A novel approach to create synthetic biomedical signals using BiRNN

Hernandez-Matamoros, Andres; Fujita, Hamido; Pérez, Héctor

doi:10.1016/j.ins.2020.06.019

Cited by 58 publications

(18 citation statements)

References 35 publications

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“…The synthetic quality score was measured in the scale (1–20)%-Very Poor, (20–40)%-Poor, (40–60)%-Moderate, (60–80)%-Good and (80–100)%-Excellent. This suggests that the quality of synthesized data is excellent, and in line with similar studies (Hernandez-Matamoros et al, 2020 ). Consequently, the synthesized data was of an additional 180 synthesized subjects with 150 data points (2.5 min of EEG activity split into 150 segments of 1 s) for each mental workload index.…”

Section: Resultssupporting

confidence: 88%

An Evaluation of the EEG Alpha-to-Theta and Theta-to-Alpha Band Ratios as Indexes of Mental Workload

Raufi

Longo

2022

Front. Neuroinform.

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Many research works indicate that EEG bands, specifically the alpha and theta bands, have been potentially helpful cognitive load indicators. However, minimal research exists to validate this claim. This study aims to assess and analyze the impact of the alpha-to-theta and the theta-to-alpha band ratios on supporting the creation of models capable of discriminating self-reported perceptions of mental workload. A dataset of raw EEG data was utilized in which 48 subjects performed a resting activity and an induced task demanding exercise in the form of a multitasking SIMKAP test. Band ratios were devised from frontal and parietal electrode clusters. Building and model testing was done with high-level independent features from the frequency and temporal domains extracted from the computed ratios over time. Target features for model training were extracted from the subjective ratings collected after resting and task demand activities. Models were built by employing Logistic Regression, Support Vector Machines and Decision Trees and were evaluated with performance measures including accuracy, recall, precision and f1-score. The results indicate high classification accuracy of those models trained with the high-level features extracted from the alpha-to-theta ratios and theta-to-alpha ratios. Preliminary results also show that models trained with logistic regression and support vector machines can accurately classify self-reported perceptions of mental workload. This research contributes to the body of knowledge by demonstrating the richness of the information in the temporal, spectral and statistical domains extracted from the alpha-to-theta and theta-to-alpha EEG band ratios for the discrimination of self-reported perceptions of mental workload.

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Section: Resultssupporting

confidence: 88%

An Evaluation of the EEG Alpha-to-Theta and Theta-to-Alpha Band Ratios as Indexes of Mental Workload

Raufi

Longo

2022

Front. Neuroinform.

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show abstract

“…The synthetic quality score was measured in the scale (1-20)%-Very Poor, (20-40)%-Poor, (40-60)%-Moderate, (60-80)%-Good and (80-100)%-Excellent. This suggests that the quality of synthesised data is excellent, and in line with similar studies (Hernandez-Matamoros et al, 2020). Consequently, the synthesised data was of an additional 180 synthesised subjects with 150 data points (2.5 minutes of EEG activity split into 150 segments of 1 second) for each mental workload index.…”

Section: Synthetic Data Evaluationsupporting

confidence: 73%

An Evaluation of the EEG alpha-to-theta and theta-to-alpha band Ratios as Indexes of Mental Workload

Raufi¹,

Longo²

2022

Preprint

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Many research works indicate that EEG bands, specifically the alpha and theta bands, have been potentially helpful cognitive load indicators. However, minimal research exists to validate this claim. This study aims to assess and analyze the impact of the alpha-to-theta and the theta-to-alpha band ratios on supporting the creation of models capable of discriminating selfreported perceptions of mental workload. A dataset of raw EEG data was utilized in which 48 subjects performed a resting activity and an induced task demanding exercise in the form of a multitasking SIMKAP test. Band ratios were devised from frontal and parietal electrode clusters. Building and model testing was done with high-level independent features from the frequency and temporal domains extracted from the computed ratios over time. Target features for model training were extracted from the subjective ratings collected after resting and task demand activities. Models were built by employing Logistic Regression, Support Vector Machines and Decision Trees and were evaluated with performance measures including accuracy, recall, precision and f1-score. The results indicate high classification accuracy of those models trained with the high-level features extracted from the alpha-to-theta ratios and theta-to-alpha ratios. Preliminary results also show that models trained with logistic regression and support vector machines can accurately classify self-reported perceptions of mental workload. This research contributes to the body of knowledge by demonstrating the richness of the information in the temporal, spectral and statistical domains extracted from the alpha-to-theta and theta-to-alpha EEG band ratios for the discrimination of self-reported perceptions of mental workload.

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“…Golany et al [ 29 ] improved classification performance by adding synthetic ECG heartbeats produced by standard GANs to the training set. Hernandez-Matamoros et al [ 30 ] employed a Bi-RNN model to synthesize numerous beat signals that were identical to the original data; however, the ECG signal was not subjected to stringent ECG signal denoising, QRS wave identification, or heartbeat segmentation in the data preparation step. Zhu et al [ 31 ] proposed a BiLSTM-CNN GAN for generating ECG signal models.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning-Based Data Augmentation and Model Fusion for Automatic Arrhythmia Identification and Classification Algorithms

Cui

Xiao

et al. 2022

Computational Intelligence and Neuroscience

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Automated ECG-based arrhythmia detection is critical for early cardiac disease prevention and diagnosis. Recently, deep learning algorithms have been widely applied for arrhythmia detection with great success. However, the lack of labeled ECG data and low classification accuracy can have a significant impact on the overall effectiveness of a classification algorithm. In order to better apply deep learning methods to arrhythmia classification, in this study, feature extraction and classification strategy based on generative adversarial network data augmentation and model fusion are proposed to address these problems. First, the arrhythmia sparse data is augmented by generative adversarial networks. Then, aiming at the identification of different types of arrhythmias in long-term ECG, a spatial information fusion model based on ResNet and a temporal information fusion model based on BiLSTM are proposed. The model effectively fuses the location information of the nearest neighbors through the local feature extraction part of the generated ECG feature map and obtains the correlation of the global features by autonomous learning in multiple spaces through the BiLSTM network in the part of the global feature extraction. In addition, an attention mechanism is introduced to enhance the features of arrhythmia-type signal segments, and this mechanism can effectively focus on the extraction of key information to form a feature vector for final classification. Finally, it is validated by the enhanced MIT-BIH arrhythmia database. The experimental results demonstrate that the proposed classification technique enhances arrhythmia diagnostic accuracy by 99.4%, and the algorithm has high recognition performance and clinical value.

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A novel approach to create synthetic biomedical signals using BiRNN

Cited by 58 publications

References 35 publications

An Evaluation of the EEG Alpha-to-Theta and Theta-to-Alpha Band Ratios as Indexes of Mental Workload

An Evaluation of the EEG Alpha-to-Theta and Theta-to-Alpha Band Ratios as Indexes of Mental Workload

An Evaluation of the EEG alpha-to-theta and theta-to-alpha band Ratios as Indexes of Mental Workload

Deep Learning-Based Data Augmentation and Model Fusion for Automatic Arrhythmia Identification and Classification Algorithms

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