Evaluation of the carotid artery using wavelet-based analysis of the pulse wave signal

El-Sharo, Sameh; Al-Ghraibah, Amani; Al-Nabulsi, Jamal; Matalgah, Mustafa M.

doi:10.11591/ijece.v12i2.pp1456-1467

Cited by 5 publications

(3 citation statements)

References 16 publications

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“…The vector coefficients are obtained by applying a convolution of the input signal through a low-pass filter for approximate coefficients and a high-pass filter for detailed coefficients. The aforementioned decomposition can be reapplied to the approximation coefficients [17]. The decomposition scheme is shown in Fig.…”

Section: A Data Decompositionmentioning

confidence: 99%

Fuzzy Controlled Wavelet-Based Edge Computing Method for Energy-Harvesting IoT Sensors

Konecny

Prauzek

Borova

2023

IEEE Internet Things J.

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The study presents a novel edge computing (EC) method based on a discrete wavelet transform (DWT) and fuzzy logic controller suitable for application with energy harvesting IoT sensors. The authors propose a new solution to address information latency in an IoT device when compressed data with high information density are transmitted to the cloud with high priority or detailed information is added to the cloud when the energy state in the IoT device is sufficient. The solution potentially delivers a completely lossless scenario for low-power sensors, a significant benefit that state-of-the-art methods do not provide. The article describes the hardware model for an IoT device, input and predicted energy data, and a methodology for designing the parameters of DWT and fuzzy logic controllers. The results of the study indicate that the proposed EC method achieved full data transmission in contrast to the reference solution which had the worst case parameters of maximum outage and penalties caused by delayed data. The average delay in uploading approximate data was 0.51 days with the proposed fuzzy controller EC method compared to reference methods, which have an average delay of at least 0.91 days. The results also highlighted the importance of the trade-off between information latency and reliable functionality. The results are discussed in terms of an innovative approach which features an IoT sensor that maximizes its own energy consumption according to the data measured from specific parameters.

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Section: A Data Decompositionmentioning

confidence: 99%

Fuzzy Controlled Wavelet-Based Edge Computing Method for Energy-Harvesting IoT Sensors

Konecny

Prauzek

Borova

2023

IEEE Internet Things J.

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“…The earliest type of wavelet is the Haar wavelet. The Haar mother wavelet is a mathematical function defined by Sameh et al [13] ( ) = 7 1 89 0 ≤ ≤ 1 2 / −1 89 1 2 ≤ / ≤ 1 0 ;<ℎ>? 8@>…”

Section: Definition Of Wavelet Eclay [6]mentioning

confidence: 99%

Frequency Domain Analysis of Nigeria All Share and Capital Index from 1989-2010 (A Case Study of Nigerian Stock Market)

Osaro¹,

Suleiman²

2022

PAMJ

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In recent years the method of wavelet analysis has been opened to researchers. It is the analyses of data at different level of decomposition and can capture the characteristics of data series in all decomposition level. In this research work, data was collected on the Nigerian stock index for All Share and Capital market indexes . The data were analyzed by wavelet method to detect the aberrant observations (AOs) over the period under study for the two indexes. Akaike information criterion (AIC) was also used to detect the 'best model' for the two indexes using some distributions. A total of seventeen and eleven AOs were detected from the original data collected on All Share and Capital Market indexes respectively. In the first, second and third resolutions, a total of four, two and two AOs were detected from the All Share index, while a total of five, four and three AOs were detected from that of Capital Market index. The results obtained showed the AOs detected in the analysis of the original data maintain the same or closely the same positions as that obtained from the analysis of the decomposed data for the two stock indexes. It was observed that the index of stocks in March, July and December are more and less in February, March, and November for the two indexes. The AIC results show that, the Cauchy distribution has the smallest AIC values among the distributions used, which means is the 'best model'.

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“…Wavelet transformation (WT) has become famous in biomedical signals since the 1990s for giving a progressive interpretation of the data. WT extracts the features from signals by decomposing input data into smooth patterns and then performing reconstruction of the signals, which gives a better understanding [13]. WT has the following two types: continuous and discrete wavelets.…”

Section: Introductionmentioning

confidence: 99%

A Denoising and Fourier Transformation-Based Spectrograms in ECG Classification Using Convolutional Neural Network

Safdar

Nowak

Pałka

2022

Sensors

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The non-invasive electrocardiogram (ECG) signals are useful in heart condition assessment and are found helpful in diagnosing cardiac diseases. However, traditional ways, i.e., a medical consultation required effort, knowledge, and time to interpret the ECG signals due to the large amount of data and complexity. Neural networks have been shown to be efficient recently in interpreting the biomedical signals including ECG and EEG. The novelty of the proposed work is using spectrograms instead of raw signals. Spectrograms could be easily reduced by eliminating frequencies with no ECG information. Moreover, spectrogram calculation is time-efficient through short-time Fourier transformation (STFT) which allowed to present reduced data with well-distinguishable form to convolutional neural network (CNN). The data reduction was performed through frequency filtration by taking a specific cutoff value. These steps makes architecture of the CNN model simple which showed high accuracy. The proposed approach reduced memory usage and computational power through not using complex CNN models. A large publicly available PTB-XL dataset was utilized, and two datasets were prepared, i.e., spectrograms and raw signals for binary classification. The highest accuracy of 99.06% was achieved by the proposed approach, which reflects spectrograms are better than the raw signals for ECG classification. Further, up- and down-sampling of the signals were also performed at various sampling rates and accuracies were attained.

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Evaluation of the carotid artery using wavelet-based analysis of the pulse wave signal

Cited by 5 publications

References 16 publications

Fuzzy Controlled Wavelet-Based Edge Computing Method for Energy-Harvesting IoT Sensors

Fuzzy Controlled Wavelet-Based Edge Computing Method for Energy-Harvesting IoT Sensors

Frequency Domain Analysis of Nigeria All Share and Capital Index from 1989-2010 (A Case Study of Nigerian Stock Market)

A Denoising and Fourier Transformation-Based Spectrograms in ECG Classification Using Convolutional Neural Network

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