LTBio Python Library#

1.1.0#

Released: 05-02-2023 | Created: 20-01-2023 | Public

Spatial efficiency for Biosignals (only loaded from .biosignal files), by using memory maps.

Added#

Changed#

  • When saving a Biosignal to a .biosignal file, the samples of all Segment’s are memory mapped to disk, and the Biosignal object is dumped as that. The main advantage is that samples are stored in disk, and only coppied to memory when needed. This functionality is kept when the Biosignal is loaded in future Python sessions.

Deprecated#

Removed#

Fixed#

1.0.2#

Released: 20-12-2022 | Created: 05-07-2022 | Not Public

Major bug fixes and more complex tests to ensure the library’s stability.

Added#

  • Class Normalizer as a formatter.

Changed#

Deprecated#

Removed#

Fixed#

1.0.1#

Released: 23-06-2022 | Created: 01-06-2022 | Public

First ready-to-use realease.

Added#

  • New biosignal modalities: ACC, ECG, EDA, EEG, EMG, PPG, RESP, TEMP.

  • New biosignal sources: Bitalino, E4, HEM, HSM, MITDB, Seer, Sense (as examples).

  • New medical conditions: Epilepsy and COVID19 (as examples).

  • New surgical procedure: CarpalTunnelRelease (as example).

  • New body locations: general anatomical location, and ECG and EEG electrode locations (as examples).

  • New features: mean, variance, HRV, … (as examples).

  • Classes OverlappingTimeseries and Frequency.

  • Class SupervisedTrainReport to produce PDF reports of ML models.

  • Package pipeline: classes Pipeline, PipelineUnit, SinglePipelineUnit, PipelineUnitsUnion (ApplyTogether and ApplySeparatly), Packet, Input, GoTo.

Changed#

  • Class Filter divided in two: FrequencyDomainFilter and TimeDomainFilter.

Fixed#

  • Public API calls. Each submodule imports what should be used by the user.

1.0.0#

Released: 31-05-2022 | Created: 01-02-2022 | Not Public

Begining of the LTBio Python Library. All packages were available, except pipeline.

Added#

  • Abstract classes Biosignal and BiosignalSource, and some concrete implementations in the sub-packages modalities and sources, respectively.

  • Classes Timeseries, Segment, Unit, Event.

  • Packages clinical: classes Patient, BodyLocation, MedicalCondition, Medication, and SurgicalProcedure.

  • Package processing: classes Segmenter and Filter.

  • Package features: classes FeatureExtractor and FeatureSelector.

  • Package ml: classes SupervisedModel, SupervisedTrainConditions, SurpervisingTrainer, SupervisedTrainResults.