An efficient compression of ECG signals using deep convolutional autoencoders

This paper presents a novel data compression and transmission scheme for power reduction in Internet-of-Things (IoT) enabled wireless sensors. In the proposed scheme, data is compressed with both lossy and lossless techniques, so as to enable hybrid transmission mode , support adaptive data rate selection and save power in wireless transmission. Applying the method to electrocardiogram (ECG), the data is first compressed using a lossy compression technique with a high compression ratio (CR). The residual error between the original data and the decompressed lossy data is preserved using entropy coding, enabling a lossless restoration of the original data when required. Average CR of 2.1 $\times$ and 7.8 $\times$ were achieved for lossless and lossy compression respectively with MIT/BIH database. The power reduction is demonstrated using a Bluetooth transceiver and is found to be reduced to 18% for lossy and 53% for lossless transmission respectively. Options for hybrid transmission mode , adaptive rate selection and system level power reduction make the proposed scheme attractive for IoT wireless sensors in healthcare applications.

A Hybrid Data Compression Scheme for Power Reduction in Wireless Sensors for IoT

The increasing proliferation and deployment of ICT in the infrastructure of cities has increased interest in smart cities. The long-term objective of a smart city is to enhance the quality of services provided to citizens and ultimately improve their quality of life. However, incorporating ICT opens up various security and privacy issues in smart cities, along with the people living in them. We briefly present the fundamental design concepts of a smart city and review recent smart city initiatives and projects. After identifying several security vulnerabilities and privacy issues within the context of smart cities that must be addressed, we then discuss various privacy and security solutions, recommendations, and standards for smart cities and their services.

Cybersecurity and Privacy Solutions in Smart Cities

Nanomaterial-based aptamer sensors for arsenic detection

Adaptive Fourier decomposition based ECG denoising

This paper presents a novel electrocardiogram (ECG) compression method for e-health applications by adapting an adaptive Fourier decomposition (AFD) algorithm hybridized with a symbol substitution (SS) technique. The compression consists of two stages: first stage AFD executes efficient lossy compression with high fidelity; second stage SS performs lossless compression enhancement and built-in data encryption, which is pivotal for e-health. Validated with 48 ECG records from MIT-BIH arrhythmia benchmark database, the proposed method achieves averaged compression ratio (CR) of 17.6–44.5 and percentage root mean square difference (PRD) of 0.8–2.0% with a highly linear and robust PRD-CR relationship, pushing forward the compression performance to an unexploited region. As such, this paper provides an attractive candidate of ECG compression method for pervasive e-health applications.

JiaLi Ma

Papers

A Novel ECG Data Compression Method Using Adaptive Fourier Decomposition With Security Guarantee in e-Health Applications