A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Classification of Heart Sounds Using Convolutional Neural Network (2020)
Li, Fan; Tang, Hong; Shang, Shang; Mathiak, Klaus; Cong, Fengyu (2020). Classification of Heart Sounds Using Convolutional Neural Network. Applied Sciences, 10 (11), 3956. DOI: 10.3390/app10113956
JYU-tekijät tai -toimittajat
Julkaisun tiedot
Julkaisun kaikki tekijät tai toimittajat: Li, Fan; Tang, Hong; Shang, Shang; Mathiak, Klaus; Cong, Fengyu
Lehti tai sarja: Applied Sciences
eISSN: 2076-3417
Julkaisuvuosi: 2020
Volyymi: 10
Lehden numero: 11
Artikkelinumero: 3956
Kustantaja: MDPI
Julkaisumaa: Sveitsi
Julkaisun kieli: englanti
DOI: https://doi.org/10.3390/app10113956
Avoin saatavuus: Open access -julkaisukanavassa ilmestynyt julkaisu
Julkaisu on rinnakkaistallennettu (JYX): https://jyx.jyu.fi/handle/123456789/69902
Tiivistelmä
Heart sounds play an important role in the diagnosis of cardiac conditions. Due to the low signal-to-noise ratio (SNR), it is problematic and time-consuming for experts to discriminate different kinds of heart sounds. Thus, objective classification of heart sounds is essential. In this study, we combined a conventional feature engineering method with deep learning algorithms to automatically classify normal and abnormal heart sounds. First, 497 features were extracted from eight domains. Then, we fed these features into the designed convolutional neural network (CNN), in which the fully connected layers that are usually used before the classification layer were replaced with a global average pooling layer to obtain global information about the feature maps and avoid overfitting. Considering the class imbalance, the class weights were set in the loss function during the training process to improve the classification algorithm’s performance. Stratified five-fold cross-validation was used to evaluate the performance of the proposed method. The mean accuracy, sensitivity, specificity and Matthews correlation coefficient observed on the PhysioNet/CinC Challenge 2016 dataset were 86.8%, 87%, 86.6% and 72.1% respectively. The proposed algorithm’s performance achieves an appropriate trade-off between sensitivity and specificity.
YSO-asiasanat: sydäntaudit; diagnostiikka; koneoppiminen; tiedonlouhinta; neuroverkot
Vapaat asiasanat: automatic heart sound classification; feature engineering; convolutional neural network
Liittyvät organisaatiot
OKM-raportointi: Kyllä
Raportointivuosi: 2020
