Electrocardiogram Data Transmission Research Articles

Enhancing Security of Telemedicine Data: A Multi-Scroll Chaotic System for ECG Signal Encryption and RF Transmission.

Protecting sensitive patient data, such as electrocardiogram (ECG) signals, during RF wireless transmission is essential due to the increasing demand for secure telemedicine communications. This paper presents an innovative chaotic-based encryption system designed to enhance the security and integrity of telemedicine data transmission. The proposed system utilizes a multi-scroll chaotic system for ECG signal encryption based on master-slave synchronization. The ECG signal is encrypted by a master system and securely transmitted to a remote location, where it is decrypted by a slave system using an extended state observer. Synchronization between the master and slave is achieved through the Lyapunov criteria, which ensures system stability. The system also supports Orthogonal Frequency Division Multiplexing (OFDM) and adaptive n-quadrature amplitude modulation (n-QAM) schemes to optimize signal discretization. Experimental validations with a custom transceiver scheme confirmed the system's effectiveness in preventing channel overlap during 2.5 GHz transmissions. Additionally, a commercial RF Power Amplifier (RF-PA) for LTE applications and a development board were integrated to monitor transmission quality. The proposed encryption system ensures robust and efficient RF transmission of ECG data, addressing critical challenges in the wireless communication of sensitive medical information. This approach demonstrates the potential for broader applications in modern telemedicine environments, providing a reliable and efficient solution for the secure transmission of healthcare data.

Secure transmission and monitoring of ECG signals based on chaotic mapping algorithms

ABSTRACT The rapid advancement of digital healthcare and telemedicine has accentuated the need for robust and secure methods of transmitting sensitive medical data, such as electrocardiogram (ECG) signals. This article presents an innovative approach to ECG signal encryption, leveraging the power of chaotic dynamics through the Henon and Baker maps. Chaotic systems have proven to be formidable tools for encryption due to their inherent unpredictability and sensitivity to initial conditions. In this study, we demonstrate the efficacy of Chaotic Henon Map (CHM) and Chaotic Baker Maps (CBM) in encrypting ECG data, ensuring both data privacy and integrity during transmission. Through a detailed exploration of the CHM and CBM, we elucidate their mathematical foundations and unique properties that make them suitable for ECG encryption. The encryption process involves the generation of chaotic keys, which are utilized to scramble the ECG signal in a way that is virtually impossible to decipher without the correct decryption keys. We also discuss the security features of this encryption method, including resistance against common cryptographic attacks. The results of our experiments demonstrate the robustness of this encryption technique in safeguarding sensitive medical information, making it a valuable addition to the toolkit of secure ECG data transmission methods.

TinyML-Based Classification in an ECG Monitoring Embedded System

Recently, the development of the Internet of Things (IoT) has enabled continuous and personal electrocardiogram (ECG) monitoring. In the ECG monitoring system, classification plays an important role because it can select useful data (i.e., reduce the size of the dataset) and identify abnormal data that can be used to detect the clinical diagnosis and guide further treatment. Since the classification requires computing capability, the ECG data are usually delivered to the gateway or the server where the classification is performed based on its computing resource. However, real-time ECG data transmission continuously consumes battery and network resources, which are expensive and limited. To mitigate this problem, this paper proposes a tiny machine learning (TinyML)-based classification (i.e., TinyCES), where the ECG monitoring device performs the classification by itself based on the machine-learning model, which can reduce the memory and the network resource usages for the classification. To demonstrate the feasibility, after we configure the convolutional neural networks (CNN)-based model using ECG data from the Massachusetts Institute of Technology (MIT)-Beth Israel Hospital (BIH) arrhythmia and the Physikalisch Technische Bundesanstalt (PTB) diagnostic ECG databases, TinyCES is validated using the TinyML-supported Arduino prototype. The performance results show that TinyCES can have an approximately 97% detection ratio, which means that it has great potential to be a lightweight and resource-efficient ECG monitoring system.

Deep learning-enabled block scrambling algorithm for securing telemedicine data of table tennis players

In sports, advance sensing technologies generate massive amount of unstructured telemedicine data that need to be refined for accurate diagnosis of underlying diseases. For accurate prediction of diseases and classification of athletes’ data, deep learning algorithms are frequently used at the cloud. However, the transmission of raw data of athletes to the cloud faces numerous challenges. Among them, security and privacy are a major challenge in view of the sensitive and personal information present within the unstructured data. In this paper, first we present a data block scrambling algorithm (without key management) for secured transmission and storage of ECG (electrocardiogram) data of table tennis players at the cloud. A small piece of original data stored at the cloud is used for scrambling the massive amount of remaining ECG data. The secured telemedicine data is then imported into Hadoop Distributed File System for data management, which is read by Spark framework to form Resilient Distributed Datasets. Finally, a deep learning approach is used that extracts useful features, learns the related information, and weights and sums the feature vectors at different layers for classification. Theoretical analysis proves that our proposed approach is highly robust and resilient to brute force attacks and at the same time has a much better accuracy, sensitivity, and specificity as compared to the existing approaches.

Smart biological sensor network for efficient energy consumption

SummaryIn this paper, a link is proposed for wireless body area sensor networks (WBASNs) for the transmission of electrocardiogram (ECG) signal. When the quantity of signal is very high, then it is beneficial that signal should be compressed before transmission. In that situation, the signal compression and error control schemes played an important role in the designing of link for WBASNs. In case of biological sensor network, it is very important that node consumed less power in transmission because the replacement of nodes is a very complex surgical procedure in many medical situations. A compressed sensing concept is introduced here. The main aspect of this paper is to present transmission strategy that provides fast and accurate transmission of ECG signals. In this the smart sensor nodes are utilized. These nodes having sensing, transformation and transmission capability. The nodes sense the ECG signals, and these signal are compressed by Walsh–Hadamard transform method. This compressed signal is transmitted to the module outside the human body. In this module, various error control coding and modulation operations are to be performed on signals, and after that, signals are transmitted to the final destination. The compressed signal occupied less storage space, and ECC provides high data rate. In that manner, the fast transmission of ECG data becomes possible on low data rate sensor network link.

Robust ECG data compression method based on ε-insensitive Huber loss function

Electrocardiogram (ECG) signals are continuously monitored for early diagnosis of heart diseases. However, a long-term monitoring generates large amounts of data at a level that makes storage and transmission difficult. Moreover, these records may be subject to different types of noise distributions resulting from operating conditions. Therefore, an effective and reliable data compression technique is needed for ECG data transmission, storage and analysis without losing the clinical information content. This study proposes the ε-insensitive Huber loss based support vector regression for the compressing of ECG signals. Since the Huber loss function is a mixture of quadratic and linear loss functions, it can properly take into account the different noise types in the data set. Compression performance of the proposed method has been assessed using ECG records from the MIT-BIH arrhythmia database. Experimental results demonstrate that the proposed loss function is an attractive candidate for compressing ECG data.

Less Is More in Biosignal Analysis: Compressed Data Could Open the Door to Faster and Better Diagnosis.

In the digital medicine field, biosignals, such as those of an electrocardiogram (ECG), are collected regularly for screening and diagnosis, and there continues to be an increasingly substantial shift towards collecting long-term ECG signals for remote monitoring, e.g., in smart homes. ECG signal collection is quite simple and only requires the use of inexpensive sensors, an active Internet connection, and a mobile device that acts as the medium between the sensors and the Internet (e.g., a mobile phone or laptop). Despite the ease and convenience of remote ECG data collection and transmission, the amount of time and energy required for the related remote computational processes remains a major limitation. This short note discusses a biosignal approach that uses fewer biomedical data for screening and diagnosis that is, compared to current data collection methods, equally, if not more, efficient.

An adaptive framework for real-time ECG transmission in mobile environments.

Wireless electrocardiogram (ECG) monitoring involves the measurement of ECG signals and their timely transmission over wireless networks to remote healthcare professionals. However, fluctuations in wireless channel conditions pose quality-of-service challenges for real-time ECG monitoring services in a mobile environment. We present an adaptive framework for layered coding and transmission of ECG data that can cope with a time-varying wireless channel. The ECG is segmented into layers with differing importance with respect to the quality of the reconstructed signal. According to this observation, we have devised a simple and efficient real-time scheduling algorithm based on the earliest deadline first (EDF) policy, which decides the order of transmitting or retransmitting packets that contain ECG data at any given time for the delivery of scalable ECG data over a lossy channel. The algorithm takes into account the differing priorities of packets in each layer, which prevents the perceived quality of the reconstructed ECG signal from degrading abruptly as channel conditions worsen, while using the available bandwidth efficiently. Extensive simulations demonstrate this improvement in perceived quality.

Design and development of a wireless internet-based ECG monitoring system for ambulatory applications

The visual observation of the Electrocardiogram (ECG) signal(s) helps in detecting the presence and nature of heart disease(s). This paper presents the design and development of a portable ECG monitoring system integrated with an ethernet link for secured transmission of real-time ECG data using TCP/IP protocol to a remote terminal. To incorporate the advantages of wireless internet technology for ambulatory applications, a highly configurable and future proof model of ECG monitoring system, which can be easily integrated with IEEE 802.11b/g, IP over GSM, CDMA networks and WiMAX, is presented.

ECG Denoising and Compression Using a Modified Extended Kalman Filter Structure

This paper presents efficient denoising and lossy compression schemes for electrocardiogram (ECG) signals based on a modified extended Kalman filter (EKF) structure. We have used a previously introduced two-dimensional EKF structure and modified its governing equations to be extended to a 17-dimensional case. The new EKF structure is used not only for denoising, but also for compression, since it provides estimation for each of the new 15 model parameters. Using these specific parameters, the signal is reconstructed with regard to the dynamical equations of the model. The performances of the proposed method are evaluated using standard denoising and compression efficiency measures. For denosing, the SNR improvement criterion is used, while for compression, we have considered the compression ratio (CR), the percentage area difference (PAD), and the weighted diagnostic distortion (WDD) measure. Several Massachusetts Institute of Technology-Beth Israel Deaconess Medical Center (MIT-BIH) ECG databases are used for performance evaluation. Simulation results illustrate that both applications can contribute to and enhance the clinical ECG data denoising and compression performance. For denoising, an average SNR improvement of 10.16 dB was achieved, which is 1.8 dB more than the next benchmark methods such as MABWT or EKF2. For compression, the algorithm was extended to include more than five Gaussian kernels. Results show a typical average CR of 11.37:1 with WDD << 1.73%. Consequently, the proposed framework is suitable for a hybrid system that integrates these algorithmic approaches for clean ECG data storage or transmission scenarios with high output SNRs, high CRs, and low distortions.

Ein kabelfreies, kapazitiv gekoppeltes EKG-Messsystem / Wireless ECG measurement system with capacitive coupling

This paper describes a measurement system that captures an electrocardiogram (ECG) using capacitively coupled electrodes. The measurement system was integrated into an off-the-shelf office chair (so-called "Aachen SmartChair"). Whereas for classical ECG measurement adhesive is used to attach conductively coupled electrodes to bare skin, the system presented allows ECG measurement through clothing without direct skin contact. Furthermore, a ZigBee communication module was integrated to allow wireless transmission of ECG data to a PC or an ICU patient monitor. For system validation, classical ECG using conductive electrodes was obtained simultaneously. First measurement results, including variations of cloth thickness and material, are presented and some of the system-specific problems of this approach are discussed.

ECG data compression using wavelets and higher order statistics methods.

This paper evaluates the compression performance and characteristics of two wavelet coding compression schemes of electrocardiogram (ECG) signals suitable for real-time telemedical applications. The two proposed methods, namely the optimal zonal wavelet coding (OZWC) method and the wavelet transform higher order statistics-based coding (WHOSC) method, are used to assess the ECG compression issues. The WHOSC method employs higher order statistics (HOS) and uses multirate processing with the autoregressive HOS model technique to provide increasing robustness to the coding scheme. The OZWC algorithm used is based on the optimal wavelet-based zonal coding method developed for the class of discrete "Lipschitizian" signals. Both methodologies were evaluated using the normalized rms error (NRMSE) and the average compression ratio (CR) and bits per sample criteria, applied on abnormal clinical ECG data samples selected from the MIT-BIH database and the Creighton University Cardiac Center database. Simulation results illustrate that both methods can contribute to and enhance the medical data compression performance suitable for a hybrid mobile telemedical system that integrates these algorithmic approaches for real-time ECG data transmission scenarios with high CRs and low NRMSE ratios, especially in low bandwidth mobile systems.

An adaptive real-time ECG compression algorithm with variable threshold.

A real-time compression algorithm has been developed which is suitable for both real-time ECG (electrocardiogram) transmission and ECG data storing. The algorithm represents a modification of the amplitude zone time epoch coding (AZTEC) technique extended with several statistical parameters used to calculate the variable threshold. The proposed algorithm has been applied in the design of a pacemaker followup system for the online ECG data transmission from the pacemaker implanted in a human being to the computer system located at the clinic. >