2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
Login Paper Search My Schedule Paper Index Help

My ICASSP 2021 Schedule

Note: Your custom schedule will not be saved unless you create a new account or login to an existing account.
  1. Create a login based on your email (takes less than one minute)
  2. Perform 'Paper Search'
  3. Select papers that you desire to save in your personalized schedule
  4. Click on 'My Schedule' to see the current list of selected papers
  5. Click on 'Printable Version' to create a separate window suitable for printing (the header and menu will appear, but will not actually print)

Paper Detail

Paper IDBIO-2.2
Paper Title MULTI-LEVEL REVERSIBLE ENCRYPTION FOR ECG SIGNALS USING COMPRESSIVE SENSING
Authors Mikko Impiö, Mehmet Yamaç, Tampere University, Finland; Jenni Raitoharju, Finnish Environment Institute, Finland
SessionBIO-2: Biomedical Signal Processing: Detection and Estimation
LocationGather.Town
Session Time:Tuesday, 08 June, 13:00 - 13:45
Presentation Time:Tuesday, 08 June, 13:00 - 13:45
Presentation Poster
Topic Biomedical Imaging and Signal Processing: [BIO] Biomedical signal processing
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Privacy concerns in healthcare have gained interest recently via GDPR, with a rising need for privacy-preserving data collection methods that keep personal information hidden in otherwise usable data. Sometimes data needs to be encrypted for several authentication levels, where a semi-authorized user gains access to data stripped of personal or sensitive information, while a fully-authorized user can recover the full signal. In this paper, we propose a compressive sensing based multi-level encryption to ECG signals to mask possible heartbeat anomalies from semi-authorized users, while preserving the beat structure for heart rate monitoring. Masking is performed both in time and frequency domains. Masking effectiveness is validated using 1D convolutional neural networks for heartbeat anomaly classification, while masked signal usefulness is validated comparing heartbeat detection accuracy between masked and recovered signals. The proposed multi-level encryption method can decrease classification accuracy of heartbeat anomalies by up to 50%, while maintaining a fairly high R-peak detection accuracy.