Deep Learning Hardware/Software Co-Design for Heart Sound Classification

被引：2

作者：

Jhong, Wun-Siou ^{[1
]}

Chu, Shao-, I ^{[1
]}

Huang, Yu-Jung ^{[2
]}

Hsu, Tsun-Yi ^{[1
]}

Lin, Wei-Chen ^{[3
]}

Huang, Pokai ^{[4
]}

Wang, Jia-Jung ^{[5
]}

机构：

[1] Natl Kaohsiung Univ Sci & Technol, Dept Elect Engn, Kaohsiung, Taiwan

[2] I Shou Univ, Dept Elect Engn, Kaohsiung, Taiwan

[3] E DA Hosp, Med Res Dept, Kaohsiung, Taiwan

[4] E DA DACHANG Hosp, Dept Paediat, Kaohsiung, Taiwan

[5] I Shou Univ, Dept Biomed Engn, Kaohsiung, Taiwan

来源：

2020 17TH INTERNATIONAL SOC DESIGN CONFERENCE (ISOCC 2020) | 2020年

关键词：

deep learning; codesign; LSTM; FPGA; FEATURES;

D O I：

10.1109/ISOCC50952.2020.9333069

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper presents a software/hardware co-design for classifying three most commonly heart sounds classes: normal, murmur and extrasystole heartbeat. The detection system extracts Mel Frequency Cepstral Coefficient (MFCC)-based heart sound features to train different deep learning network architectures for multiclass classification. The software/hardware co-design for Long Short-Term Memory (LSTM) implementation indicates the multiclass classification accuracy of 85% can be achieved. The proposed heart sound classification platform has great development potential and good application prospects.

引用

页码：27 / 28

页数：2

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