legs, and chest. The electrocardiograph amplifies these signals and produces 12 distinct tracings, offering a comprehensive understanding of the heart's electrical activity.
Three major waveforms are present in a typical ECG recording: the P wave, the QRS complex, and...

Electrocardiogram Fundamentals

Electrocardiogram Fundamentals

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...

Nursing Diagnosis

Nursing Diagnosis

Following assessment, a nursing diagnosis is the next step in the nursing process. It begins after the nurse has collected and recorded the patient data. The purpose of diagnosing is to identify how the client responds to actual or potential health processes, identify factors that bestow or that cause health problems, the etiologies, and identify resources or strengths the individual, group, or community can draw on to prevent or resolve problems.
The nursing diagnosis focuses on evidence-based...

Avoidance Learning and Learned Helplessness

Avoidance Learning and Learned Helplessness

Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...

Documentation of Nursing Diagnosis

Documentation of Nursing Diagnosis

The nurse documents nursing diagnoses and enters them into the patient record. The identified patient's nursing diagnosis is either written out with a plan of care or entered into the electronic health record.
In some settings, data-driven computerized decision support systems are in place, allowing for more accurate nursing diagnoses. The database within one of these systems includes diagnostic labels defining characteristics, activities, and indicators for nursing. A nurse enters...

Formulating and Validating Nursing Diagnosis I

Formulating and Validating Nursing Diagnosis I

A nursing diagnosis is written when the nurse recognizes a cluster of essential patient data indicating health problems treated with independent nursing interventions. The standardized terminologies of a nursing diagnosis help nurses identify and treat patients' problems. Every electronic health record that uses nursing diagnosis must employ standard diagnostic terminology. Developing an efficient, individualized care plan begins with accurate nursing diagnoses.
There are thirteen domains...

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A new deep learning model for assisted diagnosis on electrocardiogram.

Eric Ke Wang¹, Liu Xi¹, Rui Pei Sun¹

¹Harbin Institute of Technology, Shenzhen, 518055, China.

Mathematical Biosciences and Engineering : MBE

|May 30, 2019

Summary

A new deep learning model, CBRNN, improves electrocardiogram (ECG) analysis accuracy for clinical diagnosis. This advanced model outperforms existing methods and human judgment, aiding in initial ECG screening.

Keywords:

bi-directional recurrent neural network clinical medicine convolutional neural network electrocardiogram multi-lead

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Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

Area of Science:

Medical Informatics
Artificial Intelligence in Medicine
Cardiology

Background:

Computer-aided diagnosis of electrocardiograms (ECG) requires enhanced accuracy.
Deep learning models offer potential for improving ECG analysis.
Current models may not fully capture complex ECG features.

Purpose of the Study:

To develop and validate a novel deep learning model, CBRNN, for accurate computer-aided ECG analysis.
To improve the diagnostic accuracy of ECG interpretation in clinical settings.
To provide a tool for efficient first-round screening of ECG examinations.

Main Methods:

A hybrid deep learning model, CBRNN, combining Convolutional Neural Network (CNN) and Bi-directional Recurrent Neural Network (BRNN).
CNN extracts lead-specific ECG features using one-dimension convolution.
BRNN fuses multi-lead features for deeper representation.
Model trained on over 40,000 training and 19,000 validation ECG datasets.

Main Results:

The CBRNN model achieved an 87.69% accuracy rate on over 120,000 real-world ECG data.
Performance surpassed popular deep learning models like CNN and ResNet.
Accuracy was slightly higher than the average human judgment in ECG interpretation.

Conclusions:

The CBRNN model demonstrates superior accuracy for computer-aided ECG analysis.
It is effective for the initial screening of ECG examinations in clinical practice.
CBRNN shows promise in assisting medical professionals for more efficient and accurate diagnoses.