Dataset Overview
Definition：ECG Waveform Images and Diagnostic Reports Datasetis provided byChangsha Langhui Information Technology Co., Ltd.Large-scale electrocardiogram data collections obtained through compliance channels. Covers 5 million standard 12 contour electrocardiogram images and structured diagnostic reports, including automatic analysis and medical examination of diagnostic findings, resulting in high-quality training data for machine plus manual double-marked.
| Dataset Name | ECG Waveform Images and Diagnostic Reports Dataset |
| Total Data Volume | Five million cases of electrocardiograms. |
| Data Type | Image (12 Conectrone TNT) + text diagnostic report |
| Data Source | Resale (partnerships with more than 50 hospitals) |
| License Type | Sublicensing Including direct authorization from three Grade-A Tertiary Hospitals |
| Conduit Configuration | Standard 12 lead (I, II, III, AVR, AVL, AVF, V1-V6) |
| Quality Assurance | Autoanalysis of machines + medical examination of diagnosis, double-marked mechanism |
Core Data Fields
Each piece of data contains complete structured fields that cover everything from raw data to specialized diagnostics.
| Field Category | Field Count | Typical Fields |
|---|---|---|
| EKV images | 12 | 12 Console Boeroglyphic Images (I/II/III/aVR/aVL/aVF/V1-V6), Sample Rates, Gain Settings, Filter Parameters |
| Automatic analysis reports | 8 | Heart rate, PIR period, QRS width, QT/QTc period, electrical axis, automatic diagnostic findings (machine generation) |
| Doctor's approval of the diagnosis | 6 | Audit of diagnostic findings, tactocardia/room tremors/room shift/ ST paragraph lifting classification, level of doctor, date of review |
| Basic information on patients | 4 | Age group, gender, date of examination, clinical section (all de-identification) |
| 3 | Data quality level, noise level, false/disruption marking |
Data Sample Showcase
The following is a de-identified structured JSON sample illustrating the typical data format.
{
"ecg_id": "ECG-2023-00124589",
"meta": {"age_group": "40-50","gender": "male","department": "Cardiology"},
"auto_analysis": {"heart_rate": 72,"pr_interval": 160,"qrs_width": 88,"qtc": 410,"axis": 45,"auto_conclusion": "sample，sample"},
"doctor_review": {"conclusion": "sample，sample","abnormal_flag": false,"reviewer_level": "sample value"}
}AI Application Scenarios
E.C.I.A. autodiagnosis
Five million cases of double-marked training in depth learning models for telematics, automatic recognition and classification of common electrocardiogram anomalies such as tadpoles, room tremors, room early paces, ST section anomalies, etc.
Brain screening for heart disorders
Based on the 12 cosmological characterizations, an automated screening system for heart disorders is built, which is applied to the initial screening of large-scale electrocardiograms at medical centres and primary medical institutions.
I.D.A. Decision making
The AI model for integrating electrocardiogram diagnosis and clinical symptoms provides brain-based diagnostic advice to cardiologists and increases early recognition of acute disorders such as acute heart infarction.
Elementable equipment EK analysis
Standard 12 conductivity data are used to train and validate the abnormality algorithm for wearable equipment (smart watches, etc.) for single-conductor coupling electrons, which enhances the accuracy of consumer-grade electrocardiograms.
Quality assessment of the heart telecommunications
Quality label data are used to train the autometric mass assessment model of the heart telecommunications, automatically identify false and noise and improve the reliability of remote electrocardiogram monitoring.
Telecardiology platform
Large-scale electrocardiogram data support the construction of the AI engine of the remote electrocardiology platform, which provides automatic pre-determination and critical-value alerts for EKGs at the primary hospital.
Frequently asked questions
Domain expert network
Covering six major disciplines: mathematics, physics, chemistry, biology, earth sciences, and computer science
Academic credential review, professional competency assessment, data annotation training, and continuous quality monitoring
AI pre-annotation + expert review, 300% efficiency improvement, 99.5% accuracy
Need for customised medical data programs?
Changsha Langhui Information Technology Co., Ltd. The DataAssetsAPI platform is dedicated to providing high-quality, compliant medical data assets for AI companies and scientific research institutions. Cooperate with 50 + hospitals, including Fudan 100 type hospitals.
Data Sample Preview · Customized Data Solutions · Professional Technical Support