Classification of EMG signals using PSO optimized SVM for diagnosis of neuromuscular disorders

Microneedle electrodes: materials, fabrication methods, and electrophysiological signal monitoring-narrative review

Flexible, microneedle-based electrodes offer an innovative solution for high-quality physiological signal monitoring, reducing the need for complex algorithms and hardware, thus streamlining health assessments, and enabling earlier disease detection. These electrodes are particularly promising for improving patient outcomes by providing more accurate, reliable, and long-term electrophysiological data, but their clinical adoption is hindered by the limited availability of large-scale population testing. This review examines the key advantages of flexible microneedle electrodes, including their ability to conform to the skin, enhance skin-electrode contact, reduce discomfort, and deliver superior signal fidelity. The mechanical and electrical properties of these electrodes are thoroughly explored, focusing on critical aspects like fracture force, skin penetration efficiency, and impedance measurements. Their applications in capturing electrophysiological signals such as ECG, EMG, and EEG are also highlighted, demonstrating their potential in clinical scenarios. Finally, the review outlines future research directions, emphasizing the importance of further studies to enhance the clinical and consumer use of flexible microneedle electrodes in medical diagnostics.

Construction and application of optimized model for mine water inflow prediction based on neural network and ARIMA model

Mine water influx is a significant geological hazard during mine development, influenced by various factors such as geological conditions, hydrology, climate, and mining techniques. This phenomenon is characterized by non-linearity and high complexity, leading to frequent water accidents in coal mines. These accidents not only impact coal production quality but also jeopardize the safety of mine staff. In order to better predict the amount of water surging in mines and to provide an important basis for mine water damage prevention work, based on the time series data of mine water influx from January 2020 to February 2023 in Northern Guizhou Province Longfeng Coal Mine, the BP-ARIMA prediction model was established by combining the BP neural network model and ARIMA autoregressive sliding average model, It also predicted the mine influx for a total of 6 months from July 2022 to February 2023, and compared the prediction results with four models, namely, BP neural network model, ARIMA autoregressive sliding average model, traditional method of Large well method, and GM(1,1) grey model, and used the absolute relative error as the calculation of model accuracy. The results show that the established BP-ARIMA(3,1,1) prediction model is much closer to the actual value, with an average absolute relative error of 1.02% and a maximum absolute relative error of 3.036%, and the goodness of fit R² was 0.93, which is much better than the other four single models, and substantially improves the prediction accuracy of mine water influx. Furthermore, utilizing the BP-ARIMA model, future predictions for mine water influx in Longfeng Mine were made, offering a scientific foundation for effective prevention and control measures.

Near-infrared spectroscopy for the quality control of Sarassum fusiforme: Prediction of antioxidant capability of Sarassum fusiforme at different growth stages

The healthy benefits of seaweed have increased its market demand in recent times. Quality control is crucial for seaweed to ensure the customers' interest and the sustainable development of seaweed farming industry. This study developed a quality control method for seaweed Sargassum fusiforme, rapid and simple, using near-infrared spectroscopy (NIR) and chemometrics for the prediction of antioxidant capacity of S. fusiforme from different growth stages, S. fusiforme was distinguished according to growth stage by partial least squares-discriminant analysis (PLS-DA) and particle swarm optimization-support vector machine (PSO-SVM). The antioxidant properties including 2,2'-azinobis-3-ethylbenzothiazoline-6-sulfonic acid (ABTS) scavenging capacity, 2,2-diphenyl-1-picrylhydrazyl (DPPH) scavenging capacity, and ferric reducing antioxidant power (FRAP) were quantified using competitive adaptive reweighted sampling (CARS)-PLS model. Based on the spectra data preprocessed by multiplicative scatter and standard normal variate methods, the PSO-SVM models can accurately identify the growth stage of all S. fusiforme samples. The CARS-PLS models exhibited good performance in predicting the antioxidant capacity of S. fusiforme, with coefficient of determination (RP2) and root mean square error (RMSEP) values in the independent prediction sets reaching 0.9778 and 0.4018 % for ABTS, 0.9414 and 2.0795 % for DPPH, and 0.9763 and 2.4386 μmol L-1 for FRAP, respectively. The quality and market price of S. fusiforme should increase in the order of maturation < growth < seedling regarding the antioxidant property. The overall results indicated that the NIR spectroscopy accompanied by chemometrics can assist for the quality control of S. fusiforme in a more rapid and simple manner. This study also provided a customer-oriented concept of seaweed quality grading based on deep insight into the antioxidant capability of S. fusiforme at different growth stages, which is highly valuable for precise quality control and standardization of seaweed market.

AML-DECODER: Advanced Machine Learning for HD-sEMG Signal Classification-Decoding Lateral Epicondylitis in Forearm Muscles

BACKGROUND

Innovative algorithms for wearable devices and garments are critical for diagnosing and monitoring disease (such as lateral epicondylitis (LE)) progression. LE affects individuals across various professions and causes daily problems.

METHODS

We analyzed signals from the forearm muscles of 14 healthy controls and 14 LE patients using high-density surface electromyography. We discerned significant differences between groups by employing phase-amplitude coupling (PAC) features. Our study leveraged PAC, Daubechies wavelet with four vanishing moments (db4), and state-of-the-art techniques to train a neural network for the subject's label prediction.

RESULTS

Remarkably, PAC features achieved 100% specificity and sensitivity in predicting unseen subjects, while state-of-the-art features lagged with only 35.71% sensitivity and 28.57% specificity, and db4 with 78.57% sensitivity and 85.71 specificity. PAC significantly outperformed the state-of-the-art features (adj. p-value < 0.001) with a large effect size. However, no significant difference was found between PAC and db4 (adj. p-value = 0.147). Also, the Jeffries-Matusita (JM) distance of the PAC was significantly higher than other features (adj. p-value < 0.001), with a large effect size, suggesting PAC features as robust predictors of neuromuscular diseases, offering a profound understanding of disease pathology and new avenues for interpretation. We evaluated the generalization ability of the PAC model using 99.9% confidence intervals and Bayesian credible intervals to quantify prediction uncertainty across subjects. Both methods demonstrated high reliability, with an expected accuracy of 89% in larger, more diverse populations.

CONCLUSIONS

This study's implications might extend beyond LE, paving the way for enhanced diagnostic tools and deeper insights into the complexities of neuromuscular disorders.

Ensemble and optimization algorithm in support vector machines for classification of wheat genotypes

This study aimed to classifying wheat genotypes using support vector machines (SVMs) improved with ensemble algorithms and optimization techniques. Utilizing data from 302 wheat genotypes and 14 morphological attributes to evaluate six SVM kernels: linear, radial basis function (RBF), sigmoid, and polynomial degrees 1-3. Various optimization methods, including grid search, random search, genetic algorithms, differential evolution, and particle swarm optimization, were used. The radial basis function kernel achieves the highest accuracy at 93.2%, and the weighted accuracy ensemble further improves it to 94.9%. This study shows the effectiveness of these methods in agricultural research and crop improvement. Notably, optimization-based SVM classification, particularly with particle swarm optimization, saw a significant 1.7% accuracy gain in the test set, reaching 94.9% accuracy. These findings underscore the efficacy of RBF kernels and optimization techniques in improving wheat genotype classification accuracy and highlight the potential of SVMs in agricultural research and crop improvement endeavors.

Multi-strategy learning-based particle swarm optimization algorithm for COVID-19 threshold segmentation

With advancements in science and technology, the depth of human research on COVID-19 is increasing, making the investigation of medical images a focal point. Image segmentation, a crucial step preceding image processing, holds significance in the realm of medical image analysis. Traditional threshold image segmentation proves to be less efficient, posing challenges in selecting an appropriate threshold value. In response to these issues, this paper introduces Inner-based multi-strategy particle swarm optimization (IPSOsono) for conducting numerical experiments and enhancing threshold image segmentation in COVID-19 medical images. A novel dynamic oscillatory weight, derived from the PSO variant for single-objective numerical optimization (PSOsono) is incorporated. Simultaneously, the historical optimal positions of individuals in the particle swarm undergo random updates, diminishing the likelihood of algorithm stagnation and local optima. Moreover, an inner selection learning mechanism is proposed in the update of optimal positions, dynamically refining the global optimal solution. In the CEC 2013 benchmark test, PSOsono demonstrates a certain advantage in optimization capability compared to algorithms proposed in recent years, proving the effectiveness and feasibility of PSOsono. In the Minimum Cross Entropy threshold segmentation experiments for COVID-19, PSOsono exhibits a more prominent segmentation capability compared to other algorithms, showing good generalization across 6 CT images and further validating the practicality of the algorithm.

Long-term water demand forecasting using artificial intelligence models in the Tuojiang River basin, China

Accurate forecasts of water demand are a crucial factor in the strategic planning and judicious use of finite water resources within a region, underpinning sustainable socio-economic development. This study aims to compare the applicability of various artificial intelligence models for long-term water demand forecasting across different water use sectors. We utilized the Tuojiang River basin in Sichuan Province as our case study, comparing the performance of five artificial intelligence models: Genetic Algorithm optimized Back Propagation Neural Network (GA-BP), Extreme Learning Machine (ELM), Gaussian Process Regression (GPR), Support Vector Regression (SVR), and Random Forest (RF). These models were employed to predict water demand in the agricultural, industrial, domestic, and ecological sectors using actual water demand data and relevant influential factors from 2005 to 2020. Model performance was evaluated based on the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE), with the most effective model used for 2025 water demand projections for each sector within the study area. Our findings reveal that the GPR model demonstrated superior results in predicting water demand for the agricultural, domestic, and ecological sectors, attaining R2 values of 0.9811, 0.9338, and 0.9142 for the respective test sets. Also, the GA-BP model performed optimally in predicting industrial water demand, with an R2 of 0.8580. The identified optimal prediction model provides a useful tool for future long-term water demand forecasting, promoting sustainable water resource management.

Artificial intelligence for automatic classification of needle EMG signals: A scoping review

OBJECTIVE

This scoping review provides an overview of artificial intelligence (AI), including machine and deep learning techniques, in the interpretation of clinical needle electromyography (nEMG) signals.

METHODS

A comprehensive search of Medline, Embase and Web of Science was conducted to find peer-reviewed journal articles. All papers published after 2010 were included. The methodological quality of the included studies was assessed with CLAIM (checklist for artificial intelligence in medical imaging).

RESULTS

51 studies were identified that fulfilled the inclusion criteria. 61% used open-source EMGlab data set to develop models to classify nEMG signal in healthy, amyotrophic lateral sclerosis (ALS) and myopathy (25 subjects). Only two articles developed models to classify signals recorded at rest. Most articles reported high performance accuracies, but many were subject to bias and overtraining.

CONCLUSIONS

Current AI-models of nEMG signals are not sufficient for clinical implementation. Suggestions for future research include emphasizing the need for an optimal training and validation approach using large datasets of clinical nEMG data from a diverse patient population.

SIGNIFICANCE

The outcomes of this study and the suggestions made aim to contribute to developing AI-models that can effectively handle signal quality variability and are suitable for daily clinical practice in interpreting nEMG signals.

Cybersecurity in neural interfaces: Survey and future trends

With the joint advancement in areas such as pervasive neural data sensing, neural computing, neuromodulation and artificial intelligence, neural interface has become a promising technology facilitating both the closed-loop neurorehabilitation for neurologically impaired patients and the intelligent man-machine interactions for general application purposes. However, although neural interface has been widely studied, few previous studies focused on the cybersecurity issues in related applications. In this survey, we systematically investigated possible cybersecurity risks in neural interfaces, together with potential solutions to these problems. Importantly, our survey considers interfacing techniques on both central nervous systems (i.e., brain-computer interfaces) and peripheral nervous systems (i.e., general neural interfaces), covering diverse neural modalities such as electroencephalography, electromyography and more. Moreover, our survey is organized on three different levels: (1) the data level, which mainly focuses on the privacy leakage issue via attacking and analyzing neural database of users; (2) the permission level, which mainly focuses on the prospects and risks to directly use real time neural signals as biometrics for continuous and unobtrusive user identity verification; and (3) the model level, which mainly focuses on adversarial attacks and defenses on both the forward neural decoding models (e.g. via machine learning) and the backward feedback implementation models (e.g. via neuromodulation and stimulation). This is the first study to systematically investigate cybersecurity risks and possible solutions in neural interfaces which covers both central and peripheral nervous systems, and considers multiple different levels to provide a complete picture of this issue.

Prediction of histone deacetylase inhibition by triazole compounds based on artificial intelligence

A quantitative structure-activity relationship (QSAR) study was conducted to predict the anti-colon cancer and HDAC inhibition of triazole-containing compounds. Four descriptors were selected from 579 descriptors which have the most obvious effect on the inhibition of histone deacetylase (HDAC). Four QSAR models were constructed using heuristic algorithm (HM), random forest (RF), radial basis kernel function support vector machine (RBF-SVM) and support vector machine optimized by particle swarm optimization (PSO-SVM). Furthermore, the robustness of four QSAR models were verified by K-fold cross-validation method, which was described by Q 2. In addition, the R 2 of the four models are greater than 0.8, which indicates that the four descriptors selected are reasonable. Among the four models, model based on PSO-SVM method has the best prediction ability and robustness with R 2 of 0.954, root mean squared error (RMSE) of 0.019 and Q 2 of 0.916 for the training set and R 2 of 0.965, RMSE of 0.017 and Q 2 of 0.907 for the test set. In this study, four key descriptors were discovered, which will help to screen effective new anti-colon cancer drugs in the future.

Fuzzy inference system (FIS) - long short-term memory (LSTM) network for electromyography (EMG) signal analysis

A wide range of application domains,s such as remote robotic control, rehabilitation, and remote surgery, require capturing neuromuscular activities. The reliability of the application is highly dependent on an ability to decode intentions accurately based on captured neuromuscular signals. Physiological signals such as Electromyography (EMG) and Electroencephalography (EEG) generated by neuromuscular activities contain intrinsic patterns for users' particular actions. Such actions can generally be classified as motor states, such as Forward, Reverse, Hand-Grip, and Hand-Release. To classify these motor states truthfully, the signals must be captured and decoded correctly. This paper proposes a novel classification technique using a Fuzzy Inference System (FIS) and a Long Short-Term Memory (LSTM) network to classify the motor states based on EMG signals. Existing EMG signal classification techniques generally rely on features derived from data captured at a specific time instance. This typical approach does not consider the temporal correlation of the signal in the entire window. This paper proposes an LSTM with a Fuzzy Logic method to classify four major hand movements: forward, reverse, raise, and lower. Features associated with the pattern generated throughout the motor state movement were extracted by exploring published data within a given time window. The classification results can achieve a 91.3% accuracy for the 4-way action (Forward/Reverse/GripUp/RelDown) and 95.1% (Forward/Reverse Action) and 96.7% (GripUp/RelDown action) for 2-way actions. The proposed mechanism demonstrates high-level, human-interpretable results that can be employed in rehabilitation or medical-device industries.

Efficient Subject-Independent Detection of Anterior Cruciate Ligament Deficiency Based on Marine Predator Algorithm and Support Vector Machine

Anterior cruciate ligament (ACL) deficiency not only reduces knee stability, but also increases the risk of more disease and impairs daily life, thus requiring efficient detection of ACL deficiency. To build an efficient subject-independent ACL deficiency detection model, this study proposes a new method called SVM-MPA that fuses marine predator algorithm (MPA) and support vector machine (SVM) for simultaneous feature selection, hyperparameter optimization and classification. 35ACL-deficient (ACLD) and 35 ACL-intact (ACLI) participants were recruited to collect 6-degree-of-freedom knee kinematic data. Then, 216-dimensional multi-domain features covering time domain, frequency domain, time-frequency domain and nonlinearity were extracted. The error rate of SVM classification based on 5-fold cross-validation was used to construct the fitness of MPA, and MPA served to select features and optimize two hyperparameters for SVM. The majority voting strategy-based post-processing was introduced to convert the gait cycle-level to knee-level ACL deficiency detection. Comparing with 7 well-known meta-heuristic algorithms and running all 20 times, the best average gait cycle-level ACL deficiency detection performance (sensitivity: 96.78±0.4.84%, specificity: 99.43±5.70%, and accuracy: 98.48±1.70%) was obtained using the proposed method. With post-processing, this study improved the best (final) detection performance (sensitivity: 97.78±4.97%, specificity: 100±0.00%, and accuracy: 99.13±1.94%). These results demonstrate the feasibility and effectiveness of the proposed method and shows that an efficient subject-independent ACL deficiency detection model can be constructed using the proposed method, which makes it possible to provide a non-invasive, objective and accurate preoperative auxiliary detection method for diagnosing ACL deficiency clinically.

IoT-enabled product development method to support rapid manufacturing using a nature-inspired algorithm

Investigations illustrate that the Internet of Things (IoT) can save costs, increase efficiency, improve quality, and provide data-driven preventative maintenance services. Intelligent sensors, dependable connectivity, and complete integration are essential for gathering real-time information. IoT develops home appliances for improved customer satisfaction, personalization, and enhanced big data analytics as a crucial Industry 4.0 enabler. Because the product design process is an important part of controlling manufacturing, there are constant attempts to improve and minimize product design time. Utilizing a hybrid algorithm, this research provides a novel method to schedule design products in production management systems to optimize energy usage and design time (combined particle optimization algorithm and shuffled frog leaping algorithm). The issue with particle optimization algorithms is that they might become stuck in local optimization and take a long time to converge to global optimization. The strength of the combined frog leaping algorithm local searching has been exploited to solve these difficulties. The MATLAB programming tool is used to simulate the suggested technique. The simulation findings were examined from three perspectives: energy usage, manufacturing time, and product design time. According to the findings, the recommended strategy performed better in minimizing energy use and product design time. These findings also suggest that the proposed strategy has a higher degree of convergence when discovering optimal solutions.

Prediction of anti-proliferation effect of [1,2,3]triazolo[4,5-d]pyrimidine derivatives by random forest and mix-kernel function SVM with PSO

In order to predict the anti-gastric cancer effect of [1,2,3]triazolo[4,5-d]pyrimidine derivatives (1,2,3-TPD), quantitative structure-activity relationship (QSAR) studies were performed. Based on five descriptors selected from descriptors pool, four QSAR models were established by heuristic method (HM), random forest (RF), support vector machine with radial basis kernel function (RBF-SVM), and mix-kernel function support vector machine (MIX-SVM) including radial basis kernel and polynomial kernel function. Furthermore, the model built by RF explained the importance of the descriptors selected by HM. Compared with RBF-SVM, the MIX-SVM enhanced the generalization and learning ability of the constructed model simultaneously and the multi parameters optimization problem in this method was also solved by particle swarm optimization (PSO) algorithm with very low complexity and fast convergence. Besides, leave-one-out cross validation (LOO-CV) was adopted to test the robustness of the models and Q² was used to describe the results. And the MIX-SVM model showed the best prediction ability and strongest model robustness: R² = 0.927, Q² = 0.916, MSE = 0.027 for the training set and R² = 0.946, Q² = 0.913, MSE = 0.023 for the test set. This study reveals five key descriptors of 1,2,3-TPD and will provide help to screen out efficient and novel drugs in the future.

Mechanism of Hyperbaric Oxygen Combined with Astaxanthin Mediating Keap1/Nrf2/HO-1 Pathway to Improve Exercise Fatigue in Mice

Objective

This work aimed to explore the application and optimization of the electrophysiological monitoring system to real-time monitor the exercise-induced fatigue (EIF) animals and investigate the intervention mechanism of hyperbaric oxygen (HBO) combined with natural astaxanthin (NAX) on EIF.

Methods

First, a system was constructed for acquisition, processing, and feature extraction of electrocardiograph (ECG) signal and surface electromyography (EMG) signal for EIF monitoring. The mice were randomly divided into a control group (CG), EIF group (EG), HBO treatment (HBO) group, and HBO combined with NAX treatment (HBO + NAX) group. The effect of the constructed system on classification recognition of EIF was analyzed. The levels of serum antioxidative stress indicators of mice in each group were detected, including malondialdehyde (MDA), catalase (CAT), superoxide dismutase (SOD), glutathione (GSH), glutathione-peroxidation (GSH-Px), and total antioxidant capacity (T-AOC). In addition, the mRNA and protein levels of Keap1/Nrf2/HO-1 pathway related genes in liver tissue were detected.

Results

The results showed that the normalized least mean squares algorithm effectively removed the motion artifact interference of ECG signal and can clearly display the signal peak, and high-pass filtering and power frequency filtering effectively removed the motion and baseline drift interference of surface EMG signal. The recognition sensitivity, specificity, and accuracy of the EIF recognition model based on the long- and short-term memory network were 90.0%, 93.3%, and 92.5%, respectively. Compared with the CG, the characteristics of ECG signal and surface EMG signal of the mice in the EIF group changed greatly (P < 0.05); the serum MDA level was increased obviously; the CAT, SOD, GSH, GSH-Px, and T-AOC levels were observably reduced (P < 0.05); the expressions of Keap1 and HO-1 in the liver were reduced remarkably, while the expression of Nrf2 was increased notably (P < 0.05). Compared with the EIF group, the characteristics of ECG signal and surface EMG signal of the mice in the HBO and HBO + NAX groups were obviously improved (P < 0.05); the serum MDA level was significantly reduced; the CAT, SOD, GSH, GSH-Px, and T-AOC levels were greatly increased (P < 0.05); the expressions of Keap1 and HO-1 in the liver were greatly increased, while the expression of Nrf2 was decreased sharply (P < 0.05).

Conclusion

Therefore, the feature extraction and classification system of ECG signal combined with surface EMG signal could realize real-time monitoring of EIF status. HBO intervention could improve the body's ability to resist oxidative stress through the Keap1/Nrf2/HO-1 pathway and then improve the EIF state. In addition, the improvement effect of HBO + NAX was more obvious.

A Review of EMG-, FMG-, and EIT-Based Biosensors and Relevant Human–Machine Interactivities and Biomedical Applications

Wearables developed for human body signal detection receive increasing attention in the current decade. Compared to implantable sensors, wearables are more focused on body motion detection, which can support human–machine interaction (HMI) and biomedical applications. In wearables, electromyography (EMG)-, force myography (FMG)-, and electrical impedance tomography (EIT)-based body information monitoring technologies are broadly presented. In the literature, all of them have been adopted for many similar application scenarios, which easily confuses researchers when they start to explore the area. Hence, in this article, we review the three technologies in detail, from basics including working principles, device architectures, interpretation algorithms, application examples, merits and drawbacks, to state-of-the-art works, challenges remaining to be solved and the outlook of the field. We believe the content in this paper could help readers create a whole image of designing and applying the three technologies in relevant scenarios.

Lung Cancer Prediction Using Robust Machine Learning and Image Enhancement Methods on Extracted Gray-Level Co-Occurrence Matrix Features

In the present era, cancer is the leading cause of demise in both men and women worldwide, with low survival rates due to inefficient diagnostic techniques. Recently, researchers have been devising methods to improve prediction performance. In medical image processing, image enhancement can further improve prediction performance. This study aimed to improve lung cancer image quality by utilizing and employing various image enhancement methods, such as image adjustment, gamma correction, contrast stretching, thresholding, and histogram equalization methods. We extracted the gray-level co-occurrence matrix (GLCM) features on enhancement images, and applied and optimized vigorous machine learning classification algorithms, such as the decision tree (DT), naïve Bayes, support vector machine (SVM) with Gaussian, radial base function (RBF), and polynomial. Without the image enhancement method, the highest performance was obtained using SVM, polynomial, and RBF, with accuracy of (99.89%). The image enhancement methods, such as image adjustment, contrast stretching at threshold (0.02, 0.98), and gamma correction at gamma value of 0.9, improved the prediction performance of our analysis on 945 images provided by the Lung Cancer Alliance MRI dataset, which yielded 100% accuracy and 1.00 of AUC using SVM, RBF, and polynomial kernels. The results revealed that the proposed methodology can be very helpful to improve the lung cancer prediction for further diagnosis and prognosis by expert radiologists to decrease the mortality rate.

New Hybrid Machine Learning Method for Detecting Faults in Three-Phase Power Transformers

A novel hybrid machine learning technique is proposed for the protection of three-phase power transformers in this study. Here, the developed model was tested across several types of current signal fault cases from different fault conditions and examined based on a laboratory-constructed transformer system, in which internal and external faults were created. The data gathered on signals were used to develop a novel hybrid model. A process for optimal feature identification was put forward, with machine learning classifiers being trained to classify faults. The methods used included orthogonal matching pursuit and discrete wavelet transform for extraction of statistical characteristics from unprocessed data. Following this, the bees algorithm (BA) was used to create an optimized subset, minimizing the amount of data needed and making the model more accurate. In order to distinguish normal operational conditions (inrush current) from faults, an optimized feature set was used as an input for three classification algorithms: the k-nearest neighbour, support vector machine, and artificial neural network. Training was conducted via k-fold cross-validation. Comparisons were made between the proposed approach and a comparable approach, which used the genetic algorithm (GA). This model was analysed based on specificity, accuracy, precision, recall, and F1 score. The findings from the experiment suggest that the model proposed here is suitable for fault identification in a range of conditions and faults.

Machine Learning for Detection of Muscular Activity from Surface EMG Signals

Background: Muscular-activity timing is useful information that is extractable from surface EMG signals (sEMG). However, a reference method is not available yet. The aim of this study is to investigate the reliability of a novel machine-learning-based approach (DEMANN) in detecting the onset/offset timing of muscle activation from sEMG signals. Methods: A dataset of 2880 simulated sEMG signals, stratified for signal-to-noise ratio (SNR) and time support, was generated to train a hidden single-layer fully-connected neural network. DEMANN’s performance was evaluated on simulated sEMG signals and two different datasets of real sEMG signals. DEMANN was validated against different reference algorithms, including the acknowledged double-threshold statistical algorithm (DT). Results: DEMANN provided a reliable prediction of muscle onset/offset in simulated and real sEMG signals, being minimally affected by SNR variability. When directly compared with state-of-the-art algorithms, DEMANN introduced relevant improvements in prediction performances. Conclusions: These outcomes support DEMANN’s reliability in assessing onset/offset events in different motor tasks and the condition of signal quality (different SNR), improving reference-algorithm performances. Unlike other works, DEMANN’s adopts a machine learning approach where a neural network is trained by only simulated sEMG signals, avoiding the possible complications and costs associated with a typical experimental procedure, making this approach suitable to clinical practice.

Two-stage binary classifier for neuromuscular disorders using surface electromyography feature extraction and selection

If surface electromyography (sEMG) can be used to determine neuromuscular disorders, it can diagnose conditions more easily than needle electromyography. In this study, sEMG during maximum voluntary isometric contraction and repetitive exercise was measured, and normal, myopathy, and neuropathy were classified with high accuracy using these signals. First, a two-stage binary classifier model was constructed to classify the patient group and the normal group and categorize the cases assigned to the patient group into myopathy and neuropathy groups. To this end, features related to muscle activity and muscle fatigue were extracted using activity analysis and frequency analysis of the sEMG signal. Since the features for high performance are different for each classifier, the features with statistical differences in the data of each class were selected for each classifier. The selected features and a two-stage binary classifier were distinguished with an accuracy of 86.9%. This shows an accuracy higher than 82.3%, which was found for the two-stage binary classifier without feature selection and 73.9% of the multi-classifier. Through this, the possibility of using sEMG to diagnose neuromuscular disorders was confirmed.

A new protocol to compare successful versus failed patients using the electromyographic diaphragm signal in extubation process

In clinical practice, when a patient is undergoing mechanical ventilation, it is important to identify the optimal moment for extubation, minimizing the risk of failure. However, this prediction remains a challenge in the clinical process. In this work, we propose a new protocol to study the extubation process, including the electromyographic diaphragm signal (diaEMG) recorded through 5-channels with surface electrodes around the diaphragm muscle. First channel corresponds to the electrode on the right. A total of 40 patients in process of withdrawal of mechanical ventilation, undergoing spontaneous breathing tests (SBT), were studied. According to the outcome of the SBT, the patients were classified into two groups: successful (SG: 19 patients) and failure (FG: 21 patients) groups. Parameters extracted from the envelope of each channel of diaEMG in time and frequency domain were studied. After analyzing all channels, the second presented maximum differences when comparing the two groups of patients, with parameters related to root mean square (p = 0.005), moving average (p = 0.001), and upward slope (p = 0.017). The third channel also presented maximum differences in parameters as the time between maximum peak (p = 0.004), and the skewness (p = 0.027). These results suggest that diaphragm EMG signal could contribute to increase the knowledge of the behaviour of respiratory system in these patients and improve the extubation process.Clinical Relevance-This establishes the characterization of success and failure patients in the extubation process.

A Machine-Learning Method of Predicting Vital Capacity Plateau Value for Ventilatory Pump Failure Based on Data Mining

Ventilatory pump failure is a common cause of death for patients with neuromuscular diseases. The vital capacity plateau value (VCPLAT) is an important indicator to judge the status of ventilatory pump failure for patients with congenital myopathy, Duchenne muscular dystrophy and spinal muscular atrophy. Due to the complex relationship between VCPLAT and the patient's own condition, it is difficult to predict the VCPLAT for pediatric disease from a medical perspective. We established a VCPLAT prediction model based on data mining and machine learning. We first performed the correlation analysis and recursive feature elimination with cross-validation (RFECV) to provide high-quality feature combinations. Based on this, the Light Gradient Boosting Machine (LightGBM) algorithm was to establish a prediction model with powerful performance. Finally, we verified the validity and superiority of the proposed method via comparison with other prediction models in similar works. After 10-fold cross-validation, the proposed prediction method had the best performance and its explained variance score (EVS), mean absolute error (MAE), mean squared error (MSE), root mean square error (RMSE), median absolute error (MedAE) and R2 were 0.949, 0.028, 0.002, 0.045, 0.015 and 0.948, respectively. It also performed well on test datasets. Therefore, it can accurately and effectively predict the VCPLAT, thereby determining the severity of the condition to provide auxiliary decision-making for doctors in clinical diagnosis and treatment.

Prediction of Mental Health in Medical Workers During COVID-19 Based on Machine Learning

Mental health prediction is one of the most essential parts of reducing the probability of serious mental illness. Meanwhile, mental health prediction can provide a theoretical basis for public health department to work out psychological intervention plans for medical workers. The purpose of this paper is to predict mental health of medical workers based on machine learning by 32 factors. We collected the 32 factors of 5,108 Chinese medical workers through questionnaire survey, and the results of Self-reporting Inventory was applied to characterize mental health. In this study, we propose a novel prediction model based on optimization algorithm and neural network, which can select and rank the most important factors that affect mental health of medical workers. Besides, we use stepwise logistic regression, binary bat algorithm, hybrid improved dragonfly algorithm and the proposed prediction model to predict mental health of medical workers. The results show that the prediction accuracy of the proposed model is 92.55%, which is better than the existing algorithms. This method can be used to predict mental health of global medical worker. In addition, the method proposed in this paper can also play a role in the appropriate work plan for medical worker.

A Systematic Literature Review on Particle Swarm Optimization Techniques for Medical Diseases Detection

Artificial Intelligence (AI) is the domain of computer science that focuses on the development of machines that operate like humans. In the field of AI, medical disease detection is an instantly growing domain of research. In the past years, numerous endeavours have been made for the improvements of medical disease detection, because the errors and problems in medical disease detection cause serious wrong medical treatment. Meta-heuristic techniques have been frequently utilized for the detection of medical diseases and promise better accuracy of perception and prediction of diseases in the domain of biomedical. Particle Swarm Optimization (PSO) is a swarm-based intelligent stochastic search technique encouraged from the intrinsic manner of bee swarm during the searching of their food source. Consequently, for the versatility of numerical experimentation, PSO has been mostly applied to address the diverse kinds of optimization problems. However, the PSO techniques are frequently adopted for the detection of diseases but there is still a gap in the comparative survey. This paper presents an insight into the diagnosis of medical diseases in health care using various PSO approaches. This study presents to deliver a systematic literature review of current PSO approaches for knowledge discovery in the field of disease detection. The systematic analysis discloses the potential research areas of PSO strategies as well as the research gaps, although, the main goal is to provide the directions for future enhancement and development in this area. This paper gives a systematic survey of this conceptual model for the advanced research, which has been explored in the specified literature to date. This review comprehends the fundamental concepts, theoretical foundations, and conventional application fields. It is predicted that our study will be beneficial for the researchers to review the PSO algorithms in-depth for disease detection. Several challenges that can be undertaken to move the field forward are discussed according to the current state of the PSO strategies in health care.

Flexible Electrodes for In Vivo and In Vitro Electrophysiological Signal Recording

A variety of electrophysiological signals (electrocardiography, electromyography, electroencephalography, etc.) are generated during the physiological activities of human bodies, which can be collected by electrodes and thus provide critical insights into health status or facilitate fundamental scientific research. The long-term stable and high-quality recording of electrophysiological signals is the premise for their further applications, leading to demands for flexible electrodes with similar mechanical modulus and minimized irritation to human bodies. This review summarizes the latest advances in flexible electrodes for the acquisition of various electrophysiological signals. First, the concept of electrophysiological signals and the characteristics of different subcategory signals are introduced. Second, the invasive and noninvasive methods are reviewed for electrophysiological signal recording with a highlight on the design of flexible electrodes, followed by a discussion on their material selection. Subsequently, the applications of the electrophysiological signal acquisition in pathological diagnosis and restoration of body functions are discussed, showing the advantages of flexible electrodes. Finally, the main challenges and opportunities in this field are discussed. It is believed that the further exploration of materials for flexible electrodes and the combination of multidisciplinary technologies will boost the applications of flexible electrodes for medical diagnosis and human-machine interface.

Machine learning classification of texture features of MRI breast tumor and peri-tumor of combined pre- and early treatment predicts pathologic complete response

Purpose

This study used machine learning classification of texture features from MRI of breast tumor and peri-tumor at multiple treatment time points in conjunction with molecular subtypes to predict eventual pathological complete response (PCR) to neoadjuvant chemotherapy.

Materials and method

This study employed a subset of patients (N = 166) with PCR data from the I-SPY-1 TRIAL (2002–2006). This cohort consisted of patients with stage 2 or 3 breast cancer that underwent anthracycline–cyclophosphamide and taxane treatment. Magnetic resonance imaging (MRI) was acquired pre-neoadjuvant chemotherapy, early, and mid-treatment. Texture features were extracted from post-contrast-enhanced MRI, pre- and post-contrast subtraction images, and with morphological dilation to include peri-tumoral tissue. Molecular subtypes and Ki67 were also included in the prediction model. Performance of classification models used the receiver operating characteristics curve analysis including area under the curve (AUC). Statistical analysis was done using unpaired two-tailed t-tests.

Results

Molecular subtypes alone yielded moderate prediction performance of PCR (AUC = 0.82, p = 0.07). Pre-, early, and mid-treatment data alone yielded moderate performance (AUC = 0.88, 0.72, and 0.78, p = 0.03, 0.13, 0.44, respectively). The combined pre- and early treatment data markedly improved performance (AUC = 0.96, p = 0.0003). Addition of molecular subtypes improved performance slightly for individual time points but substantially for the combined pre- and early treatment (AUC = 0.98, p = 0.0003). The optimal morphological dilation was 3–5 pixels. Subtraction of post- and pre-contrast MRI further improved performance (AUC = 0.98, p = 0.00003). Finally, among the machine-learning algorithms evaluated, the RUSBoosted Tree machine-learning method yielded the highest performance.

Conclusion

AI-classification of texture features from MRI of breast tumor at multiple treatment time points accurately predicts eventual PCR. Longitudinal changes in texture features and peri-tumoral features further improve PCR prediction performance. Accurate assessment of treatment efficacy early on could minimize unnecessary toxic chemotherapy and enable mid-treatment modification for patients to achieve better clinical outcomes.

SA-SVM-Based Locomotion Pattern Recognition for Exoskeleton Robot

An exoskeleton robot is a kind of wearable mechanical instrument designed according to the shape and function of the human body. The main purpose of its design and manufacture is to enhance human strength, assist human walking and to help patients recover. The walking state of the exoskeleton robot should be highly consistent with the state of the human, so the accurate locomotion pattern recognition is the premise of the flexible control of the exoskeleton robot. In this paper, a simulated annealing (SA) algorithm-based support vector machine model is proposed for the recognition of different locomotion patterns. In order to improve the overall performance of the support vector machine (SVM), the simulated annealing algorithm is adopted to obtain the optimal parameters of support vector machine. The pressure signal measured by the force sensing resistors integrated on the sole of the shoe is fused with the position and pose information measured by the inertial measurement units attached to the thigh, shank and foot, which are used as the input information of the support vector machine. The max-relevance and min-redundancy algorithm was selected for feature extraction based on the window size of 300 ms and the sampling frequency of 100 Hz. Since the signals come from different types of sensors, normalization is required to scale the input signals to the interval (0,1). In order to prevent the classifier from overfitting, five layers of cross validation are used to train the support vector machine classifier. The support vector machine model was obtained offline in MATLAB. The finite state machine is used to limit the state transition and improve the recognition accuracy. Experiments on different locomotion patterns show that the accuracy of the algorithm is 97.47% ± 1.16%. The SA-SVM method can be extended to industrial robots and rehabilitation robots.

Biomedical signals and machine learning in amyotrophic lateral sclerosis: a systematic review

INTRODUCTION

The use of machine learning (ML) techniques in healthcare encompasses an emerging concept that envisages vast contributions to the tackling of rare diseases. In this scenario, amyotrophic lateral sclerosis (ALS) involves complexities that are yet not demystified. In ALS, the biomedical signals present themselves as potential biomarkers that, when used in tandem with smart algorithms, can be useful to applications within the context of the disease.

METHODS

This Systematic Literature Review (SLR) consists of searching for and investigating primary studies that use ML techniques and biomedical signals related to ALS. Following the definition and execution of the SLR protocol, 18 articles met the inclusion, exclusion, and quality assessment criteria, and answered the SLR research questions.

DISCUSSIONS

Based on the results, we identified three classes of ML applications combined with biomedical signals in the context of ALS: diagnosis (72.22%), communication (22.22%), and survival prediction (5.56%).

CONCLUSIONS

Distinct algorithmic models and biomedical signals have been reported and present promising approaches, regardless of their classes. In summary, this SLR provides an overview of the primary studies analyzed as well as directions for the construction and evolution of technology-based research within the scope of ALS.

A multiple combined method for rebalancing medical data with class imbalances

Most classification algorithms assume that classes are in a balanced state. However, datasets with class imbalances are everywhere. The classes of actual medical datasets are imbalanced, severely impacting identification models and even sacrificing the classification accuracy of the minority class, even though it is the most influential and representative. The medical field has irreversible characteristics. Its tolerance rate for misjudgment is relatively low, and errors may cause irreparable harm to patients. Therefore, this study proposes a multiple combined method to rebalance medical data featuring class imbalances. The combined methods include (1) resampling methods (synthetic minority oversampling technique [SMOTE] and undersampling [US]), (2) particle swarm optimization (PSO), and (3) MetaCost. This study conducted two experiments with nine medical datasets to verify and compare the proposed method with the listing methods. A decision tree is used to generate decision rules for easy understanding of the research results. The results show that (1) the proposed method with ensemble learning can improve the area under a receiver operating characteristic curve (AUC), recall, precision, and F1 metrics; (2) MetaCost can increase sensitivity; (3) SMOTE can effectively enhance AUC; (4) US can improve sensitivity, F1, and misclassification costs in data with a high-class imbalance ratio; and (5) PSO-based attribute selection can increase sensitivity and reduce data dimension. Finally, we suggest that the dataset with an imbalanced ratio >9 must use the US results to make the decision. As the imbalanced ratio is < 9, the decision-maker can simultaneously consider the results of SMOTE and US to identify the best decision.

A New Initialization Approach in Particle Swarm Optimization for Global Optimization Problems

Particle swarm optimization (PSO) algorithm is a population-based intelligent stochastic search technique used to search for food with the intrinsic manner of bee swarming. PSO is widely used to solve the diverse problems of optimization. Initialization of population is a critical factor in the PSO algorithm, which considerably influences the diversity and convergence during the process of PSO. Quasirandom sequences are useful for initializing the population to improve the diversity and convergence, rather than applying the random distribution for initialization. The performance of PSO is expanded in this paper to make it appropriate for the optimization problem by introducing a new initialization technique named WELL with the help of low-discrepancy sequence. To solve the optimization problems in large-dimensional search spaces, the proposed solution is termed as WE-PSO. The suggested solution has been verified on fifteen well-known unimodal and multimodal benchmark test problems extensively used in the literature, Moreover, the performance of WE-PSO is compared with the standard PSO and two other initialization approaches Sobol-based PSO (SO-PSO) and Halton-based PSO (H-PSO). The findings indicate that WE-PSO is better than the standard multimodal problem-solving techniques. The results validate the efficacy and effectiveness of our approach. In comparison, the proposed approach is used for artificial neural network (ANN) learning and contrasted to the standard backpropagation algorithm, standard PSO, H-PSO, and SO-PSO, respectively. The results of our technique has a higher accuracy score and outperforms traditional methods. Also, the outcome of our work presents an insight on how the proposed initialization technique has a high effect on the quality of cost function, integration, and diversity aspects.

Adaptive Extreme Edge Computing for Wearable Devices

Wearable devices are a fast-growing technology with impact on personal healthcare for both society and economy. Due to the widespread of sensors in pervasive and distributed networks, power consumption, processing speed, and system adaptation are vital in future smart wearable devices. The visioning and forecasting of how to bring computation to the edge in smart sensors have already begun, with an aspiration to provide adaptive extreme edge computing. Here, we provide a holistic view of hardware and theoretical solutions toward smart wearable devices that can provide guidance to research in this pervasive computing era. We propose various solutions for biologically plausible models for continual learning in neuromorphic computing technologies for wearable sensors. To envision this concept, we provide a systematic outline in which prospective low power and low latency scenarios of wearable sensors in neuromorphic platforms are expected. We successively describe vital potential landscapes of neuromorphic processors exploiting complementary metal-oxide semiconductors (CMOS) and emerging memory technologies (e.g., memristive devices). Furthermore, we evaluate the requirements for edge computing within wearable devices in terms of footprint, power consumption, latency, and data size. We additionally investigate the challenges beyond neuromorphic computing hardware, algorithms and devices that could impede enhancement of adaptive edge computing in smart wearable devices.

Development of a support vector machine learning and smart phone Internet of Things-based architecture for real-time sleep apnea diagnosis

Background

The breathing disorder obstructive sleep apnea syndrome (OSAS) only occurs while asleep. While polysomnography (PSG) represents the premiere standard for diagnosing OSAS, it is quite costly, complicated to use, and carries a significant delay between testing and diagnosis.

Methods

This work describes a novel architecture and algorithm designed to efficiently diagnose OSAS via the use of smart phones. In our algorithm, features are extracted from the data, specifically blood oxygen saturation as represented by SpO2. These features are used by a support vector machine (SVM) based strategy to create a classification model. The resultant SVM classification model can then be employed to diagnose OSAS. To allow remote diagnosis, we have combined a simple monitoring system with our algorithm. The system allows physiological data to be obtained from a smart phone, the data to be uploaded to the cloud for processing, and finally population of a diagnostic report sent back to the smart phone in real-time.

Results

Our initial evaluation of this algorithm utilizing actual patient data finds its sensitivity, accuracy, and specificity to be 87.6%, 90.2%, and 94.1%, respectively.

Discussion

Our architecture can monitor human physiological readings in real time and give early warning of abnormal physiological parameters. Moreover, after our evaluation, we find 5G technology offers higher bandwidth with lower delays ensuring more effective monitoring. In addition, we evaluate our algorithm utilizing real-world data; the proposed approach has high accuracy, sensitivity, and specific, demonstrating that our approach is very promising.

Conclusions

Experimental results on the apnea data in University College Dublin (UCD) Database have proven the efficiency and effectiveness of our methodology. This work is a pilot project and still under development. There is no clinical validation and no support. In addition, the Internet of Things (IoT) architecture enables real-time monitoring of human physiological parameters, combined with diagnostic algorithms to provide early warning of abnormal data.

Machine learning based congestive heart failure detection using feature importance ranking of multimodal features

In this study, we ranked the Multimodal Features extracted from Congestive Heart Failure (CHF) and Normal Sinus Rhythm (NSR) subjects. We categorized the ranked features into 1 to 5 categories based on Empirical Receiver Operating Characteristics (EROC) values. Instead of using all multimodal features, we use high ranking features for detection of CHF and normal subjects. We employed powerful machine learning techniques such as Decision Tree (DT), Naïve Bayes (NB), SVM Gaussian, SVM RBF and SVM Polynomial. The performance was measured in terms of Sensitivity, Specificity, Positive Predictive Value (PPV), Negative Predictive Value (NPV), Accuracy, False Positive Rate (FPR), and area under the Receiver Operating characteristic Curve (AUC). The highest detection performance in terms of accuracy and AUC was obtained with all multimodal features using SVM Gaussian with Sensitivity (93.06%), Specificity (81.82%), Accuracy (88.79%) and AUC (0.95). Using the top five ranked features, the highest performance was obtained with SVM Gaussian yields accuracy (84.48%), AUC (0.86); top nine ranked features using Decision Tree and Naïve Bayes got accuracy (84.48%), AUC (0.88); last thirteen ranked features using SVM polynomial obtained accuracy (80.17%), AUC (0.84). The findings indicate that proposed approach with feature ranking can be very useful for automatic detection of congestive heart failure patients and can be very helpful for further decision making by the clinicians and physicians in order to decrease the mortality rate.

Detecting Brain Tumor using Machines Learning Techniques Based on Different Features Extracting Strategies

BACKGROUND

Brain tumor is the leading cause of death worldwide. It is obvious that the chances of survival can be increased if the tumor is identified and properly classified at an initial stage. MRI (Magnetic Resonance Imaging) is one source of brain tumors detection tool and is extensively used in the diagnosis of brain to detect blood clots. In the past, many researchers developed Computer-Aided Diagnosis (CAD) systems that help the radiologist to detect the abnormalities in an efficient manner.

OBJECTIVE

The aim of this research is to improve the brain tumor detection performance by proposing a multimodal feature extracting strategy and employing machine learning techniques.

METHODS

In this study, we extracted multimodal features such as texture, morphological, entropybased, Scale Invariant Feature Transform (SIFT), and Elliptic Fourier Descriptors (EFDs) from brain tumor imaging database. The tumor was detected using robust machine learning techniques such as Support Vector Machine (SVM) with kernels: polynomial, Radial Base Function (RBF), Gaussian; Decision Tree (DT), and Naïve Bayes. Most commonly used Jack-knife 10-fold Cross- Validation (CV) was used for testing and validation of dataset.

RESULTS

The performance was evaluated in terms of specificity, sensitivity, Positive Predictive Value (PPV), Negative Predictive Value (NPV), False Positive Rate (FPR), Total Accuracy (TA), Area under the receiver operating Curve (AUC), and P-value. The highest performance of 100% in terms of Specificity, Sensitivity, PPV, NPV, TA, AUC using Naïve Bayes classifiers based on entropy, morphological, SIFT and texture features followed by Decision Tree classifier with texture features (TA=97.81%, AUC=1.0) and SVM polynomial kernel with texture features (TA=94.63%). The highest significant p-value was obtained using SVM polynomial with texture features (P-value 2.65e-104) followed by SVM RB with texture features (P-value 1.96e-98).

CONCLUSION

The results reveal that Naïve Bayes followed by Decision Tree gives highest detection accuracy based on entropy, morphological, SIFT and texture features.

Comparison study of classification methods of intramuscular electromyography data for non-human primate model of traumatic spinal cord injury

Traumatic spinal cord injury is a serious neurological disorder. Patients experience a plethora of symptoms that can be attributed to the nerve fiber tracts that are compromised. This includes limb weakness, sensory impairment, and truncal instability, as well as a variety of autonomic abnormalities. This article will discuss how machine learning classification can be used to characterize the initial impairment and subsequent recovery of electromyography signals in an non-human primate model of traumatic spinal cord injury. The ultimate objective is to identify potential treatments for traumatic spinal cord injury. This work focuses specifically on finding a suitable classifier that differentiates between two distinct experimental stages (pre-and post-lesion) using electromyography signals. Eight time-domain features were extracted from the collected electromyography data. To overcome the imbalanced dataset issue, synthetic minority oversampling technique was applied. Different ML classification techniques were applied including multilayer perceptron, support vector machine, K-nearest neighbors, and radial basis function network; then their performances were compared. A confusion matrix and five other statistical metrics (sensitivity, specificity, precision, accuracy, and F-measure) were used to evaluate the performance of the generated classifiers. The results showed that the best classifier for the left- and right-side data is the multilayer perceptron with a total F-measure of 79.5% and 86.0% for the left and right sides, respectively. This work will help to build a reliable classifier that can differentiate between these two phases by utilizing some extracted time-domain electromyography features.

Neural dynamics of sentiment processing during naturalistic sentence reading

When we read, our eyes move through the text in a series of fixations and high-velocity saccades to extract visual information. This process allows the brain to obtain meaning, e.g., about sentiment, or the emotional valence, expressed in the written text. How exactly the brain extracts the sentiment of single words during naturalistic reading is largely unknown. This is due to the challenges of naturalistic imaging, which has previously led researchers to employ highly controlled, timed word-by-word presentations of custom reading materials that lack ecological validity. Here, we aimed to assess the electrical neural correlates of word sentiment processing during naturalistic reading of English sentences. We used a publicly available dataset of simultaneous electroencephalography (EEG), eye-tracking recordings, and word-level semantic annotations from 7129 words in 400 sentences (Zurich Cognitive Language Processing Corpus; Hollenstein et al., 2018). We computed fixation-related potentials (FRPs), which are evoked electrical responses time-locked to the onset of fixations. A general linear mixed model analysis of FRPs cleaned from visual- and motor-evoked activity showed a topographical difference between the positive and negative sentiment condition in the 224-304 ​ms interval after fixation onset in left-central and right-posterior electrode clusters. An additional analysis that included word-, phrase-, and sentence-level sentiment predictors showed the same FRP differences for the word-level sentiment, but no additional FRP differences for phrase- and sentence-level sentiment. Furthermore, decoding analysis that classified word sentiment (positive or negative) from sentiment-matched 40-trial average FRPs showed a 0.60 average accuracy (95% confidence interval: [0.58, 0.61]). Control analyses ruled out that these results were based on differences in eye movements or linguistic features other than word sentiment. Our results extend previous research by showing that the emotional valence of lexico-semantic stimuli evoke a fast electrical neural response upon word fixation during naturalistic reading. These results provide an important step to identify the neural processes of lexico-semantic processing in ecologically valid conditions and can serve to improve computer algorithms for natural language processing.