Article

Hardware Aspects of Machine Learning-Based Cardiac Fibrillation Diagnosis

2026-07-10

Abstract excerpt

This paper presents the hardware aspects of a Hjorth-parameter and deep neural network 2 (DNN)–based cardiac fibrillation diagnosis pipeline targeting low-power Internet of Medi- 3 cal Things (IoMT) devices and ASIC implementations. Building on earlier edge-to-cloud 4 studies where Hjorth activity, mobility, and complexity were computed in software on 5 microcontrollers and classified by a floating-point DNN, we i...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 45586514-15ce-5371-98cb-48ab487bb739
DOI: 10.20944/preprints202607.0734.v1

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Hardware Aspects of Machine Learning-Based Cardiac Fibrillation DiagnosisDOI 10.20944/preprints202607.0734.v1Read

AFibNet: An Implementation of Atrial Fibrillation Detection With Convolutional Neural NetworkSemantic proximity Read

A Fast, Lightweight, and Generalizable Deep Neural Network for the Detection of Atrial FibrillationSemantic proximity Read

Lightweight Transformer Models for Biomedical Signal Processing: Trends, Challenges, and Future DirectionsSemantic proximity Read

Lightweight Deep Learning for Atrial Fibrillation Detection from Single-Lead Wearable ECG: A Systematic Benchmark of Convolutional, Temporal, and State-Space ArchitecturesSemantic proximity Read

Machine Learning, Deep Learning, and Hybrid Approaches for ECG-Based Cardiac Arrhythmia Detection: Techniques, Challenges, and Future PerspectivesSemantic proximity Read

AI-Enhanced ECG Intelligence: Optimized Deep Learning Pipelines for Early and Reliable Detection of Cardiac DisordersSemantic proximity Read

Evaluation of Cascade Forests towards Interpretable ECG Arrhythmia Classification: Performance, Interpretability, and Deployment Trade-offsSemantic proximity Read

Cardiac abnormality detection with a tiny diagonal state space model based on sequential liquid neural processing unitsSemantic proximity Read

CTRhythm: Accurate Atrial Fibrillation Detection from Single-Lead ECG by Convolutional Neural Network and Transformer IntegrationSemantic proximity Read

Automated Heart Disease Detection Using Swin Transformer and ECG Signal Processing: A High-Accuracy ApproachSemantic proximity Read

Prospective Validation of a Deep Learning Model to Detect Structural Heart Disease from Apple Watch ECGs: The WATCH-SHD StudySemantic proximity Read

Artificial Intelligence Driven Software Systems for Cardiovascular Disease Detection Using Physiological Signals: A Systematic ReviewSemantic proximity Read