Classification of Mental Stress Using CNN-LSTM Algorithms with Electrocardiogram Signals

A deep learning-based approach for distinguishing different stress levels of human brain using EEG and pulse rate

In today's world, people suffer from many fatal maladies, and stress is one of them. Excessive stress can have deleterious effects on the health, brain, mind, and nervous system of humans. The goal of this paper is to design a deep learningbased human stress level measurement technique using electroencephalogram (EEG), and pulse rate. In this research, EEG signals and pulse rate of healthy subjects are recorded while they solve four different question sets of increasing complexity. It is assumed that the subjects undergo through four different stress levels, i.e., 'no stress', 'low stress', 'medium stress', and 'high stress', while solving these question sets. An attention mechanism-based CNN-TLSTM (convolutional neural network-tanh long short-term memory) model is proposed to detect the mental stress level of a person. An attention layer is incorporated into the designed TLSTM network to increase the classification accuracy of the CNN-TLSTM model. The CNN network is used for the automated extraction of intricate features from the EEG signals and pulse rate. Then TLSTM is used to classify the stress level of a person into four different categories using the CNNextracted features. The obtained average accuracy of the proposed CNN-TLSTM model is 97.86%. Experimentally, it is found that the designed stress level measurement technique is highly effective and outperforms most existing state-of-the-art techniques. In the future, functional Near-Infrared Spectroscopy (fNIRS), ECG, and Galvanic Skin Response (GSR) can be employed with EEG and pulse rate to increase the effectiveness of the designed stress level measurement technique.

Exploring EEG characteristics of multi-level mental stress based on human-machine system

Objective.The understanding of cognitive states is important for the development of human-machine systems (HMSs), and one of the fundamental but challenging issues is the understanding and assessment of the operator's mental stress state in real task scenarios.Approach.In this paper, a virtual unmanned vehicle (UAV) driving task with multi-challenge-level was created to explore the operator's mental stress, and the human brain activity during the task was tracked in real time via electroencephalography (EEG). A mental stress analysis dataset for the virtual UAV task was then developed and used to explore the neural activation patterns associated with mental stress activity. Finally, a multiple attention-based convolutional neural network (MACN) was constructed for automatic stress assessment using the extracted stress-sensitive neural activation features.Main Results.The statistical results of EEG power spectral density (PSD) showed that frontal theta-PSD decreased with increasing task difficulty, and central beta-PSD increased with increasing task difficulty, indicating that neural patterns showed different trends under different levels of mental stress. The performance of the proposed MACN was evaluated based on the dimensional model, and results showed that average three-class classification accuracies of 89.49%/89.88% were respectively achieved for arousal/valence.Significance.The results of this paper suggest that objective assessment of mental stress in a HMS based on a virtual UAV scenario is feasible, and the proposed method provides a promising solution for cognitive computing and applications in human-machine tasks.

Global Stress Detection Framework Combining a Reduced Set of HRV Features and Random Forest Model

Approximately 65% of the worldwide adult population has experienced stress, affecting their daily routine at least once in the past year. Stress becomes harmful when it occurs for too long or is continuous (i.e., chronic), interfering with our performance, attention, and concentration. Chronic high stress contributes to major health issues such as heart disease, high blood pressure, diabetes, depression, and anxiety. Several researchers have focused on detecting stress through combining many features with machine/deep learning models. Despite these efforts, our community has not agreed on the number of features to identify stress conditions using wearable devices. In addition, most of the reported studies have been focused on person-specific training and testing. Thanks to our community's broad acceptance of wearable wristband devices, this work investigates a global stress detection model combining eight HRV features with a random forest (RF) algorithm. Whereas the model's performance is evaluated for each individual, the training of the RF model contains instances of all subjects (i.e., global training). We have validated the proposed global stress model using two open-access databases (the WESAD and SWELL databases) and their combination. The eight HRV features with the highest classifying power are selected using the minimum redundancy maximum relevance (mRMR) method, reducing the training time of the global stress platform. The proposed global stress monitoring model identifies person-specific stress events with an accuracy higher than 99% after a global training framework. Future work should be focused on testing this global stress monitoring framework in real-world applications.

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

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.

Genetic Algorithm-Based Human Mental Stress Detection and Alerting in Internet of Things

Healthcare is one of the emerging application fields in the Internet of Things (IoT). Stress is a heightened psycho-physiological condition of the human that occurs in response to major objects or events. Stress factors are environmental elements that lead to stress. A person's emotional well-being can be negatively impacted by long-term exposure to several stresses affecting at the same time, which can cause chronic health issues. To avoid strain problems, it is vital to recognize them in their early stages, which can only be done through regular stress monitoring. Wearable gadgets offer constant and real information collecting, which aids in experiencing an increase. An investigation of stress discovery using detecting devices and deep learning-based is implemented in this work. This proposed work investigates stress detection techniques that are utilized with detecting hardware, for example, electroencephalography (EEG), photoplethysmography (PPG), and the Galvanic skin reaction (GSR) as well as in various conditions including traveling and learning. A genetic algorithm is utilized to separate the features, and the ECNN-LSTM is utilized to classify the given information by utilizing the DEAP dataset. Before that, preprocessing strategies are proposed for eliminating artifacts in the signal. Then, the stress that is beyond the threshold value is reached the emergency/alert state; in that case, an expert who predicts the mental stress sends the report to the patient/doctor through the Internet. Finally, the performance is evaluated and compared with the traditional approaches in terms of accuracy, f1-score, precision, and recall.

A Modified Deep Learning Framework for Arrhythmia Disease Analysis in Medical Imaging Using Electrocardiogram Signal

Arrhythmias are anomalies in the heartbeat rhythm that occur occasionally in people's lives. These arrhythmias can lead to potentially deadly consequences, putting your life in jeopardy. As a result, arrhythmia identification and classification are an important aspect of cardiac diagnostics. An electrocardiogram (ECG), a recording collecting the heart's pumping activity, is regarded the guideline for catching these abnormal episodes. Nevertheless, because the ECG contains so much data, extracting the crucial data from imagery evaluation becomes extremely difficult. As a result, it is vital to create an effective system for analyzing ECG's massive amount of data. The ECG image from ECG signal is processed by some image processing techniques. To optimize the identification and categorization process, this research presents a hybrid deep learning-based technique. This paper contributes in two ways. Automating noise reduction and extraction of features, 1D ECG data are first converted into 2D pictures. Then, based on experimental evidence, a hybrid model called CNNLSTM is presented, which combines CNN and LSTM models. We conducted a comprehensive research using the broadly used MIT_BIH arrhythmia dataset to assess the efficacy of the proposed CNN-LSTM technique. The results reveal that the proposed method has a 99.10 percent accuracy rate. Furthermore, the proposed model has an average sensitivity of 98.35 percent and a specificity of 98.38 percent. These outcomes are superior to those produced using other procedures, and they will significantly reduce the amount of involvement necessary by physicians.

A Hybrid Deep Learning Approach for ECG-Based Arrhythmia Classification

Arrhythmias are defined as irregularities in the heartbeat rhythm, which may infrequently occur in a human’s life. These arrhythmias may cause potentially fatal complications, which may lead to an immediate risk of life. Thus, the detection and classification of arrhythmias is a pertinent issue for cardiac diagnosis. (1) Background: To capture these sporadic events, an electrocardiogram (ECG), a register containing the heart’s electrical function, is considered the gold standard. However, since ECG carries a vast amount of information, it becomes very complex and challenging to extract the relevant information from visual analysis. As a result, designing an efficient (automated) system to analyse the enormous quantity of data possessed by ECG is critical. (2) Method: This paper proposes a hybrid deep learning-based approach to automate the detection and classification process. This paper makes two-fold contributions. First, 1D ECG signals are translated into 2D Scalogram images to automate the noise filtering and feature extraction. Then, based on experimental evidence, by combining two learning models, namely 2D convolutional neural network (CNN) and the Long Short-Term Memory (LSTM) network, a hybrid model called 2D-CNN-LSTM is proposed. (3) Result: To evaluate the efficacy of the proposed 2D-CNN-LSTM approach, we conducted a rigorous experimental study using the widely adopted MIT–BIH arrhythmia database. The obtained results show that the proposed approach provides ≈98.7%, 99%, and 99% accuracy for Cardiac Arrhythmias (ARR), Congestive Heart Failure (CHF), and Normal Sinus Rhythm (NSR), respectively. Moreover, it provides an average sensitivity of the proposed model of 98.33% and a specificity value of 98.35%, for all three arrhythmias. (4) Conclusions: For the classification of arrhythmias, a robust approach has been introduced where 2D scalogram images of ECG signals are trained over the CNN-LSTM model. The results obtained are better as compared to the other existing techniques and will greatly reduce the amount of intervention required by doctors. For future work, the proposed method can be applied over some live ECG signals and Bi-LSTM can be applied instead of LSTM.

Real-Time Stress Level Feedback from Raw Ecg Signals for Personalised, Context-Aware Applications Using Lightweight Convolutional Neural Network Architectures

Human stress is intricately linked with mental processes such as decision making. Public protection practitioners, including Law Enforcement Agents (LEAs), are forced to make difficult decisions during high-pressure operations, under strenuous circumstances. In this respect, systems and applications that assist such practitioners to take decisions, are increasingly incorporating user stress level information for their development, adaptation, and evaluation. To that end, our goal is to accurately detect and classify the level of acute, short-term stress, in real time, for the development of personalized, context-aware solutions for LEAs. Deep Neural Networks (DNNs), and in particular Convolutional Neural Networks (CNNs), have been gaining traction in the field of stress analysis, exhibiting promising results. Furthermore, the electrocardiogram (ECG) signals, have also been widely adopted for estimating levels of stress. In this work, we propose two CNN architectures for the stress detection and 3-level (low, moderate, high) stress classification tasks, using ultra short-term raw ECG signals (3 s). One architecture is simple and with a low memory footprint, suitable for running in wearable edge-computing nodes, and the other is able to learn more complex features, having more trainable parameters. The models were trained on the two publicly available stress classification datasets, after applying pre-processing techniques, such as data pruning, down-sampling, and data augmentation, using a sliding window approach. After hyperparameter tuning, using 4-fold cross-validation, the evaluation on the test set demonstrated state-of-the-art accuracy both on the 3- and 2-level stress classification task using the DriveDB dataset, reporting an accuracy of 83.55% and 98.77% respectively.