Detection of Focal and Non-Focal Electroencephalogram Signals Using Fast Walsh-Hadamard Transform and Artificial Neural Network

Prasanna, J.; Subathra, M. S. P.; Mohammed, Mazin Abed; Maashi, Mashael; Garcia‐Zapirain, Begonya; Sairamya, N. J.; George, S. Thomas

doi:10.3390/s20174952

Cited by 47 publications

(3 citation statements)

References 66 publications

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“…In another study, Waris et al [8] utilized a bandpass digital Butterworth filter to eliminate motion artifacts from intramuscular EMG signals. Similarly, Subathra et al [9] used a band filter to denoise electroglottography (EGG) signals for voice pathology detection. In [10], the authors' utilized a bandpass filter to eliminate motion artifacts from electroencephalogram (EEG) signals.…”

Section: Introductionmentioning

confidence: 99%

Threshold Parameters Selection for Empirical Mode Decomposition-Based EMG Signal Denoising

Ashraf¹,

Waris²,

Gilani³

et al. 2021

Intelligent Automation &Amp; Soft Computing

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Empirical Mode Decomposition (EMD) is a data-driven and fully adaptive signal decomposition technique to decompose a signal into its Intrinsic Mode Functions (IMF). EMD has attained great attention due to its capabilities to process a signal in the frequency-time domain without altering the signal into the frequency domain. EMD-based signal denoising techniques have shown great potential to denoise nonlinear and nonstationary signals without compromising the signal's characteristics. The denoising procedure comprises three steps, i.e., signal decomposition, IMF thresholding, and signal reconstruction. Thresholding is performed to assess which IMFs contain noise. In this study, Interval Thresholding (IT), Iterative Interval Thresholding (IIT), and Clear Iterative Interval Thresholding (CIIT) techniques have been explored for denoising of electromyography (EMG) signals. The effect of different thresholding operators, i.e., SOFT, HARD, and Smoothly Clipped Absolute Deviation (SCAD), on the performance of EMD-based EMG denoising techniques is also investigated. 15 EMG signals, recorded from the upper limb of 5 healthy subjects, were used to identify the best possible combination of thresholding technique and thresholding operator for denoising EMG signals. The performance of denoising techniques is assessed by calculating the Signal to Noise (SNR) ratio of the signals. The results are further evaluated using a two-way Analysis of Variance (ANOVA) statistical test. The results demonstrated that the mean SNR values yielded by the IIT thresholding technique outperform the IT thresholding technique (P-value < 0.05), but there is no significant difference in mean SNR values of IIT and CIIT techniques (P-value = 0.9951). For thresholding operators, there is no significant difference in mean SNR values of the HARD and SOFT operator (P-value = 0.0968), whereas the HARD operator outperforms SCAD (P-value < 0.05). It has also shown that the combination of IIT thresholding with SOFT operator and threshold value equal to half of the universal threshold denoise the EMG signals while preserving the original signal's characteristics. IIT-based EMD denoising technique with HARD thresholding operator yields the highest SNR, irrespective of the level of noise embedded in the signal. Whereas IIT with SOFT operator provides comparable SNR and successfully preserves the shape of EMG signals. The

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Section: Introductionmentioning

confidence: 99%

Threshold Parameters Selection for Empirical Mode Decomposition-Based EMG Signal Denoising

Ashraf¹,

Waris²,

Gilani³

et al. 2021

Intelligent Automation &Amp; Soft Computing

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“…In recent years, many computerized techniques are presented for medical disease detection and classi cation. They focused on well-known medical imaging modalities such as mammography for breast cancer [9], pathology [10], Electroencephalogram Signals [11], carcinoma [12] such as deep learning (DL) shows much improvement in medical image processing [13,14]. DL is a powerful machine learning tool for automated medical infections classi cation into their relevant category [15,16].…”

Section: Introductionmentioning

confidence: 99%

Multiclass Stomach Diseases Classification Using Deep Learning Features Optimization

Khan¹,

Majid²,

Hussain³

et al. 2021

Computers, Materials &Amp; Continua

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In the area of medical image processing, stomach cancer is one of the most important cancers which need to be diagnose at the early stage. In this paper, an optimized deep learning method is presented for multiple stomach disease classi cation. The proposed method work in few important steps-preprocessing using the fusion of ltering images along with Ant Colony Optimization (ACO), deep transfer learning-based features extraction, optimization of deep extracted features using nature-inspired algorithms, and nally fusion of optimal vectors and classi cation using Multi-Layered Perceptron Neural Network (MLNN). In the feature extraction step, pretrained Inception V3 is utilized and retrained on selected stomach infection classes using the deep transfer learning step. Later on, the activation function is applied to Global Average Pool (GAP) for feature extraction. However, the extracted features are optimized through two different nature-inspired algorithms-Particle Swarm Optimization (PSO) with dynamic tness function and Crow Search Algorithm (CSA). Hence, both methods' output is fused by a maximal value approach and classi ed the fused feature vector by MLNN. Two datasets are used to evaluate the proposed method-CUI WahStomach Diseases and Combined dataset and achieved an average accuracy of 99.5%. The comparison with existing techniques, it is shown that the proposed method shows signi cant performance.

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“…This will present a novel age of smart, proactive healthcare particularly with the huge problem of constrained medical resources. Consequently, ECG monitoring systems were established and extensively utilized in the healthcare field for previous years and have considerably developed on time because of the development of smart enabling techniques (Subathra et al, 2020). Currently, the ECG monitoring system is utilized in homes, hospitals, remote contexts, and outpatient ambulatory settings.…”

Section: Introductionmentioning

confidence: 99%

Internet of things and deep learning enabled healthcare disease diagnosis using biomedical electrocardiogram signals

et al. 2021

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With recent advancements in the internet of things (IoT), wearables, and sensing technologies, the quality of healthcare services gets improved and it caused a shift from conventional clinical‐based healthcare to real‐time monitoring. The sensors are commonly integrated into several medical gadgets to save the bio‐signals produced by the physiological activities of the human body. At the same time, a biomedical electrocardiogram (ECG) signal is employed as a familiar way to examine and diagnose cardiovascular diseases (CVDs), which is rapid and non‐invasive. Since the increasing number of patients degrades the classification performance due to high differences in the ECG signal patterns among several patients, computer‐assisted automated diagnostic tools are essential for ECG signal classification. With this motivation, this paper introduces a new IoT and deep learning (DL) enabled healthcare disease diagnosis (IoTDL‐HDD) model using biomedical ECG signals. The proposed IoTDL‐HDD model aims to detect the presence of CVDs by the use of DL models in biomedical ECG signals. In addition, the proposed IoTDL‐HDD model utilizes a BiLSTM feature extraction technique to extract useful feature vectors from the ECG signals. For improving the efficiency of the BiLSTM technique, the artificial flora optimization (AFO) algorithm is employed as a hyperparameter optimizer. Besides, a fuzzy deep neural network (FDNN) classifier is employed for assigning proper class labels to the ECG signals. The performance of the IoTDL‐HDD model is examined on biomedical ECG signals and the outcomes are inspected in distinct features. The resultant experimental outcomes pointed out the supremacy of the IoTDL‐HDD model with the maximum accuracy of 93.452%.

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Detection of Focal and Non-Focal Electroencephalogram Signals Using Fast Walsh-Hadamard Transform and Artificial Neural Network

Cited by 47 publications

References 66 publications

Threshold Parameters Selection for Empirical Mode Decomposition-Based EMG Signal Denoising

Threshold Parameters Selection for Empirical Mode Decomposition-Based EMG Signal Denoising

Multiclass Stomach Diseases Classification Using Deep Learning Features Optimization

Internet of things and deep learning enabled healthcare disease diagnosis using biomedical electrocardiogram signals

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