Using time-frequency features to recognize abnormal heart sounds

Hsuan Lin Her, Hung Wen Chiu

研究成果: 書貢獻/報告類型會議貢獻

13 引文 斯高帕斯(Scopus)


Disease of the heart accounts for 6% of all death. Heart sound is a routine in physical examination clinically, and is sensitive in detecting a subset of heart diseases. In the current study, we build up a model to classify heart sounds for preclinical screening. Heart sounds are recorded under uncontrolled environment, and each sample can range from 5 to 120 seconds. This is a work raised by annual PhysioNet/CinC Challenge. Because of timing and tonic natures of heart events, we used time-frequency features to classify heart sounds in this study. Firstly, each heard sound recording was segmented into cycles using Springer's improved version of Schmidt's method. Each cardiac cycle was cut into 10 partitions and data points were obtained by zero-padding in each partition. Spectral features were extracted from each partition using fast-Fourier Transform (FFT) thus a 3,500 feature matrix was created. Using filter method, 40 features were selected for the final classifier. The average feature matrix of each cycle was then applied to a classification system using 2-means clustering and artificial neural network (ANN). By clustering the unsure class was recognized. The discrimination of normal and abnormal heart sound were performed by a well-trained ANN model. The results showed that our proposed method got a performance with an accuracy 86.5%, a sensitivity 84.4%, a specificity 86.9%. Here we show that classifying abnormal heart sound is a really difficult task due to the heterogeneity of 'abnormal events' and intra-sample deviation.
主出版物標題Computing in Cardiology Conference, CinC 2016
編輯Alan Murray
發行者IEEE Computer Society
出版狀態已發佈 - 2016
事件43rd Computing in Cardiology Conference, CinC 2016 - Vancouver, 加拿大
持續時間: 9月 11 20169月 14 2016


名字Computing in Cardiology


其他43rd Computing in Cardiology Conference, CinC 2016

ASJC Scopus subject areas

  • 電腦科學(全部)
  • 心臟病學與心血管醫學


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