Algorithms for Compression of Electrocardiogram Signals

The study is dedicated to modern methods and algorithms for compression of electrocardiogram (ECG) signals. In its original part, two lossy compression algorithms based on a combination of linear transforms are proposed. These algorithms are with relatively low computational complexity, making them applicable for implementation in low power designs such as mobile devices or embedded systems. Since the algorithms do not provide perfect signal reconstruction, they would find application in ECG monitoring systems rather than those intended for precision medical diagnosis. This monograph consists of abstract, preface, five chapters and conclusion. The chapters are as follows: Chapter 1 — Introduction to ECG; Chapter 2 — Overview of the existing methods and algorithms for ECG compression; Chapter 3 — ECG compression algorithm, based on a combination of linear transforms; Chapter 4 — Improvement of the developed algorithm for ECG compression; Chapter 5 — Experimental investigations. Този труд е посветен на съвременните методи и алгоритми за компресия на електрокардиографски (ЕКГ) сигнали. В оригиналната му част са предложени два алгоритъма за компресия със загуби, които са базирани на комбинация от линейни преобразувания. Тези алгоритми се характеризират със сравнително невисока изчислителна сложност, което дава възможност да бъдат реализирани в устройства с ниска консумация на енергия, като например мобилни устройства или вградени системи. Тъй като алгоритмите не позволяват перфектно възстановяване на сигнала, те биха намерили приложение по-скоро в системите за ЕКГ мониторинг, отколкото в тези, предназначени за прецизна медицинска диагностика. Монографията съдържа резюме, предговор, пет глави и заключение. Главите са както следва: Глава 1 — Въведение в електрокардиографията; Глава 2 — Обзор на съществуващите методи и алгоритми за компресия на ЕКГ сигнали; Глава 3 — Алгоритъм за компресия на ЕКГ сигнали, базиран на комбинация от линейни преобразувания; Глава 4 — Усъвършенстване на разработения алгоритъм за компресия на ЕКГ сигнали; Глава 5 — Експериментални изследвания

Progressive transmission of medical images

A novel adaptive source-channel coding scheme for progressive transmission of medical images with a feedback system is therefore proposed in this dissertation. The overall design includes Discrete Wavelet Transform (DWT), Embedded Zerotree Wavelet (EZW) coding, Joint Source-Channel Coding (JSCC), prioritization of region of interest (RoI), variability of parity length based on feedback, and the corresponding hardware design utilising Simulink. The JSCC can achieve an efficient transmission by incorporating unequal error projection (UEP) and rate allocation. An algorithm is also developed to estimate the number of erroneous data in the receiver. The algorithm detects the address in which the number of symbols for each subblock is indicated, and reassigns an estimated correct data according to a decision making criterion, if error data is detected. The proposed system has been designed based on Simulink which can be used to generate netlist for portable devices. A new compression method called Compressive Sensing (CS) is also revisited in this work. CS exhibits many advantages in comparison with EZW based on our experimental results. DICOM JPEG2000 is an efficient coding standard for lossy or lossless multi-component image coding. However, it does not provide any mechanism for automatic RoI definition, and is more complex compared to our proposed scheme. The proposed system significantly reduces the transmission time, lowers computation cost, and maintains an error-free state in the RoI with regards to the above provided features. A MATLAB-based TCP/IP connection is established to demonstrate the efficacy of the proposed interactive and adaptive progressive transmission system. The proposed system is simulated for both binary and symmetric channel (BSC) and Rayleigh channel. The experimental results confirm the effectiveness of the design.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Telemedicine

Telemedicine is a rapidly evolving field as new technologies are implemented for example for the development of wireless sensors, quality data transmission. Using the Internet applications such as counseling, clinical consultation support and home care monitoring and management are more and more realized, which improves access to high level medical care in underserved areas. The 23 chapters of this book present manifold examples of telemedicine treating both theoretical and practical foundations and application scenarios

Intelligent Biosignal Processing in Wearable and Implantable Sensors

This reprint provides a collection of papers illustrating the state-of-the-art of smart processing of data coming from wearable, implantable or portable sensors. Each paper presents the design, databases used, methodological background, obtained results, and their interpretation for biomedical applications. Revealing examples are brain–machine interfaces for medical rehabilitation, the evaluation of sympathetic nerve activity, a novel automated diagnostic tool based on ECG data to diagnose COVID-19, machine learning-based hypertension risk assessment by means of photoplethysmography and electrocardiography signals, Parkinsonian gait assessment using machine learning tools, thorough analysis of compressive sensing of ECG signals, development of a nanotechnology application for decoding vagus-nerve activity, detection of liver dysfunction using a wearable electronic nose system, prosthetic hand control using surface electromyography, epileptic seizure detection using a CNN, and premature ventricular contraction detection using deep metric learning. Thus, this reprint presents significant clinical applications as well as valuable new research issues, providing current illustrations of this new field of research by addressing the promises, challenges, and hurdles associated with the synergy of biosignal processing and AI through 16 different pertinent studies. Covering a wide range of research and application areas, this book is an excellent resource for researchers, physicians, academics, and PhD or master students working on (bio)signal and image processing, AI, biomaterials, biomechanics, and biotechnology with applications in medicine

An efficient secure ECG compression based on 2D-SPIHT and SIT algorithm

The 2D-SPIHT of the Electrocardiogram (ECG) Telehealth system is still be a concern. The performance in the transmission process of the ECG signal data is kept being improved. The ECG signals containing a large-size of health data that must be secured. This research proposes an efficient combination of a compression and an encryption algorithms. Two-Dimension Set Partitioning in Hierarchical Tree (2D-SPIHT) used to optimally compress signals then combined with recently lightweight encryption algorithm, namely Secure IoT (SIT). This approach also proposes new effective encryption stage by just encrypt the most important information of compression signals that is beat-order. Several scenarios were conducted like encrypting the whole bit-stream and combining 2D-SPIHT with another encryption method, Advanced Encryption Standard (AES). The experiment result shows that proposed method gives good performance in compression and encryption. Encrypting successfully produced encrypted data that is highly different data compared to original data. Finally, the combination between 2DSPIHT and SIT method with encrypting beat-order is the best approach. This got PRD is of 0.650 with compression-encryptiondecryption time is of 83.50 seconds