Integrating the Exercise and Environmental Data Into a Digital ECG Structure by Watermarking Technique

Exercise test is worldwide recognized as a valuable tool for investigating ST segment-based ischemia markers. Due to load-related risk, the test is reserved for office use, what causes inconvenience, limits patients participation rate and precludes screening for early ischemia stages. Transferring the diagnosis to patients' premises and using everyday activities as a stimulus is an interesting alternative, but needs reliable recording of physical load data. This paper presents a method for integrating the exercise and environmental data into a digital ECG structure by watermarking technique. The method analyses the time-scale ECG representation, detects the bandgap, where the bandwidth of actual cardiac content is lower than the throughput of digital series, detects the noise and replaces it by exercise-related data. Unless in irregular signals, the capacity of data container can accommodate an accompanying accelerometer and environment-related signals without deteriorating the ECG content. This makes possible to perform ECG exercise test in home conditions without additional transmission channels or data structures. The method was tested with CSE database accordingly to EN60601-2-25:2015 and proved the watermarked ECG to maintain the wave borders accuracy within tolerance limits. Consequently, restoration of original ECG record is not necessary. The method was also tested with anonymized stress-test records, which were watermarked with accelerometer data and re-interpreted to yield results fairly comparable to original diagnoses.

[1]  R. Suganya,et al.  Steganography techniques for ECG signals : A survey , 2016, 2016 11th International Conference on Industrial and Information Systems (ICIIS).

[2]  P. Augustyniak PURSUIT OF THE ECG INFORMATION DENSITY BY DATA CANCELLING IN TIME-FREQUENCY DOMAIN , 2002 .

[3]  P. Augustyniak Analysis of ECG bandwidth gap as a possible carrier for supplementary digital data , 2012, 2012 Computing in Cardiology.

[4]  K. P. Indiradevi,et al.  Integer-to-Integer Wavelet Transform Based ECG Steganography for Securing Patient Confidential Information , 2016 .

[5]  Nilanjan Dey,et al.  Watermarking in Biomedical Signal Processing , 2017 .

[6]  P. Augustyniak Moving window signal concatenation for spectral analysis of ECG waves , 2010, 2010 Computing in Cardiology.

[7]  V. B. Raskar,et al.  SURVEY PAPER ON WAVELET BASED ECG STEGANOGRAPHY , 2015 .

[8]  R A Bruce,et al.  Methods of exercise testing. Step test, bicycle, treadmill, isometrics. , 1974, The American journal of cardiology.

[9]  Weiming Zhang,et al.  Protecting patient confidential information based on ECG reversible data hiding , 2015, Multimedia Tools and Applications.

[10]  J H van Bemmel,et al.  A reference data base for multilead electrocardiographic computer measurement programs. , 1987, Journal of the American College of Cardiology.

[11]  Pablo Laguna,et al.  A wavelet-based ECG delineator: evaluation on standard databases , 2004, IEEE Transactions on Biomedical Engineering.

[12]  Deepak L. Bhatt,et al.  2013 ACCF/AHA guideline for the management of ST-elevation myocardial infarction: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines. , 2013, Circulation.

[13]  Chien-Ding Lee,et al.  A Cryptographic Key Management Solution for HIPAA Privacy/Security Regulations , 2008, IEEE Transactions on Information Technology in Biomedicine.

[14]  Piotr Augustyniak Encoding the electrocardiogram details in the host record's bandgap for authorization-dependent ECG quality , 2014, Computing in Cardiology 2014.

[15]  Ayman Ibaida,et al.  A low complexity high capacity ECG signal watermark for wearable sensor-net health monitoring system , 2011, 2011 Computing in Cardiology.