Arrhythmias

CNN and SVM-Based Models for the Detection of Heart Failure Using Electrocardiogram Signals.

Heart failure (HF) is a serious condition in which the heart fails to supply the body with enough oxygen and nutrients to function normally. Early and accurate detection of heart failure is critical for impeding disease progression. An electrocardiogram (ECG) is a test that records the rhythm and electrical activity of the heart and is used to detect HF. It is used to look for irregularities in th...

Deep learning model based on contrast-enhanced ultrasound for predicting early recurrence after thermal ablation of colorectal cancer liver metastasis.

OBJECTIVES: To develop and validate a deep learning (DL) model based on quantitative analysis of contrast-enhanced ultrasound (CEUS) images that predicts early recurrence (ER) after thermal ablation (TA) of colorectal cancer liver metastasis (CRLM).

Development and validation of deep learning ECG-based prediction of myocardial infarction in emergency department patients.

Myocardial infarction diagnosis is a common challenge in the emergency department. In managed settings, deep learning-based models and especially conv...

Prediction of the Presence of Ventricular Fibrillation From a Brugada Electrocardiogram Using Artificial Intelligence.

BACKGROUND: Brugada syndrome is a potential cause of sudden cardiac death (SCD) and is characterized by a distinct ECG, but not all patients with A Br...

Kenichi Harumi Plenary Address at Annual Meeting of the International Society of Computers in Electrocardiology: "What Should ECG Deep Learning Focus on? The diagnosis of acute coronary occlusion!".

According to the STEMI paradigm, only patients whose ECGs meet STEMI criteria require immediate reperfusion. This leads to reperfusion delays and sign...

A novel deep learning package for electrocardiography research.

. In recent years, deep learning has blossomed in the field of electrocardiography (ECG) processing, outperforming traditional signal processing metho...

Detection of arrhythmia in 12-lead varied-length ECG using multi-branch signal fusion network.

Automatic detection of arrhythmia based on electrocardiogram (ECG) plays a critical role in early prevention and diagnosis of cardiovascular diseases....

In-sensor neural network for high energy efficiency analog-to-information conversion.

This work presents an on-chip analog-to-information conversion technique that utilizes analog hyper-dimensional computing based on reservoir-computing...

A Neuromorphic Processing System With Spike-Driven SNN Processor for Wearable ECG Classification.

This paper presents a neuromorphic processing system with a spike-driven spiking neural network (SNN) processor design for always-on wearable electroc...

[Robot-assisted Mediastinal Surgery].

Because of the many important anatomical structures located closely together at very small distances, mediastinal surgery has been traditionally deman...

Percutaneous thermal ablation for cT1 renal mass in solitary kidney: A multicenter trifecta comparative analysis versus robot-assisted partial nephrectomy.

INTRODUCTION: Renal cell carcinoma (RCC) in solitary kidney (SK) represents a challenging scenario. We sought to compare outcomes of robot-assisted pa...

DDCNN: A Deep Learning Model for AF Detection From a Single-Lead Short ECG Signal.

With the popularity of the wireless body sensor network, real-time and continuous collection of single-lead electrocardiogram (ECG) data becomes possi...

Deep-Learning-Based Fast Optical Coherence Tomography (OCT) Image Denoising for Smart Laser Osteotomy.

Laser osteotomy promises precise cutting and minor bone tissue damage. We proposed Optical Coherence Tomography (OCT) to monitor the ablation process ...

Ensemble classification combining ResNet and handcrafted features with three-steps training.

This work presents an ECG classifier for variable leads as a contribution to the Computing in Cardiology Challenge/CinC Challenge 2021. It aims to int...

A novel P-QRS-T wave localization method in ECG signals based on hybrid neural networks.

As the number of people suffering from cardiovascular diseases increases every year, it becomes essential to have an accurate automatic electrocardiog...

Automatic Detection of Left Ventricular Dilatation and Hypertrophy from Electrocardiograms Using Deep Learning.

Left ventricular dilatation (LVD) and left ventricular hypertrophy (LVH) are risk factors for heart failure, and their detection improves heart failur...

A Prediction Algorithm for Hypoglycemia Based on Support Vector Machine Using Glucose Level and Electrocardiogram.

A prediction algorithm for hypoglycemic events is proposed using glucose levels and electrocardiogram (ECG) with support vector machine (SVM). We extr...

Investigation of Applying Machine Learning and Hyperparameter Tuned Deep Learning Approaches for Arrhythmia Detection in ECG Images.

The level of patient's illness is determined by diagnosing the problem through different methods like physically examining patients, lab test data, an...

Deep Learning Model for Predicting Rhythm Outcomes after Radiofrequency Catheter Ablation in Patients with Atrial Fibrillation.

Current guidelines on atrial fibrillation (AF) emphasized that radiofrequency catheter ablation (RFCA) should be decided after fully considering its p...

Arrhythmia classification of 12-lead and reduced-lead electrocardiograms via recurrent networks, scattering, and phase harmonic correlation.

We describe an automatic classifier of arrhythmias based on 12-lead and reduced-lead electrocardiograms. Our classifier comprises four modules: scatte...