Lossy compression techniques for EEG signals

Dao, Phuong; Li, Xue Jun; Ngoc, Hung

doi:10.1109/atc.2015.7388309

Cited by 20 publications

(6 citation statements)

References 34 publications

(55 reference statements)

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“…When compared with typical compression based on vector, entropy coding and coding based on predictive approaches provided greater rate of compression without loss. Dao et al 22 introduce a lossy compression techniques for EEG signals. In this article, the EEG signal has been extensively used to examine brain activities and diagnose a variety of brain‐related illnesses.…”

Section: Literature Reviewmentioning

confidence: 99%

A new lossless electroencephalogram compression technique for fog computing‐based IoHT networks

Idrees

Khlief

2023

Int J Communication

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SummaryThe ability to design smarter, more predictive healthcare solutions for use in the community (at work and at home) and in healthcare facilities has been greatly enhanced by recent developments in the Internet of Health Things (IoHT) and cyber physical systems (CPS). These data collected by such medical sensors will be sent in a large quantity to the fog gateway of IoHT networks. These data should be forwarded to far cloud for further analysis and processing. Therefore, sending all of these data over the IoHT network to the data center will impose a significant burden on the IoHT network. This paper proposed a new lossless electroencephalogram (EEG) compression technique (NeLECoT) for fog computing‐based IoHT networks. It encodes the data of the patient at the fog gateway prior to sending it to the data center, thereby reducing the data's size. In the fog node, the NeLECoT combines three efficient techniques: clustering based on density‐based spatial clustering of applications with noise (DBSCAN), RLE (run length encoding), and Huffman encoding (HE). The clustering based on DBSCAN separates a massive volume of captured data into small groups of closely related (or similar) data. RLE encodes clustered EEG data, and the resulting file is encoded with HE. The fog gateway then transmits the encoded file to the cloud. Numerous simulation experiments were carried out, and the findings demonstrated that the suggested NeLECoT achieved better results than the competing techniques in terms of transmitted data size, compression ratio, compression power, compression time, decompression time, and average compression power.

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Section: Literature Reviewmentioning

confidence: 99%

A new lossless electroencephalogram compression technique for fog computing‐based IoHT networks

Idrees

Khlief

2023

Int J Communication

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“…Conversely, lossless algorithms reconstruct the original data without any loss. Lossy algorithms, such as Transform Coding, Discrete Cosine Transform, Discrete Wavelet Transform, and Fractal Compression, are used with multimedia data (e.g., images, audio, and video) and limited non-life-threatening healthcare applications [29], while lossless algorithms, such as Run Length Encoding, Lempel-Ziv-Welch (LZW), Arithmetic Encoding, Huffman Encoding, and Shannon Fano Encoding are used with textual data [30]. In wireless sensor networks, there are five categories of compression techniques [31]: string-based, image-based, compressed sensing, distributed source coding, and data aggregation.…”

Section: Data Compression Techniquesmentioning

confidence: 99%

Optimizing Real-Time ECG Data Transmission in Constrained Environments

Ouda

Badr

Rashwan

et al. 2022

ICC 2022 - IEEE International Conference on Communications

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ECG monitoring systems have a significant role in detecting cardiovascular diseases and reducing the rate of sudden cardiac deaths through early warnings for heart attacks. One of the critical factors in supporting real-time ECG tracking is to guarantee monitoring system availability. This thesis focuses on battery life extension for a 12 Lead ECG patch utilizing compression and classification approaches. the opportunity to explore students' activities at Queen's while applying work-life balance.I would also like to thank all TRL and IoT Lab colleagues, partners of success, for their great support and encouragement towards making a successful and enjoyable academic experience. v

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“…As a result, a huge amount of data is generated and may be stored and transmitted. This consumes a large amount of storage and high bandwidth for transmission [2]. Therefore, many works in this field have made experiments to achieve the best compression ratio with zero or near-zero loss for the data since these data are sensitive and very important for researchers and physicians [3] [4].…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study Using DCT, Delta Modulation, and Double Shift Coding for Compressing Electroencephalogram Data

Hadi

George

2022

eijs

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Storing, transferring, and processing high-dimensional electroencephalogram (EGG) signals is a critical challenge. The goal of EEG compression is to remove redundant data in EEG signals. Medical signals like EEG must be of high quality for medical diagnosis. This paper uses a compression system with near-zero Mean Squared Error (MSE) based on Discrete Cosine Transform (DCT) and double shift coding for fast and efficient EEG data compression. This paper investigates and compares the use or non-use of delta modulation, which is applied to the transformed and quantized input signal. Double shift coding is applied after mapping the output to positive as a final step. The system performance is tested using EEG data files from the CHB-MIT Scalp EEG Database. Compression Ratio (CR) is used to evaluate the compression system performance. The results are encouraging when compared with previous works on the same data samples.

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Lossy compression techniques for EEG signals

Cited by 20 publications

References 34 publications

A new lossless electroencephalogram compression technique for fog computing‐based IoHT networks

A new lossless electroencephalogram compression technique for fog computing‐based IoHT networks

Optimizing Real-Time ECG Data Transmission in Constrained Environments

A Comparative Study Using DCT, Delta Modulation, and Double Shift Coding for Compressing Electroencephalogram Data

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