torch_ecg.databases#

This module contains ECG database readers.

Base classes #

`PhysioNetDataBase`(db_name[, db_dir, ...])	Base class for readers for PhysioNet database.
`NSRRDataBase`(db_name[, db_dir, working_dir, ...])	Base class for readers for the NSRR database.
`CPSCDataBase`(db_name[, db_dir, working_dir, ...])	Base class for readers for the CPSC database.
`PSGDataBaseMixin`()	A mixin class for PSG databases.

`AFDB`([db_dir, working_dir, verbose])	MIT-BIH Atrial Fibrillation Database
`ApneaECG`([db_dir, working_dir, verbose])	Apnea-ECG Database - The PhysioNet Computing in Cardiology Challenge 2000
`CINC2017`([db_dir, working_dir, verbose])	AF Classification from a Short Single Lead ECG Recording -- The PhysioNet Computing in Cardiology Challenge 2017
`CINC2018`([db_dir, working_dir, verbose])	You Snooze You Win - The PhysioNet Computing in Cardiology Challenge 2018
`CINC2020`([db_dir, working_dir, verbose])	Classification of 12-lead ECGs: the PhysioNet/Computing in Cardiology Challenge 2020
`CINC2021`([db_dir, working_dir, verbose])	Will Two Do? Varying Dimensions in Electrocardiography: The PhysioNet/Computing in Cardiology Challenge 2021
`LTAFDB`([db_dir, working_dir, verbose])	Long Term AF Database
`LUDB`([db_dir, working_dir, verbose])	Lobachevsky University Electrocardiography Database
`MITDB`([db_dir, working_dir, verbose])	MIT-BIH Arrhythmia Database
`QTDB`([db_dir, working_dir, verbose])	QT Database
`PTBXL`(db_dir[, working_dir, verbose, ...])	PTB-XL, a large publicly available electrocardiography dataset

`CPSC2018`([db_dir, working_dir, verbose])	The China Physiological Signal Challenge 2018: Automatic identification of the rhythm/morphology abnormalities in 12-lead ECGs.
`CPSC2019`([db_dir, working_dir, verbose])	The 2nd China Physiological Signal Challenge (CPSC 2019): Challenging QRS Detection and Heart Rate Estimation from Single-Lead ECG Recordings
`CPSC2020`([db_dir, working_dir, verbose])	The 3rd China Physiological Signal Challenge 2020: Searching for Premature Ventricular Contraction (PVC) and Supraventricular Premature Beat (SPB) from Long-term ECGs
`CPSC2021`([db_dir, working_dir, verbose])	The 4th China Physiological Signal Challenge 2021: Paroxysmal Atrial Fibrillation Events Detection from Dynamic ECG Recordings

SHHS([db_dir, working_dir, verbose])

Sleep Heart Health Study

`BeatAnn`(index, symbol)	Dataclass for beat annotation.
`DataBaseInfo`(title, about, usage, references)	A dataclass to store the information of a database.
`list_databases`()	List the names of all database reader classes available in this module (base classes excluded).
`WFDB_Beat_Annotations`	dict() -> new empty dictionary dict(mapping) -> new dictionary initialized from a mapping object's (key, value) pairs dict(iterable) -> new dictionary initialized as if via: d = {} for k, v in iterable: d[k] = v dict(**kwargs) -> new dictionary initialized with the name=value pairs in the keyword argument list. For example: dict(one=1, two=2).
`WFDB_Non_Beat_Annotations`	dict() -> new empty dictionary dict(mapping) -> new dictionary initialized from a mapping object's (key, value) pairs dict(iterable) -> new dictionary initialized as if via: d = {} for k, v in iterable: d[k] = v dict(**kwargs) -> new dictionary initialized with the name=value pairs in the keyword argument list. For example: dict(one=1, two=2).
`WFDB_Rhythm_Annotations`	dict() -> new empty dictionary dict(mapping) -> new dictionary initialized from a mapping object's (key, value) pairs dict(iterable) -> new dictionary initialized as if via: d = {} for k, v in iterable: d[k] = v dict(**kwargs) -> new dictionary initialized with the name=value pairs in the keyword argument list. For example: dict(one=1, two=2).

This module contains PyTorch ECG datasets for training neural networks.

`CINC2020Dataset`(config[, training, lazy])	Data generator for feeding data into pytorch models using the `CINC2020` database.
`CINC2021Dataset`([config, training, lazy])	Data generator for feeding data into pytorch models using the `CINC2021` database.
`CPSC2019Dataset`(config[, training, lazy])	Data generator for feeding data into pytorch models using the `CPSC2019` database.
`CPSC2021Dataset`(config, task[, training, lazy])	Data generator for feeding data into pytorch models using the `CPSC2021` database.
`LUDBDataset`(config[, training, lazy])	Data generator for feeding data into pytorch models using the `LUDB` database.
`MITDBDataset`(config, task[, training, lazy])	Data generator for feeding data into pytorch models using the `MITDB` database.

`CACHET_CADB`([db_dir, working_dir, verbose])	CACHET-CADB: A Contextualized Ambulatory Electrocardiography Arrhythmia Dataset
`SPH`([db_dir, working_dir, verbose])	Shandong Provincial Hospital Database