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Agnieszka Świerkosz

Bio: Agnieszka Świerkosz is an academic researcher from AGH University of Science and Technology. The author has contributed to research in topics: Digital watermarking & Data security. The author has an hindex of 1, co-authored 3 publications receiving 9 citations.

Papers
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Journal ArticleDOI
11 Oct 2018-Sensors
TL;DR: Experimental work on the assessment of the loss of ECG (electrocardiogram signal) diagnostic quality from the industrial standard EN60601-2-25:2015 point of view is presented and the Symlet of 11-th order is found as the best of the wavelets that were tested.
Abstract: Watermarking is currently investigated as an efficient and safe method of embedding additional patient or environment-related data into the electrocardiogram. This paper presents experimental work on the assessment of the loss of ECG (electrocardiogram signal) diagnostic quality from the industrial standard EN60601-2-25:2015 point of view. We implemented an original time-frequency watermarking technique with an adaptive beat-to-beat lead-independent data container design. We tested six wavelets, six coding bit depth values (including the automatic noise-dependent one) and two types of watermark content to find the conditions that are necessary for watermarked ECG to maintain the compliance with International Electrotechnical Commission (IEC) requirements for interpretation performance. Unlike other authors, we did not assess the differences of signal values, but errors in ECG wave delineation results. The results of a total of 7300 original and watermarked 10 s ECGs were statistically processed to reveal possible interpretation quality degradation due to watermarking. Finally we found (1) the Symlet of 11-th order as the best of the wavelets that were tested; (2) the important role of ECG wave delineation and noise tracking procedures; (3) the high influence of the watermark-to-noise similarity of amplitude and values distribution and (4) the stability of the watermarking capacity for different heart rates in atrial rhythms.

10 citations

Journal ArticleDOI
TL;DR: The author recommends selected methods for personal self-check of glucose level and stresses on the importance of regularly checking blood-related parameters.
Abstract: Abstract Diabetes mellitus is a group of metabolic diseases caused by malfunction of blood sugar regulatory processes and has been reported as related to 8.3% of adult population, i.e. nearly 400 million people worldwide. This paper provides a review of facts and principles important for understanding the regulation mechanisms and the role of insulin. The author relies on mathematical modeling of these mechanisms and provides few formulas and computer applications dedicated for use in diabetes. The modeling aims to find a correct dose of insulin as a response to a series of measurement results on glucose concentration. In conclusion, the author recommends selected methods for personal self-check of glucose level and stresses on the importance of regularly checking blood-related parameters.

1 citations

Book ChapterDOI
01 Jan 2017
TL;DR: This paper presents a review of solutions applied to ECG watermarking in both: local area and wide area networks and discusses their features and hierarchy in aspect of personalized telemedicine of cardiovascular diseases.
Abstract: Despite fast growth of scope and availability of telemedicine, the concern of data security still remains one of crucial unresolved problems. This paper presents a review of solutions applied to ECG watermarking in both: local area and wide area networks. Digital watermarking provides a simple yet effective way of data protection against unauthorized access and modification, authentication of the sender and secret data containers for additional data. Thanks to a thorough, cardiology-oriented data analysis, all this is possible without alteration of medical content of the record. The review is followed by a discussion of their features and hierarchy in aspect of personalized telemedicine of cardiovascular diseases. It is possible to code in this transmission any other information. Why not to invent a new way of watermarking? This paper shows selected results of the work about this topic.

1 citations


Cited by
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Journal ArticleDOI
11 Oct 2018-Sensors
TL;DR: Experimental work on the assessment of the loss of ECG (electrocardiogram signal) diagnostic quality from the industrial standard EN60601-2-25:2015 point of view is presented and the Symlet of 11-th order is found as the best of the wavelets that were tested.
Abstract: Watermarking is currently investigated as an efficient and safe method of embedding additional patient or environment-related data into the electrocardiogram. This paper presents experimental work on the assessment of the loss of ECG (electrocardiogram signal) diagnostic quality from the industrial standard EN60601-2-25:2015 point of view. We implemented an original time-frequency watermarking technique with an adaptive beat-to-beat lead-independent data container design. We tested six wavelets, six coding bit depth values (including the automatic noise-dependent one) and two types of watermark content to find the conditions that are necessary for watermarked ECG to maintain the compliance with International Electrotechnical Commission (IEC) requirements for interpretation performance. Unlike other authors, we did not assess the differences of signal values, but errors in ECG wave delineation results. The results of a total of 7300 original and watermarked 10 s ECGs were statistically processed to reveal possible interpretation quality degradation due to watermarking. Finally we found (1) the Symlet of 11-th order as the best of the wavelets that were tested; (2) the important role of ECG wave delineation and noise tracking procedures; (3) the high influence of the watermark-to-noise similarity of amplitude and values distribution and (4) the stability of the watermarking capacity for different heart rates in atrial rhythms.

10 citations

Journal ArticleDOI
TL;DR: Evaluation demonstrates the usefulness of the proposed methodology in successfully embedding the patient information without distorting the important medical information in an ECG signal.
Abstract: At present, a patient’s demography, such as name, age, and gender are stored separately from the acquired electrocardiogram (ECG) signal. This multiple storage mechanisms can create a severe threat to the reliability of diagnostics if the link between the demography data and the ECG signal breaks, either intentionally or unintentionally. This issue has become more prominent in recent years due to the use of a large number of wearable devices for physiological signal collection, especially in remote or non-clinical settings. In order to address this problem, in this paper, we propose a novel mechanism to embed patient’s information within an ECG signal without degrading the accuracy of the physiological information contained in the ECG signal. In this work, a methodology is presented to find the less-significant region of the ECG signal. Then, the patient information is hidden in this region by modifying the selected discrete cosine transform (DCT) coefficients of the signal using our proposed embedding and decoding algorithms. Moreover, the patient information hidden in the ECG signal is able to resist filtering attack, such as high-pass filtering, which generally occur with the ECG signal processing. This is achieved via the use of error buffers in the embedding algorithm. The proposed mechanism can extract the embedded patient information, either in the presence or without the filtering attack. Moreover, a specifically designed synchronization sequence is added to identify the patient data embedded regions of the ECG signal at the decoding end. Further, as a security measure, the embedded patient details are scrambled using a secret key to protect the privacy of the patient. Our evaluation demonstrates the usefulness of the proposed methodology in successfully embedding the patient information without distorting the important medical information in an ECG signal.

8 citations

Journal ArticleDOI
TL;DR: A comprehensive survey on data hiding techniques for smart healthcare applications is presented in this article, where the authors introduce various aspects of data hiding along with major properties, generic embedding and extraction process, and recent applications.
Abstract: Due to smart healthcare systems highly connected information and communications technologies, sensitive medical information and records are easily transmitted over the networks. However, stealing of healthcare data is increasing crime every day to greatly impact on financial loss. In order to this, researchers are developing various cost-effective bio-signal based data hiding techniques for smart healthcare applications. In this paper, we first introduce various aspects of data hiding along with major properties, generic embedding and extraction process, and recent applications. This survey provides a comprehensive survey on data hiding techniques, and their new trends for solving new challenges in real-world applications. Then, we survey the various notable bio-signal based data hiding techniques. The summary of some notable techniques in terms of their objective, type of data hiding, methodology and database used, performance metrics, important features, and limitations are also presented in tabular form. At the end, we discuss the major issues and research directions to explore the promising areas for future research.

7 citations

Journal ArticleDOI
TL;DR: Simulations confirmed that hidden bits were extracted without distortion and the original ECG signal can be completely recovered and the efficient reversible data hiding method is robust against attacks such as noise addition, inversion, truncation, translations, and so on.
Abstract: Based on the processing of one-dimensional fast discrete cosine transform (1D FDCT) coefficients, we present an efficient reversible data hiding method for electrocardiogram (ECG) signal. The proposed method is implemented in two phases. The purpose of phase-I is to classify the FDCT (host) bundles, where each input bundle will be attributed to one of four different bundles. The aim of phase-II is to embed data bits in the selected coefficients of the classified bundles according to a predetermined bit-index table (which generated from phase-I) via adaptive least significant bit (LSB) technique. Simulations confirmed that hidden bits were extracted without distortion and the original ECG signal can be completely recovered. Furthermore, a good perceived quality and a hiding capacity superior to existing techniques were achieved. Moreover, our method is robust against attacks such as noise addition, inversion, truncation, translations, and so on. Notice that the robustness is rarely seen in conventional reversible ECG steganography methods. Since this method has a fast computation speed, it is feasible for real-time applications and can be installed in the health care devices such as the wearable ECG measure equipment.

5 citations

Journal ArticleDOI
TL;DR: The Modified Dynamic Classification (MDC) algorithm based on the features of the ECG signal is proposed and the novelty is implemented in the compression, embedding, and classification stages to improve the security of patient’s health information.
Abstract: The Electrocardiogram (ECG) signal processing is one of the exciting research areas in recent days. Ensuring security to the patient’s confidential information is a demanding critical task in many healthcare systems. So, the traditional works developed the security mechanisms for embedding the original ECG signal with the image, audio, or video. But, it does not focus on reducing the size of the original message before transmitting it to others. Also, it has significant limitations of inefficient security, increased complexity, and reduced classification accuracy. To rectify this issue, our research proposed the new embedding mechanism to improve the security of patient’s health information. In this system, the original ECG signals compressed at the initial stage by using the proposed Dictionary Matrix Generation (DMG) algorithm. Then, the compressed signals embedded within the cover image by using the Bitwise Embedding (BE) mechanism. At the receiver side, the bedded goal is de-embedded and decompressed by using the DMG and BE algorithms. The features such as spectral and peak values of the signal are extracted for increasing the efficiency of classification. Classification and detection of abnormality present in ECG signal of patient is the most essential part. To achieve this, we proposed the Modified Dynamic Classification (MDC) algorithm based on the features. In this work, the novelty is implemented in the compression, embedding, and classification stages. The proposed system reduces the data loss during transmission, memory storage and time complexity. The overall process evaluated by using PTB diagnostic ECG database. In experiments, the proposed classification technique provides the accuracy of 98.39% and it proved that the proposed method had highest performances than existing methods such as PNN, SVM and RF classification.

4 citations