Joint Heart Sounds Segmentation and Murmur Detection with Masked Loss Function
| Autor: | Tomasz Grzywalski, Honorata Hafke-Dys, Krzysztof Szarzynski, Adam Maciaszek, Mateusz Piecuch, Riccardo Belluzzo |
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| Rok vydání: | 2020 |
| Předmět: |
Masking (art)
Heartbeat Stethoscope business.industry Computer science 0206 medical engineering Pattern recognition 02 engineering and technology 020601 biomedical engineering law.invention 030507 speech-language pathology & audiology 03 medical and health sciences law Heart sounds Heart beat Heart murmur medicine Segmentation Artificial intelligence medicine.symptom 0305 other medical science business |
| Zdroj: | IJCNN |
| DOI: | 10.1109/ijcnn48605.2020.9207315 |
| Popis: | In recent years, many approaches have been proposed for automated heart sound analysis. However, most of these algorithms separate the two steps required for an accurate detection of abnormal cardiac sounds: segmentation of stethoscope recording into individual heart cycles and detection of heart murmurs. In this work we propose a method to train a neural network to perform both of these tasks simultaneously achieving a synergy effect. Despite the fact that the method uses both types of labels for training, they don’t have to be specified for all training examples. Moreover it also supports negative examples, i.e recordings with no heart sounds. The result is a single neural network model that can detect individual heartbeat cycles, segment these into the 4 heartbeat phases and predict heart murmur presence. This is achieved by using a training loss function that incorporates relations between the different output types and uses masking in case of missing labels. We evaluated our results on the popular 2016 PhysioNet/CinC Challenge dataset for heart sounds and benchmarked it with respect to three state-of-the-art heart murmur detection algorithms. Our method significantly outperforms the latter algorithms achieving, in particular, an F1-score of 83.9% – an enhancement of 7.6 percentage points over the best of the considered alternatives. |
| Databáze: | OpenAIRE |
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