AIMC Topic: Electrocardiography

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

Scientific reports
This work presents an on-chip analog-to-information conversion technique that utilizes analog hyper-dimensional computing based on reservoir-computing paradigm to process electrocardiograph (ECG) signals locally in-sensor and reduce radio frequency t...

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

IEEE transactions on biomedical circuits and systems
This paper presents a neuromorphic processing system with a spike-driven spiking neural network (SNN) processor design for always-on wearable electrocardiogram (ECG) classification. In the proposed system, the ECG signal is captured by level crossing...

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

IEEE journal of biomedical and health informatics
With the popularity of the wireless body sensor network, real-time and continuous collection of single-lead electrocardiogram (ECG) data becomes possible in a convenient way. Data mining from the collected single-lead ECG waves has therefore aroused ...

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

Physiological measurement
This work presents an ECG classifier for variable leads as a contribution to the Computing in Cardiology Challenge/CinC Challenge 2021. It aims to integrate deep and classic machine learning features into a single model, exploring the proper structur...

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

Computers in biology and medicine
As the number of people suffering from cardiovascular diseases increases every year, it becomes essential to have an accurate automatic electrocardiogram (ECG) diagnosis system. Researchers have adopted different methods, such as deep learning, to in...

Machine Learning Methods in Predicting Patients with Suspected Myocardial Infarction Based on Short-Time HRV Data.

Sensors (Basel, Switzerland)
Diagnosis of cardiovascular diseases is an urgent task because they are the main cause of death for 32% of the world's population. Particularly relevant are automated diagnostics using machine learning methods in the digitalization of healthcare and ...

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

International heart journal
Left ventricular dilatation (LVD) and left ventricular hypertrophy (LVH) are risk factors for heart failure, and their detection improves heart failure screening. This study aimed to investigate the ability of deep learning to detect LVD and LVH from...

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

Journal of medical systems
A prediction algorithm for hypoglycemic events is proposed using glucose levels and electrocardiogram (ECG) with support vector machine (SVM). We extracted the corrected QT interval and five heart rate variability parameters from the ECG, along with ...

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

Computational and mathematical methods in medicine
The level of patient's illness is determined by diagnosing the problem through different methods like physically examining patients, lab test data, and history of patient and by experience. To treat the patient, proper diagnosis is very much importan...

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

Physiological measurement
We describe an automatic classifier of arrhythmias based on 12-lead and reduced-lead electrocardiograms. Our classifier comprises four modules: scattering transform (ST), phase harmonic correlation (PHC), depthwise separable convolutions (DSC), and a...