Biosignal Compression Toolbox for Digital Biomarker Discovery

A critical challenge to using longitudinal wearable sensor biosignal data for healthcare applications and digital biomarker development is the exacerbation of the healthcare “data deluge,” leading to new data storage and organization challenges and costs. Data aggregation, sampling rate minimization, and effective data compression are all methods for consolidating wearable sensor data to reduce data volumes. There has been limited research on appropriate, effective, and efficient data compression methods for biosignal data. Here, we examine the application of different data compression pipelines built using combinations of algorithmic- and encoding-based methods to biosignal data from wearable sensors and explore how these implementations affect data recoverability and storage footprint. Algorithmic methods tested include singular value decomposition, the discrete cosine transform, and the biorthogonal discrete wavelet transform. Encoding methods tested include run-length encoding and Huffman encoding. We apply these methods to common wearable sensor data, including electrocardiogram (ECG), photoplethysmography (PPG), accelerometry, electrodermal activity (EDA), and skin temperature measurements. Of the methods examined in this study and in line with the characteristics of the different data types, we recommend direct data compression with Huffman encoding for ECG, and PPG, singular value decomposition with Huffman encoding for EDA and accelerometry, and the biorthogonal discrete wavelet transform with Huffman encoding for skin temperature to maximize data recoverability after compression. We also report the best methods for maximizing the compression ratio. Finally, we develop and document open-source code and data for each compression method tested here, which can be accessed through the Digital Biomarker Discovery Pipeline as the “Biosignal Data Compression Toolbox,” an open-source, accessible software platform for compressing biosignal data.

Design and Development of a Hardware Efficient Image Compression Improvement Framework

Background: In the region of image processing, a varied number of methods have already initiated the concept of data sciences optimization, in which, numerous global researchers have put their efforts upon the reduction of compression ratio and increment of PSNR. Additionally, the efforts have also separated into hardware and processing sections, that would help in emerging more prospective outcomes from the research. In this particular paper, a mystical concept for the image segmentation has been developed that helps in splitting the image into two different halves’, which is further termed as the atomic image. In-depth, the separations were done on the bases of even and odd pixels present within the size of the original image in the spatial domain. Furthermore, by splitting the original image into an atomic image will reflect an efficient result in experimental data. Additionally, the time for compression and decompression of the original image with both Quadtree and Huffman is also processed to receive the higher results observed in the result section. The superiority of the proposed schemes is further described upon the comparison basis of performances through the conventional Quadtree decomposition process. Objective : The objective of this present work is to find out the minimum resources required to reconstruct the image after compression. Method : The popular method of quadtree decomposition with Huffman encoding used for image compression. Results : The proposed algorithm was implemented on six types of images and got maximum PSNR of 30.12dB for Lena Image and a maximum compression ratio of 25.96 for MRI image. Conclusion: Different types of images are tested and a high compression ratio with acceptable PSNR was obtained.

Burrows–Wheeler Transform Based Lossless Text Compression Using Keys and Huffman Coding

Text compression is one of the most significant research fields, and various algorithms for text compression have already been developed. This is a significant issue, as the use of internet bandwidth is considerably increasing. This article proposes a Burrows–Wheeler transform and pattern matching-based lossless text compression algorithm that uses Huffman coding in order to achieve an excellent compression ratio. In this article, we introduce an algorithm with two keys that are used in order to reduce more frequently repeated characters after the Burrows–Wheeler transform. We then find patterns of a certain length from the reduced text and apply Huffman encoding. We compare our proposed technique with state-of-the-art text compression algorithms. Finally, we conclude that the proposed technique demonstrates a gain in compression ratio when compared to other compression techniques. A small problem with our proposed method is that it does not work very well for symmetric communications like Brotli.

An enhanced lossless compression with cryptography hybrid mechanism for ECG biomedical signal monitoring

Due to their use in daily life situation, demand for remote health applications and e-health monitoring equipment is growing quickly. In this phase, for fast diagnosis and therapy, information can be transferred from the patient to the distant clinic. Nowadays, the most chronic disease is cardiovascular diseases (CVDs). However, the storage and transmission of the ECG signal, consumes more energy, bandwidth and data security which is faced many challenges. Hence, in this work, we present a combined approach for ECG data compression and cryptography. The compression is performed using adaptive Huffman encoding and encrypting is done using AES (CBC) scheme with a 256-bit key. To increase the security, we include Diffie-Hellman Key exchange to authenticate the receiver, RSA key generation for encrypting and decrypting the data. Experimental results show that the proposed approach achieves better performance in terms of compression and encryption on MIT-BIH ECG dataset.