IoT based ECG monitoring system with encryption and authentication in secure data transmission for clinical health care approach

An efficient medical data encryption scheme using selective shuffling and inter-intra pixel diffusion IoT-enabled secure E-healthcare framework

Security in e-healthcare applications such as Telemedicine is crucial in safeguarding patients' sensitive data during transmission. The proposed system measures the patient's health parameters, such as body temperature and pulse rate, using LM35 and pulse sensors, respectively. The sensor data and the patient's medical image are encrypted in the Raspberry Pi 3 B + processor using Python's proposed text and medical image encryption scheme. The encrypted data is transmitted via the Thing Speak cloud and received by another Raspberry Pi at the receiver to decrypt the cipher data. The flask webserver can view the decrypted data by the doctor at the other end. This IoT implementation of secure Electronic Health Record (EHR) transmission employs text and medical image encryption schemes using a Combined Chaotic System (CCS). The CCS generates the chaotic key sequences to shuffle the medical image row-wise and column-wise. Then, selective shuffling between the cut-off points breaks the statistical relationship between the neighbouring pixels. Finally, the intra and inter-pixel diffusion is carried out using bit permutation and bit-wise XOR operation to create a highly random cipher image. The initial seed for inter-pixel diffusion is obtained from the hash of intra-pixel diffused images to resist chosen plain text and cipher text attacks. The efficiency of the developed medical image encryption algorithm is tested against various attack analyses. The results and the security analyses validate the effectiveness of the proposed scheme.

Enhancing safety in surface mine blasting operations with IoT based ground vibration monitoring and prediction system integrated with machine learning

Monitoring and predicting ground vibration levels during blasting operations is essential to safeguard mining sites and surrounding communities. This study introduces an IoT-based ground vibration monitoring device specifically designed for limestone mining operations, combined with machine learning algorithms to predict ground vibration intensity. The primary aim is to provide an efficient predictive tool for anticipating hazardous vibration levels, enabling proactive safety measures. A comparative analysis with the industry-standard Minimate Blaster indicates high accuracy of the IoT device, with percentage errors as low as 0.803% across multiple blasts. The study also employed Support Vector Regression (SVR), Gradient Boosting Regression (GBR), and Random Forest (RF) algorithms to predict Peak Particle Velocity (PPV) values. Among these, the Random Forest model outperformed the others, achieving an R2 score of 0.92, Mean Absolute Error (MAE) of 0.21, and Root Mean Squared Error (RMSE) of 0.31. These findings underscore the reliability and predictive accuracy of the IoT-integrated Random Forest model, suggesting that it can significantly contribute to enhancing safety and operational efficiency in mining. The research highlights the potential of IoT and machine learning technologies to transform ground vibration monitoring, promoting safer and more sustainable mining practices.

Analysis of Cardiac Arrhythmias Based on ResNet-ICBAM-2DCNN Dual-Channel Feature Fusion

Cardiovascular disease (CVD) poses a significant challenge to global health, with cardiac arrhythmia representing one of its most prevalent manifestations. The timely and precise classification of arrhythmias is critical for the effective management of CVD. This study introduces an innovative approach to enhancing arrhythmia classification accuracy through advanced Electrocardiogram (ECG) signal processing. We propose a dual-channel feature fusion strategy designed to enhance the precision and objectivity of ECG analysis. Initially, we apply an Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) and enhanced wavelet thresholding for robust noise reduction. Subsequently, in the primary channel, region of interest features are emphasized using a ResNet-ICBAM network model for feature extraction. In parallel, the secondary channel transforms 1D ECG signals into Gram angular difference field (GADF), Markov transition field (MTF), and recurrence plot (RP) representations, which are then subjected to two-dimensional convolutional neural network (2D-CNN) feature extraction. Post-extraction, the features from both channels are fused and classified. When evaluated on the MIT-BIH database, our method achieves a classification accuracy of 97.80%. Compared to other methods, our approach of two-channel feature fusion has a significant improvement in overall performance by adding a 2D convolutional network. This methodology represents a substantial advancement in ECG signal processing, offering significant potential for clinical applications and improving patient care efficiency and accuracy.

Superimposed Semantic Communication for IoT-Based Real-Time ECG Monitoring

Real-time electrocardiogram (ECG) monitoring and diagnosis through Internet of Things (IoT) are crucial for addressing the severity and timely treatment of cardiovascular diseases, enabling timely intervention and preventing life-threatening complications. However, current ECG monitoring research predominantly focuses on individual aspects such as signal compression, diagnostic analysis, or secure transmission, lacking joint optimization of various modules in IoT scenarios. To address this gap, this work proposes a novel framework based on superimposed semantic communication for real-time ECG monitoring in IoT. The framework comprises three hierarchical levels: the edge level for data collection and processing, the relay level for signal compression and coding, and the cloud level for data analysis and reconstruction. The proposed framework offers several unique advantages. By employing semantic encoding guided by ECG classification tasks, it selectively extracts crucial features within and between signals, improving compression ratio and adaptability to channel noise. The superimposed semantic encoding achieves content encryption without requiring any additional operations. Moreover, the framework utilizes lightweight anomaly detection neural networks, reducing edge device power consumption and conserving communication resources. Simulation and real experimental results demonstrate that the proposed method achieves real-time encoding and transmission of ECG signals with a compression ratio of 0.019 on the MIT-BIH dataset. Furthermore, it attains a heartbeat classification accuracy of 0.988 and a reconstruction error of 0.061.

A robust and secured fusion based hybrid medical image watermarking approach using RDWT-DWT-MSVD with Hyperchaotic system-Fibonacci Q Matrix encryption

Digital image watermarking, the process of marking a host image with a watermark, is generally used to authenticate the data. In the medical field, it is of utmost importance to verify the authenticity of the data using Medical Image Watermarking (MIW), especially in e-healthcare applications. Recently, MIW with image fusion, the merging of multimodal images to improve image quality, is being widely utilized to make diagnosis more accessible and precise with the verified data. This paper offers a durable and secure fusion-based hybrid MIW approach. The method initially used Fast Filtering (FF) to merge two medical images from different modalities to form the cover image. A first-level Redundant Discrete Wavelet Transform (RDWT) is employed on this host image to locate the component with the highest entropy. Then a single-level Discrete Wavelet Transform (DWT) is applied to it. It performed a Multi-resolution Singular Value Decomposition (MSVD) on the wavelet decomposed component and the embedding watermark. Finally, a Hyperchaotic System-Fibonacci Q Matrix (HFQM) encryption system was utilized, which increases the watermarked image's security. Here, using various medical images, the performance of the proposed technique is evaluated. Without any attacks, the approach achieved a maximum Peak Signal to Noise Ratio (PSNR) of 90.31 dB and a Structural Similarity Index Matrix (SSIM) of value 1. Various watermarking assaults were employed to test the proposed method's resilience. The suggested technique achieved a perfect value of 1 for the Normalised Correlation (NC) for almost all attacks with acceptable imperceptibility, which substantially improves over current procedures. The suggested technique's average embedding and extraction times are 0.3958 and 0.4721 seconds, respectively, which are pretty short compared to existing approaches.

A multi-label classification system for anomaly classification in electrocardiogram

Automatic classification of ECG signals has become a research hotspot, and most of the research work in this field is currently aimed at single-label classification. However, a segment of ECG signal may contain more than two cardiac diseases, and single-label classification cannot accurately judge all possibilities. Besides, single-label classification performs classification in units of segmented beats, which destroys the contextual relevance of signal data. Therefore, studying the multi-label classification of ECG signals becomes more critical. This study proposes a method based on the multi-label question transformation method-binary correlation and classifies ECG signals by constructing a deep sequence model. Binary correlation simplifies the learning difficulty of deep learning models and converts multi-label problems into multiple binary classification problems. The experimental results are as follows: F1 score is 0.767, Hamming Loss is 0.073, Coverage is 3.4, and Ranking Loss is 0.262. It performs better than existing work.