Development of a cryptography model based on improved filtering, compression and encryption techniques for ECG signal processing

Electrocardiography is the process of producing an electrocardiogram (ECG) which is a convenient tool for identifying people with potential heart diseases which may need immediate referral to a hospital or emergency medical services in E-healthcare. The ECG signal remote monitoring application in the healthcare services face many challenges related to the real-time diagnosis. The noise cancellation of the ECG signal is critical for accurate extraction of useful heart data from ECG. Additionally, the continuous flow of signals may lead to a sheer increase in the volume of the data, the ECG data needs a large memory storage device. At the same time, security and privacy of the data is considered as a significant aspect of remote diagnosis medical application that further increases the volume of data sharing, including the risk factor. This research work proposed a model to combine approaches for ECG denoising, data encoding, and encryption. Further, improved ECG signal processing based on improved filtering, an adaptive lossless compression mechanism, and hybrid cryptography are proposed. For the denoising of the ECG signal, an enhanced and extended Kalman and adaptive Recursive Least Square (RLS) filtering have been used for signal filtering along with Discrete Wavelet Transform (DWT). The compression mechanism is performed using adaptive lossless compression based on Huffman encoding. Furthermore, to increase security, a cryptography mechanism has been employed using the Advanced Encryption Standard (AES) algorithm and Cipher Block Chaining (CBC) operation mode scheme with a 256-bit key. The Diffie-Hellman key exchange and Rivest Shamir Adleman (RSA) key generation algorithms have been used to authenticate the receiver, and key generation for encrypting and decrypting processes, respectively. Consequently, the main contributions of this research work include a high level of security, privacy, encoding with low error reconstruction along with reduced noise and processing time for the ECG signal in e-healthcare services. The proposed model is for denoising, assuring data security, and compression performance for ECG data storage and transmission on MIT-BIH and PTB Diagnostic ECG dataset. The experimental results show that the proposed system model is successfully the denoising, and secure storage and transmission of ECG data. Based on the simulation results show a decrease for SNR by SNRimp of 55 in dB, a significant improvement of 21.92 for MSE and good accuracy for PSNR and CC. Furthermore, the throughput average of CR is enhanced by 26.66 and 0.8416 for PRD compared with existing different compression schemes for the ECG signal. Finally, the proposed system model is utilized for high-level security against for various kinds of attacks such as denial-of-service (DoS), Distributed DoS, privacy attack, and Man-in-the-middle (MitM)

Compression and encryption for ECG biomedical signal in healthcare system

The ECG data needs large memory storage device due to continuous heart rate logs and vital parameter storage. Thus, efficient compression schemes are applied to it before sending it to the telemedicine center for monitoring and analysis. Proper compression mechanisms can not only improve the storage efficiency but also help in faster porting of data from one device to another due to its compact size. Also, the collected ECG signals are processed through various filtering techniques to remove unnecessary noise and then compressed. In our scheme, we propose use of buffer blocks, which is quite novel in this field. Usage of highly efficient methods for peak detection, noise removal, compression and encryption enable seamless and secure transmission of ECG signal from sensor to the monitor. This work further makes use of AES 256 CBC mode, which is barely used in embedded devices, proves to be very strong and efficient in ciphering of the information. The PRD outcome of proposed work comes as 0.41% and CR as 0.35%, which is quite better than existing schemes. Experimental results prove the efficiency of proposed schemes on five distinct signal records from MIT-BIH arrhythmia datasets

Comparative study of several operation modes of AES algorithm for encryption ECG biomedical signal

Biomedical signal processing provides a cross-disciplinary international forum through which research on signal and images measurement and analysis in clinical medicine as well as biological sciences is shared. Electrocardiography (ECG) signal is more frequently used for diagnosis of cardiovascular diseases. However, the ECG signals contain sensitive private health information as well as details that serve to individually distinguish patients. For this reason, the information must be encrypted prior to transmission across public media so as to prevent unauthorized access by adversaries. In this paper, the proposed the use of the Advanced Encryption Standard algorithm (AES), which is one of a symmetric key block cipher with lightweight properties for enhances confidentiality, integrity and authentication in ECG signal transmission. However, some of the challenges arising from the use of this algorithm are computational overhead and level of security, which occur when handling more complex.The AES algorithm has different operation modes using three different key sizes which can be utilized in encrypting the whole sample of ECG biomedical signal in electronic healthcare. The experiments in this research, exhibit comparative study of using five modes of operation in AES algorithm, which are coupled with three key sizes based on the execution time and security level for the encryption of ECG biomedical signals in electronic healthcare application. Thus, we reported that the CBC mode of the AES algorithm is suitable to be applied of security purpose

Synthesis and antimicrobial screening of tetra Schiff bases of 1,2,4,5-tetra (5-amino-1,3,4-thiadiazole-2-yl)benzene

AbstractIn the present study, novel tetra Schiff bases were synthesized by condensation of 1,2,4,5-tetra (5-amino-1,3,4-thiadiazole-2-yl)benzene with different aromatic aldehydes. The chemical structures were confirmed by means of IR, 1H NMR, 13C NMR, and elemental analysis. All compounds were screened for antibacterial (Staphylococcus aureus ATCC-9144, Staphylococcus epidermidis ATCC-155, Micrococcus luteus ATCC-4698, Bacillus cereus ATCC-11778, Escherichia coli ATCC-25922, and Pseudomonas aeruginosa ATCC-2853) and antifungal (Aspergillus niger ATCC-9029 and Aspergillus fumigatus ATCC-46645) activities by paper disc diffusion technique. The minimum inhibitory concentrations (MICs) of the compounds were also determined by agar streak dilution method. Among the synthesized compounds 1,2,4,5-tetra[5-(4-nitrobenzylideneamino)-1,3,4-thiadiazole-2-yl]benzene 7 was found to be the most potent antimicrobial activity with MICs of 3.4, 2.1, 1.2, 2.0, 3.1, 2.4, 1.1, and 1.7μg/mL against the above mentioned respective strains

Sentimental classification analysis of polarity multi-view textual data using data mining techniques

The data and information available in most community environments is complex in nature. Sentimental data resources may possibly consist of textual data collected from multiple information sources with different representations and usually handled by different analytical models. These types of data resource characteristics can form multi-view polarity textual data. However, knowledge creation from this type of sentimental textual data requires considerable analytical efforts and capabilities. In particular, data mining practices can provide exceptional results in handling textual data formats. Besides, in the case of the textual data exists as multi-view or unstructured data formats, the hybrid and integrated analysis efforts of text data mining algorithms are vital to get helpful results. The objective of this research is to enhance the knowledge discovery from sentimental multi-view textual data which can be considered as unstructured data format to classify the polarity information documents in the form of two different categories or types of useful information. A proposed framework with integrated data mining algorithms has been discussed in this paper, which is achieved through the application of X-means algorithm for clustering and HotSpot algorithm of association rules. The analysis results have shown improved accuracies of classifying the sentimental multi-view textual data into two categories through the application of the proposed framework on online polarity user-reviews dataset upon a given topics

A Review On IoT-Based Healthcare Monitoring Systems For Patient In Remote Environments

The Internet of Things (IoT) is a powerful technology that can enable users’ smart devices and sensors to link each other and access the internet for daily usage. It is a platform that can be used by end users to access many smart application fields, such as smart city, smart home, and smart healthcare. In recent years, the healthcare monitoring systems are becoming the major development field that use the IoT as advanced technology enabler. This review is discussing more about the design, functions, and the main uses of the IoT-based healthcare monitoring applications and systems for remote patient environments. Therefore, a deep review has been made to identify the usage, efficiency, and acceptability for the current applications and systems to become more efficient to patients. Different healthcare monitoring systems have employed the IoT to integrate the different wireless interfaces with the cloud-based healthcare services. These services are including the sensing, gathering, processing, storing, and learning more among the patients’ data. The various IoT healthcare applications will help to develop useful and effective solutions by using these systems in practice. From this review it can be proof that the IoT-based healthcare monitoring applications are growing faster to build more healthcare solutions that useful for different healthcare conditions and environment

A Review on Security Challenges and Features in Wireless Sensor Networks: IoT Perspective

The Internet of Things is becoming a very promising paradigm with the extensive market adoption of the development of associated technologies, such as; cloud computing, near-field communications, wireless mobile networks, etc. This will expose the future direction of communication around the world. Wireless Sensor Networks together with the existing communication technologies are enabling the continuous integration of controlling and processing the functionality of the Internet of Things applications. Since Wireless Sensor Networks are typically deployed for gathering sensitive information from unattended or hostile environments, they are exposed for security attacks, which are strongly affecting the user privacy and the network performance. There are various security mechanisms and solutions for Wireless Sensor Networks that have been proposed in the previous works. Therefore, it is mandatory to give attention for its applicability and feasibility features in terms of related security challenges based on the Internet of Things perspectives. The purpose of this paper is to explore and show the influence of Wireless Sensor Networks security challenges within the perspective of the Internet of Things and its applications. Consequently, an exploration of the major and minor security requirements in the Wireless Sensor Networks has been made in this paper, accompanied by a classification of the available attacks and threats against these requirements. Finally, a discussion on the Internet of Things security issues and challenges in Wireless Sensor Networks is provided

Antimicrobial resistance among migrants in Europe: a systematic review and meta-analysis

BACKGROUND: Rates of antimicrobial resistance (AMR) are rising globally and there is concern that increased migration is contributing to the burden of antibiotic resistance in Europe. However, the effect of migration on the burden of AMR in Europe has not yet been comprehensively examined. Therefore, we did a systematic review and meta-analysis to identify and synthesise data for AMR carriage or infection in migrants to Europe to examine differences in patterns of AMR across migrant groups and in different settings. METHODS: For this systematic review and meta-analysis, we searched MEDLINE, Embase, PubMed, and Scopus with no language restrictions from Jan 1, 2000, to Jan 18, 2017, for primary data from observational studies reporting antibacterial resistance in common bacterial pathogens among migrants to 21 European Union-15 and European Economic Area countries. To be eligible for inclusion, studies had to report data on carriage or infection with laboratory-confirmed antibiotic-resistant organisms in migrant populations. We extracted data from eligible studies and assessed quality using piloted, standardised forms. We did not examine drug resistance in tuberculosis and excluded articles solely reporting on this parameter. We also excluded articles in which migrant status was determined by ethnicity, country of birth of participants' parents, or was not defined, and articles in which data were not disaggregated by migrant status. Outcomes were carriage of or infection with antibiotic-resistant organisms. We used random-effects models to calculate the pooled prevalence of each outcome. The study protocol is registered with PROSPERO, number CRD42016043681. FINDINGS: We identified 2274 articles, of which 23 observational studies reporting on antibiotic resistance in 2319 migrants were included. The pooled prevalence of any AMR carriage or AMR infection in migrants was 25·4% (95% CI 19·1-31·8; I2 =98%), including meticillin-resistant Staphylococcus aureus (7·8%, 4·8-10·7; I2 =92%) and antibiotic-resistant Gram-negative bacteria (27·2%, 17·6-36·8; I2 =94%). The pooled prevalence of any AMR carriage or infection was higher in refugees and asylum seekers (33·0%, 18·3-47·6; I2 =98%) than in other migrant groups (6·6%, 1·8-11·3; I2 =92%). The pooled prevalence of antibiotic-resistant organisms was slightly higher in high-migrant community settings (33·1%, 11·1-55·1; I2 =96%) than in migrants in hospitals (24·3%, 16·1-32·6; I2 =98%). We did not find evidence of high rates of transmission of AMR from migrant to host populations. INTERPRETATION: Migrants are exposed to conditions favouring the emergence of drug resistance during transit and in host countries in Europe. Increased antibiotic resistance among refugees and asylum seekers and in high-migrant community settings (such as refugee camps and detention facilities) highlights the need for improved living conditions, access to health care, and initiatives to facilitate detection of and appropriate high-quality treatment for antibiotic-resistant infections during transit and in host countries. Protocols for the prevention and control of infection and for antibiotic surveillance need to be integrated in all aspects of health care, which should be accessible for all migrant groups, and should target determinants of AMR before, during, and after migration. FUNDING: UK National Institute for Health Research Imperial Biomedical Research Centre, Imperial College Healthcare Charity, the Wellcome Trust, and UK National Institute for Health Research Health Protection Research Unit in Healthcare-associated Infections and Antimictobial Resistance at Imperial College London

Burnout among surgeons before and during the SARS-CoV-2 pandemic: an international survey

Background: SARS-CoV-2 pandemic has had many significant impacts within the surgical realm, and surgeons have been obligated to reconsider almost every aspect of daily clinical practice. Methods: This is a cross-sectional study reported in compliance with the CHERRIES guidelines and conducted through an online platform from June 14th to July 15th, 2020. The primary outcome was the burden of burnout during the pandemic indicated by the validated Shirom-Melamed Burnout Measure. Results: Nine hundred fifty-four surgeons completed the survey. The median length of practice was 10 years; 78.2% included were male with a median age of 37 years old, 39.5% were consultants, 68.9% were general surgeons, and 55.7% were affiliated with an academic institution. Overall, there was a significant increase in the mean burnout score during the pandemic; longer years of practice and older age were significantly associated with less burnout. There were significant reductions in the median number of outpatient visits, operated cases, on-call hours, emergency visits, and research work, so, 48.2% of respondents felt that the training resources were insufficient. The majority (81.3%) of respondents reported that their hospitals were included in the management of COVID-19, 66.5% felt their roles had been minimized; 41% were asked to assist in non-surgical medical practices, and 37.6% of respondents were included in COVID-19 management. Conclusions: There was a significant burnout among trainees. Almost all aspects of clinical and research activities were affected with a significant reduction in the volume of research, outpatient clinic visits, surgical procedures, on-call hours, and emergency cases hindering the training. Trial registration: The study was registered on clicaltrials.gov "NCT04433286" on 16/06/2020