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Volume 46 Issue 2
Feb.  2024
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ZHOU Weixin, GAO Zhaogang, XIAO Wan'ang. Intelligent Heart Sound Abnormal Diagnosis Chip Based on LSTM for Wearable Applications[J]. Journal of Electronics & Information Technology, 2024, 46(2): 555-563. doi: 10.11999/JEIT230934
Citation: ZHOU Weixin, GAO Zhaogang, XIAO Wan'ang. Intelligent Heart Sound Abnormal Diagnosis Chip Based on LSTM for Wearable Applications[J]. Journal of Electronics & Information Technology, 2024, 46(2): 555-563. doi: 10.11999/JEIT230934

Intelligent Heart Sound Abnormal Diagnosis Chip Based on LSTM for Wearable Applications

doi: 10.11999/JEIT230934
Funds:  The Key Research Program of the Chinese Academy of Sciences (XDPB22)
  • Received Date: 2023-08-28
  • Rev Recd Date: 2024-01-17
  • Available Online: 2024-01-23
  • Publish Date: 2024-02-10
  • The gravity of cardiovascular disease hazards necessitates the utmost importance of preventive measures and early diagnosis for such ailments. Conventional manual auscultation techniques and computer-based diagnostic methods prove inadequate in meeting the demands of auscultation. Consequently, wearable devices attract increasing attention, but they are required to obtain both a high accuracy and low-power consumption. An intelligent heart sound abnormal diagnostic chip based on LSTM for wearable applications is presented. The abnormal heart sound diagnostic system is developed, including preprocessing, feature extraction, and abnormal diagnosis. Furthermore, an FPGA-based system for heart sounds acquisition is constructed. The challenge of imbalanced datasets is addressed through the implementation of data augmentation techniques. By utilizing pre-trained model as a foundation, the intelligent heart sound abnormal diagnostic chip is developed, and the layout and MPW are finished under SMIC 180nm. The post-simulation results demonstrate that the chip achieves a diagnostic accuracy of 98.6%, a power consumption of 762 μW, and an area of 3.06 mm$ \times $2.45 mm, meeting the high-performance and low-power consumption prerequisites of wearable devices.
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  • [1]
    WHO. The top 10 causes of death[EB/OL]. https://www.who.int/en/news-room/fact-sheets/detail/the-top-10-causes-of-death, 2020.
    [2]
    WANG Fei, SYEDA-MAHMOOD T, and BEYMER D. Finding disease similarity by combining ECG with heart auscultation sound[C]. 2007 Computers in Cardiology, Durham, USA, 2007: 261–264. doi: 10.1109/CIC.2007.4745471.
    [3]
    DOMINGUEZ-MORALES J P, JIMENEZ-FERNANDEZ A F, DOMINGUEZ-MORALES M J, et al. Deep neural networks for the recognition and classification of heart murmurs using neuromorphic auditory sensors[J]. IEEE Transactions on Biomedical Circuits and Systems, 2018, 12(1): 24–34. doi: 10.1109/TBCAS.2017.2751545.
    [4]
    EJAZ K, NORDEHN G, ALBA-FLORES R, et al. A heart murmur detection system using spectrograms and artificial neural networks[C]. Proceedings of the Second IASTED International Conference on Circuits, Signals, and Systems, Clearwater Beach, USA, 2004: 374–379.
    [5]
    MILANI M G M, ABAS P E, DE SILVA L C, et al. Abnormal heart sound classification using phonocardiography signals[J]. Smart Health, 2021, 21: 100194. doi: 10.1016/j.smhl.2021.100194.
    [6]
    MILANI M G M, ABAS P E, and DE SILVA L C. A critical review of heart sound signal segmentation algorithms[J]. Smart Health, 2022, 24: 100283. doi: 10.1016/j.smhl.2022.100283.
    [7]
    XU Weize, YU Kai, YE Jingjing, et al. Automatic pediatric congenital heart disease classification based on heart sound signal[J]. Artificial Intelligence in Medicine, 2022, 126: 102257. doi: 10.1016/j.artmed.2022.102257.
    [8]
    ZHENG Yineng, GUO Xingming, WANG Yingying, et al. A multi-scale and multi-domain heart sound feature-based machine learning model for ACC/AHA heart failure stage classification[J]. Physiological Measurement, 2022, 43(6): 065002. doi: 10.1088/1361-6579/ac6d40.
    [9]
    RENNA F, OLIVEIRA J, and COIMBRA M T. Deep convolutional neural networks for heart sound segmentation[J]. IEEE Journal of Biomedical and Health Informatics, 2019, 23(6): 2435–2445. doi: 10.1109/JBHI.2019.2894222.
    [10]
    TARIQ Z, SHAH S K, and LEE Y. Feature-based fusion using CNN for lung and heart sound classification[J]. Sensors, 2022, 22(4): 1521. doi: 10.3390/s22041521.
    [11]
    DENG Muqing, MENG Tingting, CAO Jiuwen, et al. Heart sound classification based on improved MFCC features and convolutional recurrent neural networks[J]. Neural Networks, 2020, 130: 22–32. doi: 10.1016/j.neunet.2020.06.015.
    [12]
    CHEN Wei, SUN Qiang, CHEN Xiaomin, et al. Deep learning methods for heart sounds classification: A systematic review[J]. Entropy, 2021, 23(6): 667. doi: 10.3390/e23060667.
    [13]
    SHI K, SCHELLENBERGER S, WEBER L, et al. Segmentation of radar-recorded heart sound signals using bidirectional LSTM networks[C]. 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin, Germany, 2019: 6677–6680. doi: 10.1109/EMBC.2019.8857863.
    [14]
    LEE S H, KIM Y S, and YEO W H. Advances in microsensors and wearable bioelectronics for digital stethoscopes in health monitoring and disease diagnosis[J]. Advanced Healthcare Materials, 2021, 10(22): 2101400. doi: 10.1002/adhm.202101400.
    [15]
    WEI Min, SUN Kexue, WANG Chenxi, et al. A design and implementation for heart sound detection instrument based on FPGA[C]. 2015 3rd International Conference on Machinery, Materials and Information Technology Applications, Qingdao, China, 2015: 278–282. doi: 10.2991/icmmita-15.2015.55.
    [16]
    JUSAK J, PUSPASARI I, and KUSUMAWATI W I. A semi-automatic heart sounds identification model and its implementation in internet of things devices[J]. Advances in Electrical and Computer Engineering, 2021, 21(1): 45–56. doi: 10.4316/AECE.2021.01005.
    [17]
    LI Tao, YIN Yibo, MA Kainan, et al. Lightweight end-to-end neural network model for automatic heart sound classification[J]. Information, 2021, 12(2): 54. doi: 10.3390/info12020054.
    [18]
    WANG Jing, CHEN Ping, ZHANG Cheng, et al. Corona virus disease 2019 respiratory cycle detection based on convolutional neural network[C]. 2021 IEEE Biomedical Circuits and Systems Conference (BioCAS), Berlin, Germany, 2021: 1–4. doi: 10.1109/BioCAS49922.2021.9644970.
    [19]
    YASEEN, SON G Y, and KWON S. Classification of heart sound signal using multiple features[J]. Applied Sciences, 2018, 8(12): 2344. doi: 10.3390/app8122344.
    [20]
    GHOSH S K, PONNALAGU R N, TRIPATHY R K, et al. Automated detection of heart valve diseases using chirplet transform and multiclass composite classifier with PCG signals[J]. Computers in Biology and Medicine, 2020, 118: 103632. doi: 10.1016/j.compbiomed.2020.103632.
    [21]
    ALKHODARI M and FRAIWAN L. Convolutional and recurrent neural networks for the detection of valvular heart diseases in phonocardiogram recordings[J]. Computer Methods and Programs in Biomedicine, 2021, 200: 105940. doi: 10.1016/j.cmpb.2021.105940.
    [22]
    KAO Chaoyang, KUO H C, CHEN Jianwen, et al. RNNAccel: A fusion recurrent neural network accelerator for edge intelligence[EB/OL]. https://arxiv.org/abs/2010.13311, 2020.
    [23]
    CHEN Chixiao, DING Hongwei, PENG Huwan, et al. OCEAN: An on-chip incremental-learning enhanced processor with gated recurrent neural network accelerators[C]. 43rd IEEE European Solid State Circuits Conference, Leuven, Belgium, 2017: 259–262. doi: 10.1109/ESSCIRC.2017.8094575.
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