LUDB

class torch_ecg.databases.LUDB(db_dir: str | bytes | PathLike | None = None, working_dir: str | bytes | PathLike | None = None, verbose: int = 1, **kwargs: Any)

Bases: PhysioNetDataBase

Lobachevsky University Electrocardiography Database

ABOUT

1. consist of 200 10-second conventional 12-lead (i, ii, iii, avr, avl, avf, v1, v2, v3, v4, v5, v6) ECG signal records, with sampling frequency 500 Hz

2. boundaries of P, T waves and QRS complexes were manually annotated by cardiologists, and with the corresponding diagnosis

3. annotated are 16797 P waves, 21966 QRS complexes, 19666 T waves (in total, 58429 annotated waves)

4. distributions of data:

   • rhythm distribution:

     Rhythms	Number of ECGs
     Sinus rhythm	143
     Sinus tachycardia	4
     Sinus bradycardia	25
     Sinus arrhythmia	8
     Irregular sinus rhythm	2
     Abnormal rhythm	19

   • electrical axis distribution:

     Heart electric axis	Number of ECGs
     Normal	75
     Left axis deviation (LAD)	66
     Vertical	26
     Horizontal	20
     Right axis deviation (RAD)	3
     Undetermined	10

   • distribution of records with conduction abnomalities (totally 79):

     Conduction abnormalities	Number of ECGs
     Sinoatrial blockade, undetermined	1
     I degree AV block	10
     III degree AV-block	5
     Incomplete right bundle branch block	29
     Incomplete left bundle branch block	6
     Left anterior hemiblock	16
     Complete right bundle branch block	4
     Complete left bundle branch block	4
     Non-specific intravintricular conduction delay	4

   • distribution of records with extrasystoles (totally 35):

     Extrasystoles	Number of ECGs
     Atrial extrasystole, undetermined	2
     Atrial extrasystole, low atrial	1
     Atrial extrasystole, left atrial	2
     Atrial extrasystole, SA-nodal extrasystole	3
     Atrial extrasystole, type: single PAC	4
     Atrial extrasystole, type: bigemini	1
     Atrial extrasystole, type: quadrigemini	1
     Atrial extrasystole, type: allorhythmic pattern	1
     Ventricular extrasystole, morphology: polymorphic	2
     Ventricular extrasystole, localisation: RVOT, anterior wall	3
     Ventricular extrasystole, localisation: RVOT, antero-septal part	1
     Ventricular extrasystole, localisation: IVS, middle part	1
     Ventricular extrasystole, localisation: LVOT, LVS	2
     Ventricular extrasystole, localisation: LV, undefined	1
     Ventricular extrasystole, type: single PVC	6
     Ventricular extrasystole, type: intercalary PVC	2
     Ventricular extrasystole, type: couplet	2

   • distribution of records with hypertrophies (totally 253):

     Hypertrophies	Number of ECGs
     Right atrial hypertrophy	1
     Left atrial hypertrophy	102
     Right atrial overload	17
     Left atrial overload	11
     Left ventricular hypertrophy	108
     Right ventricular hypertrophy	3
     Left ventricular overload	11

   • distribution of records of pacing rhythms (totally 12):

     Cardiac pacing	Number of ECGs
     UNIpolar atrial pacing	1
     UNIpolar ventricular pacing	6
     BIpolar ventricular pacing	2
     Biventricular pacing	1
     P-synchrony	2

   • distribution of records with ischemia (totally 141):

     Ischemia	Number of ECGs
     STEMI: anterior wall	8
     STEMI: lateral wall	7
     STEMI: septal	8
     STEMI: inferior wall	1
     STEMI: apical	5
     Ischemia: anterior wall	5
     Ischemia: lateral wall	8
     Ischemia: septal	4
     Ischemia: inferior wall	10
     Ischemia: posterior wall	2
     Ischemia: apical	6
     Scar formation: lateral wall	3
     Scar formation: septal	9
     Scar formation: inferior wall	3
     Scar formation: posterior wall	6
     Scar formation: apical	5
     Undefined ischemia/scar/supp.NSTEMI: anterior wall	12
     Undefined ischemia/scar/supp.NSTEMI: lateral wall	16
     Undefined ischemia/scar/supp.NSTEMI: septal	5
     Undefined ischemia/scar/supp.NSTEMI: inferior wall	3
     Undefined ischemia/scar/supp.NSTEMI: posterior wall	4
     Undefined ischemia/scar/supp.NSTEMI: apical	11

   • distribution of records with non-specific repolarization abnormalities (totally 85):

     Non-specific repolarization abnormalities	Number of ECGs
     Anterior wall	18
     Lateral wall	13
     Septal	15
     Inferior wall	19
     Posterior wall	9
     Apical	11

   • there are also 9 records with early repolarization syndrome

   there might well be records with multiple conditions.

5. ludb.csv stores information about the subjects (gender, age, rhythm type, direction of the electrical axis of the heart, the presence of a cardiac pacemaker, etc.)

6. Webpage of the database on PhysioNet [1]. Paper describing the database [2].

Usage

1. ECG wave delineation

2. ECG arrhythmia classification

Issues

1. (version 1.0.0, fixed in version 1.0.1) ADC gain might be wrong, either units should be μV, or adc_gain should be 1000 times larger

References

[1]

https://physionet.org/content/ludb/1.0.1/

[2]

Kalyakulina, A., Yusipov, I., Moskalenko, V., Nikolskiy, A., Kozlov, A., Kosonogov, K., Zolotykh, N., & Ivanchenko, M. (2020). Lobachevsky University Electrocardiography Database (version 1.0.1).

Citation

10.1109/ACCESS.2020.3029211 10.13026/eegm-h675

Parameters:

• db_dir (path-like, optional) – Storage path of the database. If not specified, data will be fetched from Physionet.

• working_dir (path-like, optional) – Working directory, to store intermediate files and log files.

• verbose (int, default 1) – Level of logging verbosity.

• kwargs (dict, optional) – Auxilliary key word arguments.

property database_info: DataBaseInfo

The DataBaseInfo object of the database.

from_masks(masks: ndarray, mask_format: str = 'channel_first', leads: str | int | Sequence[int | str] | None = None, class_map: Dict[str, int] | None = None, fs: Real | None = None) → Dict[str, List[ECGWaveForm]]

Convert masks into lists of waveforms.

Parameters:

• masks (numpy.ndarray) – Wave delineation in the form of masks, of shape (n_leads, seq_len) or (seq_len,).

• mask_format (str, default "channel_first") – Format of the mask, used only when masks.ndim = 2. One of “channel_last” (alias “lead_last”), or “channel_first” (alias “lead_first”).

• leads (str or int or List[str] or List[int], optional) – The leads of the wave delineation to be loaded.

• class_map (dict, optional) – Custom class map. If not set, self.class_map will be used.

• fs (numbers.Real, optional) – Sampling frequency of the signal corresponding to the masks, If is None, self.fs will be used, to compute duration of the ECG waveforms.

Returns:

waves – The wave delineation annotations of the record. Each item value of the dict is a list containing the ECGWaveForm corr. to the lead (item key).

Return type:

dict

get_absolute_path(rec: str | int, extension: str | None = None) → Path

Get the absolute path of the record.

Parameters:

• rec (str or int) – Record name or index of the record in all_records.

• extension (str, optional) – Extension of the file.

Returns:

Absolute path of the file.

Return type:

pathlib.Path

get_subject_id(rec: str | int) → int

Attach a unique subject ID for the record.

Parameters:

rec (str or int) – Record name or index of the record in all_records.

Returns:

Subject ID associated with the record.

Return type:

int

load_ann(rec: str | int, leads: str | int | Sequence[int | str] | None = None, metadata: bool = False) → dict

Load the wave delineation, along with metadata if specified.

Parameters:

• rec (str or int) – Record name or index of the record in all_records.

• leads (str or int or List[str] or List[int], optional) – The leads of the wave delineation to be loaded.

• metadata (bool, default False) – If True, metadata will be loaded from corresponding head file.

Returns:

ann_dict – The wave delineation annotations.

Return type:

dict

load_diagnoses(rec: str | int) → List[str]

Load diagnoses of the record.

Parameters:

rec (str or int) – Record name or index of the record in all_records.

Returns:

diagnoses – List of diagnoses of the record.

Return type:

List[str]

load_masks(rec: str | int, leads: str | int | Sequence[int | str] | None = None, mask_format: str = 'channel_first', class_map: Dict[str, int] | None = None) → ndarray

Load the wave delineation in the form of masks.

Parameters:

• rec (str or int) – Record name or index of the record in all_records.

• leads (str or int or List[str] or List[int], optional) – The leads of the wave delineation to be loaded.

• mask_format (str, default "channel_first") – Format of the mask, “channel_last” (alias “lead_last”), or “channel_first” (alias “lead_first”).

• class_map (dict, optional) – Custom class map. If not set, self.class_map will be used.

Returns:

masks – The masks corresponding to the wave delineation annotations of the record.

Return type:

numpy.ndarray

load_subject_info(rec: str | int, fields: str | Sequence[str] | None = None) → dict | str

Load subject info of a record.

Parameters:

• rec (str or int) – Record name or index of the record in all_records.

• fields (str or Sequence[str], optional) – Field(s) of the subject info of record. If not specified, all fields of the subject info will be loaded.

Returns:

info – Subject info of the given fields of the record.

Return type:

dict or str

plot(rec: str | int, data: ndarray | None = None, ticks_granularity: int = 0, leads: str | int | Sequence[int | str] | None = None, same_range: bool = False, waves: ECGWaveForm | None = None, **kwargs: Any) → None

Plot the signals of a record or external signals (units in μV), with metadata (fs, labels, tranche, etc.), possibly also along with wave delineations.

Parameters:

• rec (str or int) – Record name or index of the record in all_records.

• data (numpy.ndarray, optional) – 12-lead ECG signal to plot. If is not None, data of rec will not be used. This is useful when plotting filtered data

• ticks_granularity (int, default 0) – Granularity to plot axis ticks, the higher the more ticks. 0 (no ticks) –> 1 (major ticks) –> 2 (major + minor ticks)

• leads (str or int or List[str] or List[int], optional) – The leads of the ECG signal to plot.

• same_range (bool, default False) – If True, all leads are forced to have the same y range.

• waves (ECGWaveForm, optional) – The waves (p waves, t waves, qrs complexes) to plot.

• kwargs (dict, optional) – Additional keyword arguments to pass to matplotlib.pyplot.plot().

TODO

1. Slice too long records, and plot separately for each segment.

2. Plot waves using matplotlib.pyplot.axvspan().

Note

Locator of plt has default MAXTICKS of 1000. If not modifying this number, at most 40 seconds of signal could be plotted once.

Contributors: Jeethan, and WEN Hao