EEG Signal Denoising Using Haar Transform and Maximal Overlap Discrete Wavelet Transform (MODWT) for the Finding of Epilepsy

Gurumoorthy, Sasikumar; Muppalaneni, Naresh Babu; Kumari, G. Sandhya

doi:10.5772/intechopen.93180

Cited by 5 publications

(3 citation statements)

References 4 publications

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“…By choosing the appropriate frequency level, we are able to remove undesired components present in the signal [25]. MODWT was preferred over traditional wavelet decomposition due to its better alignment of the reconstructed signal with the original time-series [26]. The most suitable wavelet was chosen based on the quantification of how much the R-peaks were enhanced using the maximization of the energy-to-Shannon entropy ratio criterion, proposed by He et al [27].…”

Section: Signal Processing 251 Electrocardiographymentioning

confidence: 99%

Cardiorespiratory Response to Workload Volume and Ergonomic Risk: Automotive Assembly Line Operators’ Adaptations

Furk,

Silva,

Dias

et al. 2024

Applied Sciences

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Repetitive tasks can lead to long-term cardiovascular problems due to continuous strain and inadequate recovery. The automobile operators on the assembly line are exposed to these risks when workload volume changes according to the workstation type. However, the current ergonomic assessments focus primarily on observational and, in some cases, biomechanical methods that are subjective and time-consuming, overlooking cardiorespiratory adaptations. This study aimed to analyze the cardiorespiratory response to distinct workload volumes and ergonomic risk (ER) scores for an automotive assembly line. Sixteen male operators (age = 38 ± 8 years; BMI = 25 ± 3 kg·m2) volunteered from three workstations (H1, H2, and H3) with specific work cycle duration (1, 3, and 5 min respectively). Electrocardiogram (ECG), respiratory inductance plethysmography (RIP), and accelerometer (ACC) data were collected during their shift. The results showed significant differences from the first to the last 10 min, where H3 had its SDRRi reduced (p = 0.014), H1’s phase synchrony and H2’s coordination between thoracic and abdominal movements decreased (p < 0.001, p = 0.039). In terms of ergonomic risk, the moderate-high rank showed a reduction in SDRRi (p = 0.037) and moderate-risk activities had diminished phase synchrony (p = 0.018) and correlation (p = 0.004). Thus, the explored parameters could have the potential to develop personalized workplace adaptation and risk assessment systems.

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Section: Signal Processing 251 Electrocardiographymentioning

confidence: 99%

Cardiorespiratory Response to Workload Volume and Ergonomic Risk: Automotive Assembly Line Operators’ Adaptations

Furk,

Silva,

Dias

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…Wavelets have found application for the enhancement and denoising of medical images and bio signals [ 36 , 37 , 38 ]. In particular, MODWT has been used successfully for other physiological signals such as ECG, electroencephalogram (EEG), and magnetoencephalography (MEG) [ 39 , 40 ], but has also proved suitable, in our work, for the PPG signal. This wavelet is an undecimated wavelet transform similar to the discrete wavelet transform (DWT); however, no down sampling of coefficients is operated for its computation, hence it has a high amount of redundancy.…”

Section: Features Extractionmentioning

confidence: 99%

Photoplethysmography Signal Wavelet Enhancement and Novel Features Selection for Non-Invasive Cuff-Less Blood Pressure Monitoring

Attivissimo

Palma

Nisio

et al. 2023

Sensors

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In this paper, new features relevant to blood pressure (BP) estimation using photoplethysmography (PPG) are presented. A total of 195 features, including the proposed ones and those already known in the literature, have been calculated on a set composed of 50,000 pulses from 1080 different patients. Three feature selection methods, namely Correlation-based Feature Selection (CFS), RReliefF and Minimum Redundancy Maximum Relevance (MRMR), have then been applied to identify the most significant features for BP estimation. Some of these features have been extracted through a novel PPG signal enhancement method based on the use of the Maximal Overlap Discrete Wavelet Transform (MODWT). As a matter of fact, the enhanced signal leads to a reliable identification of the characteristic points of the PPG signal (e.g., systolic, diastolic and dicrotic notch points) by simple means, obtaining results comparable with those from purposely defined algorithms. For systolic points, mean and std of errors computed as the difference between the locations obtained using a purposely defined already known algorithm and those using the MODWT enhancement are, respectively, 0.0097 s and 0.0202 s; for diastolic points they are, respectively, 0.0441 s and 0.0486 s; for dicrotic notch points they are 0.0458 s and 0.0896 s. Hence, this study leads to the selection of several new features from the MODWT enhanced signal on every single pulse extracted from PPG signals, in addition to features already known in the literature. These features can be employed to train machine learning (ML) models useful for estimating systolic blood pressure (SBP) and diastolic blood pressure (DBP) in a non-invasive way, which is suitable for telemedicine health-care monitoring.

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“…Then, they utilized k-NN for recognizing the activities, and they claimed best accuracy. However, Haar wavelet transform has a technical limitation, which is not continuous and hence is not distinguishable [ 11 ]. Moreover, if the corresponding data is large, then the observation step of k-NN can be slow, which is one major limitation of k-NN.…”

Section: Introductionmentioning

confidence: 99%

Edge Detection-Based Feature Extraction for the Systems of Activity Recognition

Ṣiddiqi

Alrashdi

2022

Computational Intelligence and Neuroscience

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Human activity recognition (HAR) is a fascinating and significant challenging task. Generally, the accuracy of HAR systems relies on the best features from the input frames. Mostly, the activity frames have the hostile noisy conditions that cannot be handled by most of the existing edge operators. In this paper, we have designed an adoptive feature extraction method based on edge detection for HAR systems. The proposed method calculates the direction of the edges under the presence of nonmaximum conquest. The benefits are in ease that depends upon the modest procedures, and the extension possibility is to determine other types of features. Normally, it is practical to extract extra low-level information in the form of features when determining the shapes and to get the appropriate information, the additional cultured shape detection procedure is utilized or discarded. Basically, this method enlarges the percentage of the product of the signal-to-noise ratio (SNR) and the highest isolation along with localization. During the processing of the frames, again some edges are demonstrated as a footstep function; the proposed approach might give better performance than other operators. The appropriate information is extracted to form feature vector, which further be fed to the classifier for activity recognition. We assess the performance of the proposed edge-based feature extraction method under the depth dataset having thirteen various kinds of actions in a comprehensive experimental setup.

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EEG Signal Denoising Using Haar Transform and Maximal Overlap Discrete Wavelet Transform (MODWT) for the Finding of Epilepsy

Cited by 5 publications

References 4 publications

Cardiorespiratory Response to Workload Volume and Ergonomic Risk: Automotive Assembly Line Operators’ Adaptations

Cardiorespiratory Response to Workload Volume and Ergonomic Risk: Automotive Assembly Line Operators’ Adaptations

Photoplethysmography Signal Wavelet Enhancement and Novel Features Selection for Non-Invasive Cuff-Less Blood Pressure Monitoring

Edge Detection-Based Feature Extraction for the Systems of Activity Recognition

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