RETRACTED ARTICLE: Huffman quantization approach for optimized EEG signal compression with transformation technique

Rajasekar, P.; Pushpalatha, M.

doi:10.1007/s00500-020-04804-z

Cited by 19 publications

(11 citation statements)

References 38 publications

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“…The use of data segmentation improves compression time significantly, according to simulation results. The Huffman quantization scheme for optimum EEG signal compression with the transformation method is demonstrated by Rajasekar and Pushpalatha 28 . In this study, they effectively transmitted data using a compression approach without loss termed the Huffman‐based DCT.…”

Section: Literature Reviewmentioning

confidence: 93%

“…The Huffman quantization scheme for optimum EEG signal compression with the transformation method is demonstrated by Rajasekar and Pushpalatha. 28 In this study, they effectively transmitted data using a compression approach without loss termed the Huffman-based DCT. The DCT and its inverse are discussed as a means of enhancing data privacy while simultaneously decreasing data complexity.…”

Section: Paper Organizationmentioning

confidence: 99%

“…Based on the character's relative frequency, it replaced each character with variable-length code. 28 The Huffman algorithm begins by constructing a list of all alphabet symbols in descending probability order. It then builds a tree from the bottom up, with a symbol at each leaf.…”

Section: Huffman Coding Algorithmmentioning

confidence: 99%

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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: 93%

Section: Paper Organizationmentioning

confidence: 99%

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

Idrees

Khlief

2023

Int J Communication

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“…Nonetheless, many techniques were developed for feature extraction of EEG signals, but it has the issue of information loss (Islam et al 2021). The main issues of the EEG signal are noisy data, miss classification, and loss of information (Rajasekar and Pushpalatha 2020;Amin et al 2019). There are a lot of techniques introduced to overcome these issues, such as the EMD-AR technique (Zhang et al 2018), EEG-AR model (Ouyang et al 2020), convolution neural system (Dose et al 2018), and so on, but has the issues of noise in the signal, loss of information, improper classification (Algan and Ulusoy 2021).…”

Section: Introductionmentioning

confidence: 99%

A design of bat-based optimized deep learning model for EEG signal analysis

Gupta¹,

Kanungo²,

Kumar

et al. 2023

Multimed Tools Appl

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Depression is one of the mental illnesses that negatively affect a person's thinking, action, and feeling. Thus the rate of depression is identified by analyzing Electroencephalogram (EEG) signals, but it has the problem of classifying depression rate because of noise. In this paper, a novel Bat-based UNET Signal Analysis (BUSA) framework is designed to organize the depression rate of patients with an EEG dataset. This technique involves preprocessing, feature selection, feature extraction, and classification. After the data training process preprocessing function was activated to remove the noise in the brain signal. Hereafter, the noiseless signal is used for the further process.Here, the fitness of the bat is upgraded in the UNET classification layer. Moreover, the brain signal's feature selection and depression rate were classified using the bat fitness that has helped to gain the desired output. Finally, performance metrics of the proposed BUSA technique are compared with other existing methods regarding the accuracy, AUC, precision, recall, and power. In that, the developed framework has attained better results to classify depression rates.

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“…In general, Various of algorithms are introduced for compressing biomedical signal [26] such as Electrocardiography (ECG) [37], Electromyography (EMG) [54], Electroencephalographic (EEG) [9,47] and Salt sensitive Rat Blood Pressure signal [5]. Concerning PCG signals, several publications have appeared in recent years documenting.…”

Section: Introductionmentioning

confidence: 99%

An efficient compression technique for Foetal phonocardiogram signals in remote healthcare monitoring systems

Fathi

Ahmed

Makhlouf

2022

Multimed Tools Appl

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Remote Healthcare Monitoring Systems (RHMs) that employ fetal phonocardiography (fPCG) signals are highly efficient technologies for monitoring continuous and long-term fetal heart rate. Wearable devices used in RHMs still face a challenge that decreases their efficacy in terms of energy consumption because these devices have limited storage and are powered by batteries. This paper proposes an effective fPCG compression algorithm to reduce RHM energy consumption. In the proposed algorithm, the Discrete Orthogonal Charlier Moment (DOCMs) is used to extract features of the signal. The householder orthonormalization method (HOM) is used with the Charlier Moment to overcome the propagation of numerical errors that occur when computing high-order Charlier polynomials. The proposed algorithm’s performance is evaluated in terms of CR, PRD, SNR, PSNR, and QS and provides the average values 18.33, 0.21, 48.85, 68.86, and 90.88, respectively. The results of the comparison demonstrate the proposed compression algorithm’s superiority over other algorithms. It also tested in terms of compression speed and computational efficiency. The results indicate that the proposed algorithm has a high Compression speed (218.672 bps) and high computational efficiency (21.33). Additionally, the results reveal that the proposed algorithm decreases the energy consumption of a wearable device due to the transmission time decreasing for data by 3.68 s.

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RETRACTED ARTICLE: Huffman quantization approach for optimized EEG signal compression with transformation technique

Cited by 19 publications

References 38 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

A design of bat-based optimized deep learning model for EEG signal analysis

An efficient compression technique for Foetal phonocardiogram signals in remote healthcare monitoring systems

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