Secure compressive sensing for ECG monitoring

被引:16
|
作者
Djelouat, Hamza [1 ]
Amira, Abbes [2 ]
Bensaali, Faycal [1 ]
Boukhennoufa, Issam [1 ]
机构
[1] Qatar Univ, Coll Engn, POB 2713, Doha, Qatar
[2] De Montfort Univ, Inst Artificial Intelligence, Leicester, Leics, England
关键词
Compressive sensing (CS); Encryption; LFSR; Sparse sensing matrices; IoT; ENCRYPTION; RECOVERY; PRIVACY; SYSTEM;
D O I
10.1016/j.cose.2019.101649
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
With the proliferation of wearable wireless sensors and adequate communication protocols, the internet of things (IoT) started to shape the healthcare sector into a new frame of connection between patients with health providers. Although the huge opportunities granted by this paradigm, it raises some concerns as well. Two main issues that could be highlighted are power consumption and medical record security. This paper aims to address both issues in an IoT-based remote health monitoring systems by exploring compressive sensing (CS). The presented solution exploits shift registers with sparse sensing matrices in order to construct an encryption key to implement a lightweight efficient encryption scheme. The obtained results show that CS can reduce power consumption by a factor of 35% by only transmitting 70% of the data while preserving the ECG information. In addition, the paper demonstrates that transmission is kept secure even if the illegitimate attacker can access 95% of the information needed to recover the data. (C) 2019 Elsevier Ltd. All rights reserved.
引用
收藏
页数:8
相关论文
共 50 条
  • [41] Deep Compressive Sensing on ECG Signals with Modified Inception Block and LSTM
    Hua, Jing
    Rao, Jue
    Peng, Yingqiong
    Liu, Jizhong
    Tang, Jianjun
    ENTROPY, 2022, 24 (08)
  • [42] On exploiting interbeat correlation in compressive sensing-based ECG compression
    Polania, Luisa F.
    Carrillo, Rafael E.
    Blanco-Velasco, Manuel
    Barner, Kenneth E.
    COMPRESSIVE SENSING, 2012, 8365
  • [43] Distributed Compressive Sensing for Multichannel ECG Signals over Learned Dictionaries
    Singh, Anurag
    Dandapat, S.
    2014 ANNUAL IEEE INDIA CONFERENCE (INDICON), 2014,
  • [44] Matched Filtering for Heart Rate Estimation on Compressive Sensing ECG Measurements
    Da Poian, Giulia
    Rozell, Christopher J.
    Bernardini, Riccardo
    Rinaldo, Roberto
    Clifford, Gari D.
    IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, 2018, 65 (06) : 1349 - 1358
  • [45] Compressive sensing exploiting wavelet-domain dependencies for ECG compression
    Polania, Luisa F.
    Carrillo, Rafael E.
    Blanco-Velasco, Manuel
    Barner, Kenneth E.
    COMPRESSIVE SENSING, 2012, 8365
  • [46] Compressive sensing of ECG signals using plug-and-play regularization
    Unni, V. S.
    Gavaskar, Ruturaj G.
    Chaudhury, Kunal N.
    SIGNAL PROCESSING, 2023, 202
  • [47] A Study on Dictionary Selection in Compressive Sensing for ECG Signals Compression and Classification
    Fira, Monica
    Costin, Hariton-Nicolae
    Goras, Liviu
    BIOSENSORS-BASEL, 2022, 12 (03):
  • [48] A Secure Steganography Algorithm Using Compressive Sensing based on HVS Feature
    Shafee, Samaneh
    Rajaei, Boshra
    2017 SEVENTH INTERNATIONAL CONFERENCE ON EMERGING SECURITY TECHNOLOGIES (EST), 2017, : 73 - 77
  • [49] Compressive sensing and paillier cryptosystem based secure data collection in WSN
    Samir Ifzarne
    Imad Hafidi
    Nadia Idrissi
    Journal of Ambient Intelligence and Humanized Computing, 2023, 14 : 6243 - 6250
  • [50] Compressive Sensing Matrix Designed by Tent Map, for Secure Data Transmission
    Frunzete, Madalin
    Yu, Lei
    Barbot, Jean-Pierre
    Vlad, Adriana
    SPA 2011: SIGNAL PROCESSING ALGORITHMS, ARCHITECTURES, ARRANGEMENTS, AND APPLICATIONS CONFERENCE PROCEEDINGS, 2011, : 11 - +