Ke Sun
ABSTRACT
At present, the recognition and analysis of heart sound signal was usually run by using high-performance PC. It was hardly done by embedded devices due to limited resources. Provide a portable device for assisting in the initial diagnosis of congenital heart disease (CHD) for doctors with outdated equipment in remote mountainous areas. A novel embedded heart sound analysis and recognition system based on Raspberry pi 3b+ with a NCS2 neural computing stick was put forward in this paper. Firstly, the OpenVINO software platform launched by Intel was used to transfer the ssd_inception_v2 model into the Raspberry Pi after performing transfer learning optimization. Then, reasoning calculation was carried out in Raspberry pi with neural computing stick. Neural computing stick is a deep learning and reasoning tool based on USB mode and an independent artificial intelligence accelerator. NCS2 neural computing stick was used to realize the heart sound analysis and recognition of embedded devices. The sensitivity of the experimental results is 80.7%, the specificity is 95.5%, and the accuracy is 91.4%. The experimental results show that the system has the advantages of low power dissipation, low cost, small size, fast speed, and high recognition rate. It can be used for machine assisted diagnosis of congenital heart disease.
- Li Kunlin and Jiang Lihong (2020). Research reports on the incidence of congenital heart disease at home and abroad[J]. Yunnan Medicine, 41(03), 286--289.Google Scholar
- Jiang Hong (2012). Heart sound signal analysis and recognition based on wavelet and HHT[D]. Wuhan University of Technology.Google Scholar
- Tan Chaowen, Zong Rong, Wang William and Yang Hongbo (2019). Heart sound preprocessing algorithm suitable for convolutional neural networks[J]. Computer Simulation, 36(12), 214--218.Google Scholar
- Shu Lih Oh, V Jahmunah, Chui Ping Ooi, Ru-San Tan, Edward J Ciaccio, Toshitaka Yamakawa, Masayuki Tanabe, Makiko Kobayashi and U Rajendra Acharya (2020). Classification of heart sound signals using a novel deep WaveNet model[J]. Computer Methods and Programs in Biomedicine, 196.Google Scholar
- Raza Ali, Mehmood Arif, Ullah Saleem, Ahmad Maqsood, Choi Gyu Sang and On Byung-Won (2019). Heartbeat Sound Signal Classification Using Deep Learning[J]. Sensors (Basel, Switzerland), 19(21).Google Scholar
- Zha Qiqiu, Zhao Yang, Song Shaoshuai, Zong Rong and Wang William (2017). Design of FPGA-based heart sound signal acquisition system[J]. Journal of Yunnan University (Natural Science Edition), 39(03), 356--359.Google Scholar
- Huang Bin, Zeng Qingning and Long Chao (2016). A portable heart sound ECG real-time acquisition system for Android platform[J]. Modern Electronic Technology, 39(02), 85--88.Google Scholar
- G Hao, Y Yingkun and Q Yi (2019). General Target Detection Method Based on Improved SSD. 2019 IEEE 8th Joint International Information Technology and Artificial Intelligence Conference (ITAIC), Chongqing, China, pp. 1787--1791, doi: 10.1109/ITAIC.2019.8785733.Google Scholar
- Murad Al Qurishee, Weidong Wu, Babatunde Atolagbe, et al. (2020). Bridge Girder Crack Assessment Using Faster RCNN Inception V2 and Infrared Thermography, 10(02), 110--127.Google Scholar
- Weilian Wan, Ke Sun, Ai Xiong, Ruping Yao, Rusheng Xie and Qiang Zhang (2020). Heart sound signal wireless acquisition device[P]. Yunnan Province: CN211022740U.Google Scholar
- Liu W, Anguelov D, Erhan D, et al. (2016). SSD: Single Shot MultiBox Detector[J].Google Scholar
- Ioffe S and Szegedy C (2015). Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. Computer Science.Google Scholar
- Soft Computing (2020). Researchers at Southern University of Science and Technology (SUSTech) Target Soft Computing (Simultaneous Use of Two Normalization Methods In Decomposition-based Multi-objective Evolutionary Algorithms)[J]. News of Science.Google Scholar
- S Rubin Bose and V Sathiesh Kumar (2020). Efficient inception V2 based deep convolutional neural network for real-time hand action recognition. 14(4), 688--696.Google Scholar
- Riel D Castro-Zunti, Juan Yépez and Seok-Bum Ko (2020). License plate segmentation and recognition system using deep learning and OpenVINO. 14(2), 119--126.Google Scholar
- Chen Bolei (2020). Smart Home Robot Based on Raspberry Pi and Neural Compute Stick[J]. Application of Microcontroller and Embedded System, 19(07), 56--59+64.Google Scholar
Index Terms
- Analysis and Recognition of Heart Sound Based on NCS2 Neural Computing Stick
Recommendations
Automatic pediatric congenital heart disease classification based on heart sound signal
AbstractCongenital heart diseases (CHD) are the most common birth defects, and the early diagnosis of CHD is crucial for CHD therapy. However, there are relatively few studies on intelligent auscultation for pediatric CHD, due to the fact that ...
Highlights- A database of heart sounds in CHD is established using electronic stethoscopes and is proposed to be made publicly available online.
An Effective CAD System for Heart Sound Abnormality Detection
AbstractThe study of heart sound signals is considered a helpful approach for monitoring heart diseases and for assessing heart hemodynamic condition. In fact, several cardiac disorders are tangible in heart sound signal characteristics such as intensity, ...
Automatic Heart Sound Classification Using One Dimension Deep Neural Network
Security, Privacy, and Anonymity in Computation, Communication, and StorageAbstractCardiovascular disease (CVD) is one of the life-threatening diseases. Many researchers handcrafted features of heart sound to analyze heart sound signals for CVD automatically and achieved great success. But the handcrafted features of heart sound ...
Comments