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面向可穿戴式的基于LSTM神經(jīng)網(wǎng)絡(luò)的智能心音異常診斷芯片

周維新 高肇崗 肖宛昂

周維新, 高肇崗, 肖宛昂. 面向可穿戴式的基于LSTM神經(jīng)網(wǎng)絡(luò)的智能心音異常診斷芯片[J]. 電子與信息學(xué)報, 2024, 46(2): 555-563. doi: 10.11999/JEIT230934
引用本文: 周維新, 高肇崗, 肖宛昂. 面向可穿戴式的基于LSTM神經(jīng)網(wǎng)絡(luò)的智能心音異常診斷芯片[J]. 電子與信息學(xué)報, 2024, 46(2): 555-563. doi: 10.11999/JEIT230934
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

面向可穿戴式的基于LSTM神經(jīng)網(wǎng)絡(luò)的智能心音異常診斷芯片

doi: 10.11999/JEIT230934
基金項目: 中國科學(xué)院先導(dǎo)科技專項培育項目(XDPB22)
詳細(xì)信息
    作者簡介:

    周維新:男,博士生,研究方向為智能體音診斷算法研究、可穿戴式智能體音芯片設(shè)計

    高肇崗:男,碩士生,研究方向為深度學(xué)習(xí)算法研究、數(shù)字電路設(shè)計

    肖宛昂:男,研究員,研究方向為智能聲音信號處理芯片、無線通信基帶芯片以及機器學(xué)習(xí)的FPGA加速

    通訊作者:

    肖宛昂 waxiao@semi.ac.cn

  • 中圖分類號: TN492;TP183

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

Funds: The Key Research Program of the Chinese Academy of Sciences (XDPB22)
  • 摘要: 心血管疾病是造成全球死亡人數(shù)最多的疾病之一,因此對心血管疾病的預(yù)防與提前診斷至關(guān)重要。人工聽診技術(shù)與計算機心音診斷技術(shù)無法滿足對心音長時間聽診的需求,因而可穿戴式聽診設(shè)備越來越受到關(guān)注,但是其具有高精度與低功耗的要求。該文設(shè)計了低功耗的面向可穿戴式的基于長短期記憶網(wǎng)絡(luò)(Long Short-Term Memory, LSTM)的智能心音異常診斷芯片,提出了包括預(yù)處理、特征提取以及異常診斷的心音異常診斷系統(tǒng),并搭建了基于聽診器的心音采集FPGA系統(tǒng),采用了數(shù)據(jù)增強的方法解決數(shù)據(jù)集的不平衡問題。基于預(yù)訓(xùn)練模型設(shè)計了智能心音異常診斷芯片,在SMIC180 nm工藝下完成了版圖設(shè)計和MPW流片。后仿真結(jié)果表明,智能心音異常診斷芯片的診斷準(zhǔn)確率為98.6%,功耗為762 μW,面積為3.06 mm × 2.45 mm,滿足可穿戴式智能心音異常診斷設(shè)備的高性能與低功耗的需求。
  • 圖  1  基于聽診器的心音采集FPGA系統(tǒng)

    圖  2  采用數(shù)據(jù)增強技術(shù)后的心音

    圖  3  心音異常診斷系統(tǒng)

    圖  4  心肺音分離效果圖

    圖  5  心音異常診斷模型訓(xùn)練過程

    圖  6  心音異常診斷芯片架構(gòu)

    圖  7  診斷模型的電路設(shè)計

    圖  8  FPGA原型驗證方案

    圖  9  心音異常診斷芯片的版圖

    表  1  心音診斷模型訓(xùn)練參數(shù)

    訓(xùn)練參數(shù)
    框架 Pytorch
    GPU型號 TITAN5 12 GB
    EPOCH 300
    BATCH SIZE 32
    學(xué)習(xí)率 0.0001
    優(yōu)化器 Adam
    下載: 導(dǎo)出CSV

    表  2  不同方法的性能對比

    方法準(zhǔn)確率(%)功耗面積(mm2)
    YASEEN等人[19]97.9//
    CHOSH等人[20]98.3//
    ALKHODARI等人[21]99.3//
    KAO等人[22]/12.6 mW @ 40 nm
    (前仿)
    /
    CHEN等人[23]/6.6 mW @ 65 nm(實測)10.15
    心音異常診斷芯片98.6762 μW @ 180 nm
    (后仿)
    7.50
    下載: 導(dǎo)出CSV
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出版歷程
  • 收稿日期:  2023-08-28
  • 修回日期:  2024-01-17
  • 網(wǎng)絡(luò)出版日期:  2024-01-23
  • 刊出日期:  2024-02-29

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