Awesome

Dreem Open Dataset Evaluation (DOD-Evaluation)

DOD-Evaluation provides tools to build sleep staging consensus and compare sleep stagings to these consensus. Our paper "Dreem Open Datasets: Multi-Scored Sleep Datasets to compare Human and Automated sleep staging" uses the code from this repository to computer the scorer consensus and the model performances. The repository also contains each of the scorer's sleep staging on DOD-O and DOD-H, their consensus on both dataset and the results from all the models presented in the paper.

Building a consensus from scorers

The individual sleep staging from each scorer for each record has to be provided in the 'scorers/dataset_name/scorer_name/' directory. All the sleep staging are assumed to list of the same length, the sleep stages are denoted by the following numbers: {'Not scored': -1, 'Wake':0,'N1':1,'N2':2,'N3':3,'REM':4}. Hence a sleep staging is simply a list of int between -1 and 4 stored in record_name.json file.

The consensus can then be built using evaluation.ResultsEvaluation:

from evaluation import ResultsEvaluation

dataset = 'dodo'
scorers_folder = "scorers/{}/".format(dataset)

result_evaluation = ResultsEvaluation(
    scorers_folder=scorers_folder
)
result_evaluation.print_soft_agreements()
result_evaluation.print_scores()

Evaluation of a sleep staging against consensus

Sleep stagings to evaluate are to be stored in "results/dataset_name/table_name/" in different folders. Each of the folder must contains the hypnogram outputted by the model for all the record of the consensus. Each of these hypnogram must have the same length as the consensus. The overall performance analysis can be performed with:

dataset = 'dodo'
table = 'base_models'
scorers_folder = "scorers/{}/".format(dataset)
results_folder = f"results/{dataset}/{table + '/' if table is not None else ''}"
if dataset == 'dodo':
    result_evaluation = ResultsEvaluation(
        scorers_folder=scorers_folder,
        results_folder=results_folder

    )
elif dataset == 'dodh':
    result_evaluation = ResultsEvaluation(
        scorers_folder=scorers_folder,
        results_folder=results_folder,
        lights_off={
            "63b799f6-8a4f-4224-8797-ea971f78fb53": 60,
            "de3af7b1-ab6f-43fd-96f0-6fc64e8d2ed4": 60,
        },
        lights_on={
            "a14f8058-f636-4be7-a67a-8f7f91a419e7": 620,
        }
    )

result_evaluation.print_soft_agreements()
result_evaluation.print_scores()