Large Scale Arrhythmia Database (LSAD)

Overview

The large scale arrhythmia database (LSAD) is a large-scale, multi-center, multi-lead, and multi-class ECG dataset for arrhythmia detection. The dataset contains ECG recordings from 45,152 patients. The dataset is collected from 3 different centers: Shaoxing People’s Hospital, the Second Affiliated Hospital of Zhejiang University, and the First Affiliated Hospital of Zhejiang University. The dataset contains 11 different arrhythmia classes and 1 normal class. The dataset is collected from 12-lead ECGs and is annotated by a team of expert cardiologists. The dataset includes over 100 scp codes.

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Usage

Python

from pathlib import Path
import helia_edge as helia
import heartkit as hk

ds = hk.DatasetFactory.get('lsad')(
    path=Path("./datasets/lsad")
)

# Download dataset
ds.download(force=False)

# Create signal generator
data_gen = self.ds.signal_generator(
    patient_generator=helia.utils.uniform_id_generator(ds.patient_ids, repeat=True, shuffle=True),
    frame_size=256,
    samples_per_patient=5,
    target_rate=100,
)

# Grab single ECG sample
ecg = next(data_gen)

Statistics

Acronym Name	Full Name	Frequency, n(%)	Age, Mean ± SD	Male,n(%)
SB	Sinus Bradycardia	15,528 (38.6)	58.4 ± 14.02	9844 (63.4%)
SR	Sinus Rhythm	7,291 (18.1)	54.38 ± 16.17	4107 (56.33%)
AFIB	Atrial Fibrillation	7,028 (17.5)	73.07 ± 11.27	4051 (57.64%)
ST	Sinus Tachycardia	6,208 (15.4)	54.24 ± 21.41	3208 (51.68%)
AFL	Atrial Flutter	1,725 (4.3)	71.57 ± 13.23	1001 (58.03%)
SI	Sinus Irregularity	1,773 (4.4)	37.3 ± 22.98	979 (55.22%)
SVT	Supraventricular Tachycardia	542 (1.3)	55.44 ± 18.41	289 (53.32%)
AT	Atrial Tachycardia	133 (0.3)	65.92 ± 18.7	69 (51.88%)
AVNRT	Atrioventricular Node Reentrant Tachycardia	16 (0.03)	57.88 ± 17.34	12 (75%)
AVRT	Atrioventricular Reentrant Tachycardia	7 (0.01)	56.43 ± 17.89	5 (71.43%)
WAP	Wandering Atrial Pacemaker	7 (0.01)	51.14 ± 31.83	6 (85.71%)

Funding

This dataset received funding from the Kay Family Foundation Data Analytic Grant. This dataset received funding from 2018 Shaoxing Medical and Hygiene Research Grant, ID 2018C30070.

License

The dataset is available under Creative Commons Attribution 4.0 International Public License