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The electrocardiogram (ECG) is one of the most significant methods of diagnostics for determining heart rhythm disorders. For this study, raw ECG signals from the Physio Bank database are subjected to an important preprocessing step that uses empirical mode decomposition (EMD) on signal denoising and distortion elimination. Establishing functioning spiking neural networks (SNN) involves figuring out the neuron's state through its activity level, challenging due to its resemblance to the human brain's data processing, yet appealing due to factors like improved unsupervised learning methods, with ten parameters chosen for the learning algorithm of SNN. A comprehensive set of 15 different time-domain features and 10 Cepstral domain features is precisely extracted to train the SNN classifier. An extensive study is conducted to analyse the learning parameters that affect SNN performance, significantly influencing result accuracy. Through a two-classification process, the differentiation between normal and abnormal ECG patterns can be achieved in this study. A maximum testing accuracy of 91.6667% and a maximum training accuracy of 99.1667% have been attained by the process. These results demonstrate the competency of the system in distinguishing between distinct ECG classes, particularly in identifying normal and abnormal cardiac rhythms through ECG classification. [ABSTRACT FROM AUTHOR]

Background and Objectives: Accurate diagnosis of cardiovascular diseases often relies on the electrocardiogram (ECG). Since the cardiac vector is located within a three-dimensional space and the standard ECG comprises 12 projections or leads derived from it, redundant information is inherently present. This study aims to quantify this redundancy and its impact on classification tasks using Convolutional Neural Networks (CNNs) in cardiovascular diseases. Methods: We employed signal theory and mutual information to introduce a novel redundancy metric and explored techniques for redundancy augmentation and reduction. This involved lead selection and transformation to evaluate the effects on neural network performance. Results: Our findings indicate that optimizing input configurations through redundancy reduction techniques can enhance the performance of deep learning models in cardiovascular diagnostics, provided that the information is preserved and minimally distorted. Conclusion: For the first time, this research has quantified the redundancy present in the input by validating various redundancy reduction techniques using a CNN. This discovery paves the way for advancing biomedical signal processing research, simplifying model complexity, and enhancing diagnostic performance in cardiovascular medicine within reduced lead systems, such as Holter monitors or wearables. [ABSTRACT FROM AUTHOR]

Due to the advancement in biomedical technologies, to diagnose problems in people, a number of psychological signals are extracted from patients. We should be able to ensure that psychological signals are not altered by adversaries and it should be possible to relate a patient to his/her corresponding psychological signal. As far as our awareness extends, none of the existing methods possess the capability to both identify and verify the authenticity of the ECG signals. Consequently, this paper introduces an innovative dual-layer data-embedding approach for electrocardiogram (ECG) signals, aiming to achieve both signal identification and authenticity verification. Since file name-based signal identification is vulnerable to modifications, we propose a robust watermarking method which will embed patient-related details such as patient identification number, into the medically less-significant portion of the ECG signals. The proposed robust watermarking algorithm adds data into ECG signals such that the patient information hidden in an ECG signal can resist the filtering attack (such as high-pass filtering) and noise addition. This is achieved via the use of error buffers in the embedding algorithm. Further, modification-sensitive fragile watermarks are added to ECG signals. By extracting and checking the fragile watermark bits, we can determine whether an ECG signal is modified or not. To ensure the security of the proposed mechanism, two secret keys are used. Our evaluation demonstrates the usefulness of the proposed system. [ABSTRACT FROM AUTHOR]

Electroretinography (ERG) is a non-invasive method of assessing retinal function by recording the retina's response to a brief flash of light. This study focused on optimizing the ERG waveform signal classification by utilizing Short-Time Fourier Transform (STFT) spectrogram preprocessing with a machine learning (ML) decision system. Several window functions of different sizes and window overlaps were compared to enhance feature extraction concerning specific ML algorithms. The obtained spectrograms were employed to train deep learning models alongside manual feature extraction for more classical ML models. Our findings demonstrated the superiority of utilizing the Visual Transformer architecture with a Hamming window function, showcasing its advantage in ERG signal classification. Also, as a result, we recommend the RF algorithm for scenarios necessitating manual feature extraction, particularly with the Boxcar (rectangular) or Bartlett window functions. By elucidating the optimal methodologies for feature extraction and classification, this study contributes to advancing the diagnostic capabilities of ERG analysis in clinical settings. [ABSTRACT FROM AUTHOR]

Electrocardiography (ECG) plays a pivotal role in monitoring cardiac health, yet the manual analysis of ECG signals is challenging due to the complex task of identifying and categorizing various waveforms and morphologies within the data. Additionally, ECG datasets often suffer from a significant class imbalance issue, which can lead to inaccuracies in detecting minority class samples. To address these challenges and enhance the effectiveness and efficiency of cardiac arrhythmia detection from imbalanced ECG datasets, this study proposes a novel approach. This research leverages the MIT-BIH arrhythmia dataset, encompassing a total of 109,446 ECG beats distributed across five classes following the Association for the Advancement of Medical Instrumentation (AAMI) standard. Given the dataset's inherent class imbalance, a 1D generative adversarial network (GAN) model is introduced, incorporating the Bi-LSTM model to synthetically generate the two minority signal classes, which represent a mere 0.73% fusion (F) and 2.54% supraventricular (S) of the data. The generated signals are rigorously evaluated for similarity to real ECG data using three key metrics: mean squared error (MSE), structural similarity index (SSIM), and Pearson correlation coefficient (r). In addition to addressing data imbalance, the work presents three deep learning models tailored for ECG classification: SkipCNN (a convolutional neural network with skip connections), SkipCNN+LSTM, and SkipCNN+LSTM+Attention mechanisms. To further enhance efficiency and accuracy, the test dataset is rigorously assessed using an ensemble model, which consistently outperforms the individual models. The performance evaluation employs standard metrics such as precision, recall, and F1-score, along with their average, macro average, and weighted average counterparts. Notably, the SkipCNN+LSTM model emerges as the most promising, achieving remarkable precision, recall, and F1-scores of 99.3%, which were further elevated to an impressive 99.60% through ensemble techniques. Consequently, with this innovative combination of data balancing techniques, the GAN-SkipNet model not only resolves the challenges posed by imbalanced data but also provides a robust and reliable solution for cardiac arrhythmia detection. This model stands poised for clinical applications, offering the potential to be deployed in hospitals for real-time cardiac arrhythmia detection, thereby benefiting patients and healthcare practitioners alike. [ABSTRACT FROM AUTHOR]

To understand the information processing mechanism of the brain, it is important to decode the bidirectional communication between the brain and organs. For this purpose, computational models were proposed to simulate brain–organ interfaces at different levels of abstraction. Conventional computational models can be modified to understand the bidirectional interactions for further clarification and treatment of morbidity. In this work, a unified model of excitable cells (brain, heart, and pancreatic cells) is proposed that can predict the electrical response with adrenergic features. This enables us to activate the sparsely coupled cardio-neural network to estimate the heart rate variability, one of the key features to identify a healthy heart. The recent advancements in nano- and bioelectronics will make it possible to build and deploy the brain–heart interface as a nanochip in the body to monitor and control the electrophysiological abnormality of the brain and heart by integrating nano-regulators with ion channels for stimulation. [ABSTRACT FROM AUTHOR]

This paper introduces a fully differential asynchronous successive approximation register analog-to-digital converter (SAR ADC) designed for biomedical signal processing. By extending the tracking time and utilizing fully differential inputs in the analog front-end circuit, the signal-to-noise ratio is enhanced in the system. Using an asynchronous clock can reduce power consumption across a wider range of sampling frequencies. In comparison to conventional architecture in high-speed SAR ADC, using an internal clock generator can operate at lower frequencies. A fully differential input can eliminate the DC offset of the analog front-end circuit and reduce the adverse effects of process variation, voltage variation, and temperature variation. The chip is implemented by TSMC 0.18 μ m complementary metal-oxide-semiconductor (CMOS) technology, and the chip area is 0.680 mm2 (including ESD I/O PAD). At a 1.2 V supply, the maximum sampling rate is 10 Kilo Samples per second (KSps). The implemented ADC has an 11-bit resolution, while the input voltage range is 300∼900 mV. The total power consumption is 1.7 μ W, with the core power consumption at 932 nW. [ABSTRACT FROM AUTHOR]

With the development of modern technology, many people work for a long time around various artificial light sources and electronic equipment, causing them to feel discomfort in their eyes and even eye diseases. The industry currently lacks an objective quantitative environmental–visual comfort index that combines subjective and objective indicators. For this experiment, objective eye movement and electroencephalogram (EEG) signals were collected in combination with a subjective questionnaire survey and a preference inquiry for comprehensive data mining. Finally, the results on a Likert scale show that high screen brightness can reduce the visual fatigue of subjects under high illuminance and high correlated color temperature (CCT). Pupil data show that, under medium and high ambient illuminance, visual perception sensitivity is more likely to be stimulated, and visual fatigue is more likely to deepen. EEG data show that visual fatigue is related to illuminance and screen brightness. On this basis, this study proposes a new evaluation index, the visual comfort level (0.6404 average at a low screen brightness, 0.4218 average at a medium screen brightness, and 0.5139 average at a high screen brightness), where a higher score for the visual comfort level represents a better visual experience. The visual comfort level provides a useful reference for enhancing the processing of multi-dimensional and biomedical signals and protecting the eyes. [ABSTRACT FROM AUTHOR]

(1) Objective: This study aims to lay a foundation for noncontact intensive care monitoring of premature babies. (2) Methods: Arterial oxygen saturation and heart rate were measured using a monochrome camera and time-division multiplex controlled lighting at three different wavelengths (660 nm, 810 nm and 940 nm) on a piglet model. (3) Results: Using this camera system and our newly designed algorithm for further analysis, the detection of a heartbeat and the calculation of oxygen saturation were evaluated. In motionless individuals, heartbeat and respiration were separated clearly during light breathing and with only minor intervention. In this case, the mean difference between noncontact and contact saturation measurements was 0.7% (RMSE = 3.8%, MAE = 2.93%). (4) Conclusions: The new sensor was proven effective under ideal animal experimental conditions. The results allow a systematic improvement for the further development of contactless vital sign monitoring systems. The results presented here are a major step towards the development of an incubator with noncontact sensor systems for use in the neonatal intensive care unit. [ABSTRACT FROM AUTHOR]

Abstract Due to the advancement in biomedical technologies, to diagnose problems in people, a number of psychological signals are extracted from patients. We should be able to ensure that psychological signals are not altered by adversaries and it should be possible to relate a patient to his/her corresponding psychological signal. As far as our awareness extends, none of the existing methods possess the capability to both identify and verify the authenticity of the ECG signals. Consequently, this paper introduces an innovative dual-layer data-embedding approach for electrocardiogram (ECG) signals, aiming to achieve both signal identification and authenticity verification. Since file name-based signal identification is vulnerable to modifications, we propose a robust watermarking method which will embed patient-related details such as patient identification number, into the medically less-significant portion of the ECG signals. The proposed robust watermarking algorithm adds data into ECG signals such that the patient information hidden in an ECG signal can resist the filtering attack (such as high-pass filtering) and noise addition. This is achieved via the use of error buffers in the embedding algorithm. Further, modification-sensitive fragile watermarks are added to ECG signals. By extracting and checking the fragile watermark bits, we can determine whether an ECG signal is modified or not. To ensure the security of the proposed mechanism, two secret keys are used. Our evaluation demonstrates the usefulness of the proposed system.

Biomedical signal processing is crucial in many sectors that save lives. Artificial intelligence improvement in signal collection and conditioning boosted this application's adaptability to varied bodily circumstances. In this study, a novel method is put forth for predicting the type of stroke in the human brain based on the observation of the Electroencephalography (EEG) signal. The signal is the first condition for removing undesirable frequencies by passing through a lowpass filter. To accurately extract the signal features, the signal is first transformed into a 1-second frame format and then normalised. Certain statistical and frequency domain aspects are highlighted to increase taxonomic accuracy. Under the wavelet packet transform, the empirical mode decomposition approach is utilised to recover the most information feasible from the signal. After training on extracted characteristics, the extreme learning machine is regarded to conduct classification. These work achieves 94.95 of Sensitivity, 84.95 of Specificity, 93.74 of Precision, 96.96 of Accuracy, 96.12 of F1 Score. Compared to the standard procedures, the proposed techniques have a greater accuracy rate of about 98%. [ABSTRACT FROM AUTHOR]

Classified as biomedical signal processing, cerebral signal processing plays a key role in human-computer interaction (HCI) and medical diagnosis. The motor imagery (MI) problem is an important research area in this field. Accurate solutions to this problem will greatly affect real-world applications. Most of the proposed methods are based on raw signal processing techniques. Known as prior knowledge, the structural-functional information and interregional connections can improve signal processing accuracy. It is possible to correctly perceive the generated signals by considering the brain structure (i.e. anatomical units), the source of signals, and the structural-functional dependence of different brain regions (i.e. effective connection) that are the semantic generators of signals. This study employed electroencephalograph (EEG) signals based on the activity of brain regions (cortex) and effective connections between brain regions based on dynamic causal modeling to solve the MI problem. EEG signals, as well as effective connections between brain regions to improve the interpretability of MI action, were fed into the architecture of Graph Convolutional Neural Network (GCN). The proposed model allowed GCN to extract more discriminative features. The results indicated that the proposed method was successful in developing a model with a MI detection accuracy of 93.73%. [ABSTRACT FROM AUTHOR]

Objective: This study was undertaken to develop and evaluate a machine learning‐based algorithm for the detection of focal to bilateral tonic–clonic seizures (FBTCS) using a novel multimodal connected shirt. Methods: We prospectively recruited patients with epilepsy admitted to our epilepsy monitoring unit and asked them to wear the connected shirt while under simultaneous video‐electroencephalographic monitoring. Electrocardiographic (ECG) and accelerometric (ACC) signals recorded with the connected shirt were used for the development of the seizure detection algorithm. First, we used a sliding window to extract linear and nonlinear features from both ECG and ACC signals. Then, we trained an extreme gradient boosting algorithm (XGBoost) to detect FBTCS according to seizure onset and offset annotated by three board‐certified epileptologists. Finally, we applied a postprocessing step to regularize the classification output. A patientwise nested cross‐validation was implemented to evaluate the performances in terms of sensitivity, false alarm rate (FAR), time in false warning (TiW), detection latency, and receiver operating characteristic area under the curve (ROC‐AUC). Results: We recorded 66 FBTCS from 42 patients who wore the connected shirt for a total of 8067 continuous hours. The XGBoost algorithm reached a sensitivity of 84.8% (56/66 seizures), with a median FAR of.55/24 h and a median TiW of 10 s/alarm. ROC‐AUC was.90 (95% confidence interval =.88–.91). Median detection latency from the time of progression to the bilateral tonic–clonic phase was 25.5 s. Significance: The novel connected shirt allowed accurate detection of FBTCS with a low false alarm rate in a hospital setting. Prospective studies in a residential setting with a real‐time and online seizure detection algorithm are required to validate the performance and usability of this device. [ABSTRACT FROM AUTHOR]

This paper presents the design of an efficient operational transconductance amplifier (OTA) to be explicitly used for electroencephalogram (EEG) signal acquisition in neural amplifiers (NAs). The central objective of this study revolves around addressing the fundamental compromise between noise and power in the design of NAs. The overarching goal is to effectively mitigate this trade-off by introducing novel approaches. The proposed design's novelty lies in utilizing an adaptive biasing technique instead of the traditional current mirror biasing technique. Furthermore, the conventional input differential pair of the OTA is modified to employ a self cascode flipped voltage follower (SCFVF), which eventually reduces power consumption of the NA to 0.822 μ W. Additionally, notable improvements in noise performance are achieved and found to be 624.9 nV/ (H z) at 1 Hz. The gain and bandwidth range of the input low-noise amplifier is 54.2 dB and 2.8 Hz–207 Hz, respectively, which effectively amplifies low-amplitude noisy incoming signals, addressing the specific requirements of EEG signal acquisition. [ABSTRACT FROM AUTHOR]

As the automotive industry undergoes a phase of rapid transformation driven by technological advancements, the integration of driving simulators stands out as an important tool for research and development. The usage of such simulators offers a controlled environment for studying driver behavior; the alignment of data, however, remains a complex aspect that warrants a thorough investigation. This research investigates driver state classification using a dataset obtained from real-road and simulated conditions, recorded through JINS MEME ES_R smart glasses. The dataset encompasses electrooculography signals, with a focus on standardizing and processing the data for subsequent analysis. For this purpose, we used a recurrent neural network model, which yielded a high accuracy on the testing dataset (86.5%). The findings of this study indicate that the proposed methodology could be used in real scenarios and that it could be used for the development of intelligent transportation systems and driver monitoring technology. [ABSTRACT FROM AUTHOR]

The Electrocardiogram (ECG) records are crucial for predicting heart diseases and evaluating patient's health conditions. ECG signals provide essential peak values that reflect reliable health information. Analyzing ECG signals is a fundamental technique for computerized prediction with advancements in Very Large-Scale Integration (VLSI) technology and significantly impacts in biomedical signal processing. VLSI advancements focus on high-speed circuit functionality while minimizing power consumption and area occupancy. In ECG signal denoising, digital filters like Infinite Impulse Response (IIR) and Finite Impulse Response (FIR) are commonly used. The FIR filters are preferred for their higher-order performance and stability over IIR filters, especially in real-time applications. The Modified FIR (MFIR) blocks were reconstructed using the optimized adder-multiplier block for better noise reduction performance. The MIT-BIT database is used as reference where the noises are filtered by the MFIR based on Optimized Kogge Stone Adder (OKSA). Features are extracted and analyzed using Discrete wavelet transform (DWT) and Cross Correlation (CC). At this modern era, Hybrid methods of Machine Learning (HMLM) methods are preferred because of their combined performance which is better than non-fused methods. The accuracy of the Hybrid Neural Network (HNN) model reached 92.3%, surpassing other models such as Generalized Sequential Neural Networks (GSNN), Artificial Neural Networks (ANN), Support Vector Machine with linear kernel (SVM linear), and Support Vector Machine with Radial Basis Function kernel (SVM RBF) by margins of 3.3%, 5.3%, 23.3%, and 24.3%, respectively. While the precision of the HNN is 91.1%, it was slightly lower than GSNN and ANN but higher than both SVM linear and SVM -RBF. The HNN with various features are incorporated to improve the ECG classification. The accuracy of the HNN is switched to 95.99% when the DWT and CC are combined. Also, it improvises other parameters such as precision 93.88%, recall is 0.94, F1 score is 0.88, Kappa is 0.89, kurtosis is 1.54, skewness is 1.52 and error rate 0.076. These parameters are higher than recently developed models whose algorithms and methods accuracy is more than 90%. [ABSTRACT FROM AUTHOR]

Remote patient-monitoring systems are helpful since they can provide timely and effective healthcare facilities. Such online telemedicine is usually achieved with the help of sophisticated and advanced wearable sensor technologies. The modern type of wearable connected devices enable the monitoring of vital sign parameters such as: heart rate variability (HRV) also known as electrocardiogram (ECG), blood pressure (BLP), Respiratory rate and body temperature, blood pressure (BLP), respiratory rate, and body temperature. The ubiquitous problem of wearable devices is their power demand for signal transmission; such devices require frequent battery charging, which causes serious limitations to the continuous monitoring of vital data. To overcome this, the current study provides a primary report on collecting kinetic energy from daily human activities for monitoring vital human signs. The harvested energy is used to sustain the battery autonomy of wearable devices, which allows for a longer monitoring time of vital data. This study proposes a novel type of stress- or exercise-monitoring ECG device based on a microcontroller (PIC18F4550) and a Wi-Fi device (ESP8266), which is cost-effective and enables real-time monitoring of heart rate in the cloud during normal daily activities. In order to achieve both portability and maximum power, the harvester has a small structure and low friction. Neodymium magnets were chosen for their high magnetic strength, versatility, and compact size. Due to the non-linear magnetic force interaction of the magnets, the non-linear part of the dynamic equation has an inverse quadratic form. Electromechanical damping is considered in this study, and the quadratic non-linearity is approximated using MacLaurin expansion, which enables us to find the law of motion for general case studies using classical methods for dynamic equations and the suitable parameters for the harvester. The oscillations are enabled by applying an initial force, and there is a loss of energy due to the electromechanical damping. A typical numerical application is computed with Matlab 2015 software, and an ODE45 solver is used to verify the accuracy of the method. [ABSTRACT FROM AUTHOR]

This study aims to demonstrate the feasibility of using a new wireless electroencephalography (EEG)–electromyography (EMG) wearable approach to generate characteristic EEG-EMG mixed patterns with mouth movements in order to detect distinct movement patterns for severe speech impairments. This paper describes a method for detecting mouth movement based on a new signal processing technology suitable for sensor integration and machine learning applications. This paper examines the relationship between the mouth motion and the brainwave in an effort to develop nonverbal interfacing for people who have lost the ability to communicate, such as people with paralysis. A set of experiments were conducted to assess the efficacy of the proposed method for feature selection. It was determined that the classification of mouth movements was meaningful. EEG-EMG signals were also collected during silent mouthing of phonemes. A few-shot neural network was trained to classify the phonemes from the EEG-EMG signals, yielding classification accuracy of 95%. This technique in data collection and processing bioelectrical signals for phoneme recognition proves a promising avenue for future communication aids. [ABSTRACT FROM AUTHOR]

Epilepsy affects millions worldwide, making timely seizure detection crucial for effective treatment and enhanced well-being. Electroencephalogram (EEG) analysis offers a non-intrusive solution, but its visual interpretation is prone to errors and requires a lot of time. Many existing works focus solely on achieving competitive levels of accuracy without considering processing speed or the computational complexity of their models. This study aimed to develop an automated technique for identifying epileptic seizures in EEG data through analysis methods. The efforts have been primarily focused on achieving high accuracy results by operating exclusively within a narrow frequency band of the signal, while also aiming to minimize computational complexity. In this article, a new automated approach is presented for seizure detection by combining signal processing and machine learning techniques. The proposed method comprises four stages: (1) Preprocessing: Savitzky–Golay filter to remove the background noise. (2) Decomposition: discrete wavelet transform (DWT) to extract spontaneous alpha and beta frequency bands. (3) Feature extraction: six features (mean, standard deviation, skewness, kurtosis, energy, and entropy) are computed for each frequency band. (4) Classification: a support vector machine (SVM) method classifies signals as normal or containing a seizure. The method was assessed using two publicly available EEG datasets. For the alpha band, the highest achieved accuracy was 92.82%, and for the beta band it was 90.55%, which demonstrates adequate capability in both bands for accurate seizure detection. Furthermore, the obtained low computational cost suggests a potentially valuable application in real-time assessment scenarios. The obtained results indicate its capacity as a valuable instrument for diagnosing epilepsy and monitoring patients. Further research is necessary for clinical validation and potential real-time deployment. [ABSTRACT FROM AUTHOR]

Respiratory rate (RR) is a vital indicator for assessing the bodily functions and health status of patients. RR is a prominent parameter in the field of biomedical signal processing and is strongly associated with other vital signs such as blood pressure, heart rate, and heart rate variability. Various physiological signals, such as photoplethysmogram (PPG) signals, are used to extract respiratory information. RR is also estimated by detecting peak patterns and cycles in the signals through signal processing and deep-learning approaches. In this study, we propose an end-to-end RR estimation approach based on a third-generation artificial neural network model—spiking neural network. The proposed model employs PPG segments as inputs, and directly converts them into sequential spike events. This design aims to reduce information loss during the conversion of the input data into spike events. In addition, we use feedback-based integrate-and-fire neurons as the activation functions, which effectively transmit temporal information. The network is evaluated using the BIDMC respiratory dataset with three different window sizes (16, 32, and 64 s). The proposed model achieves mean absolute errors of 1.37 ± 0.04, 1.23 ± 0.03, and 1.15 ± 0.07 for the 16, 32, and 64 s window sizes, respectively. Furthermore, it demonstrates superior energy efficiency compared with other deep learning models. This study demonstrates the potential of the spiking neural networks for RR monitoring, offering a novel approach for RR estimation from the PPG signal. [ABSTRACT FROM AUTHOR]

Our skin reacts to various stimuli that we receive. Since all parts of the human body are controlled by the brain, a relationship should exist among brain and skin activities. This study evaluates the relation among skin and brain activities. As such, we benefited from the information-based analysis. We collected GSR and EEG signals of eight participants in various olfactory stimulations. Accordingly, we ran Shannon entropy-based analysis to evaluate the correlation between the information contents of these two signals. The results showed that the alterations in the complexity of stimulus and the information of GSR and EEG signals are strongly correlated. We also verified the results of the analysis of Shannon entropy of signals by calculating their Hurst exponent to quantify their memory. According to the results, the alterations of the memory of EEG and GSR signals are similar to the alterations of the information of these signals. [ABSTRACT FROM AUTHOR]

Emotion recognition using physiological signals has gained significant attention in recent years due to its potential applications in various domains, such as healthcare and entertainment. EEG signals have been particularly useful in emotion recognition due to their non-invasive nature and high temporal resolution. However, the development of accurate and efficient algorithms for emotion classification using EEG signals remains a challenging task. This paper proposes a novel hybrid algorithm for emotion classification based on EEG signals, which combines multiple adaptive network models and probabilistic neural networks. The research aims to improve the recognition accuracy of three and four emotions, which has been a challenge for existing approaches. The proposed model consists of N adaptively neuro-fuzzy inference system (ANFIS) classifiers designed in parallel, in which N is the number of emotion classes. The selected features with the most appropriate distribution for classification are given as input vectors to the ANFIS structures, and the system is trained. The outputs of these trained ANFIS models are combined to create a feature vector, which provides the inputs for adaptive networks, and the system is trained to acquire the emotional recognition output. The performance of the proposed model has been evaluated for classification on well-known emotion benchmark datasets, including DEAP and Feeling Emotions. The study results indicate that the model achieves an accuracy rate of 73.49% on the DEAP datasets and 95.97% on the Feeling Emotions datasets. These results demonstrate that the proposed model efficiently recognizes emotions and exhibits a promising classification performance. [ABSTRACT FROM AUTHOR]

In the realm of medical education, research, and device testing, Electrocardiogram (ECG) simulators are indispensable tools. They provide authentic representations of cardiac electrical activity, aiding healthcare professionals in practical training and facilitating the assessment of ECG device efficacy. This paper presents an efficient ECG simulator capable of replicating synthetic ECG waves, which are combinations of individual waves namely, the P wave, QRS complex, and T wave. The simulator can generate both normal and abnormal ECG waves and offers the flexibility to produce ECG waves at different frequencies. [ABSTRACT FROM AUTHOR]

IntroductionMulti-channel electrophysiology systems for recording of neuronal activity face significant data throughput limitations, hampering real-time, data-informed experiments. These limitations impact both experimental neurobiology research and next-generation neuroprosthetics.MethodsWe present a novel solution that leverages the high integration density of 22nm fully-depleted silicon-on-insulator technology to address these challenges. The proposed highly integrated programmable System-on-Chip (SoC) comprises 68-channel 0.41 μW/Ch recording frontends, spike detectors, 16-channel 0.87–4.39 μW/Ch action potentials and 8-channel 0.32 μW/Ch local field potential codecs, as well as a multiply-accumulate-assisted power-efficient processor operating at 25 MHz (5.19 μW/MHz). The system supports on-chip training processes for compression, training, and inference for neural spike sorting. The spike sorting achieves an average accuracy of 91.48 or 94.12% depending on the utilized features. The proposed programmable SoC is optimized for reduced area (9 mm2) and power. On-chip processing and compression capabilities free up the data bottlenecks in data transmission (up to 91% space saving ratio), and moreover enable a fully autonomous yet flexible processor-driven operation.DiscussionCombined, these design considerations overcome data-bottlenecks by allowing on-chip feature extraction and subsequent compression.

Children with type 1 diabetes (T1D) frequently have nocturnal hypoglycemia, daytime physical activity being the most important risk factor. The risk for late post-exercise hypoglycemia depends on various factors and is difficult to anticipate. The availability of continuous glucose monitoring (CGM) enabled the development of various machine learning approaches for nocturnal hypoglycemia prediction for different prediction horizons. Studies focusing on nocturnal hypoglycemia prediction in children are scarce, and none, to the best knowledge of the authors, investigate the effect of previous physical activity. The primary objective of this work was to assess the risk of hypoglycemia throughout the night (prediction horizon 9 h) associated with physical activity in children with T1D using data from a structured setting. Continuous glucose and physiological data from a sports day camp for children with T1D were input for logistic regression, random forest, and deep neural network models. Results were evaluated using the F2 score, adding more weight to misclassifications as false negatives. Data of 13 children (4 female, mean age 11.3 years) were analyzed. Nocturnal hypoglycemia occurred in 18 of a total included 66 nights. Random forest using only glucose data achieved a sensitivity of 71.1% and a specificity of 75.8% for nocturnal hypoglycemia prediction. Predicting the risk of nocturnal hypoglycemia for the upcoming night at bedtime is clinically highly relevant, as it allows appropriate actions to be taken—to lighten the burden for children with T1D and their families.

Curative effects of electromyography (EMG) feedback in treatment of various conditions and/or recovery after injuries have been earlier reported. However, wider application on EMG feedback is somehow limited due to the overall price of such systems and limited availability outside of the specialized treatment centers. Development of a personalized device for EMG feedback would be of great importance for home recovery after stroke or injuries, achieving better success in fitness and improving biofeedback-based treatments of conditions such as urinary incontinence. Despite extensive research on EMG signal collection, there is a lack of focus on in-situ EMG analysis that considers the intensity and duration of muscle activities. This gap presents the motivation for our research. In this paper, we present a methodology for the realization of wearable, rechargeable battery-powered, small-sized (90 mm × 60 mm) electronic device for recording two EMG channels (12-bits resolution, sampling frequency up to 1.6 kHz) with Bluetooth Low Energy connectivity to a smartphone. An average current consumption of 20.5 mA was experimentally determined, suggesting that multiday continuous functionality is possible. Advancing the state of the art, we propose a cross-correlation-based algorithm for in-situ dynamical computing and evaluation of muscle activation levels. This algorithm can determine if the muscle follows a predefined profile of contractions/relaxations (as needed for the treatment) and indicate if two muscles in a specific exercise were or were not engaged in proper time and with the proper intensity. The performed simulation analysis showed that the proposed approach exhibited shorter processing time compared to Morlet Wavelet Transform and Dynamic Time Warping. Finally, experimental work with five human volunteers demonstrated the reliability in EMG acquisition and signal processing. Therefore, the main contribution of this work is a cost-effective, small-sized, customizable EMG recording system with an efficient cross-correlation-based algorithm for in-situ EMG collection and processing.

Recently, Electrocardiography (ECG) signals are commonly used in diagnosing the cardiac arrhythmia that shows up with the loss of the regular movement of the heart. Approximately 5% of the world population have cardio motor disorders. Therefore, usage of the ECG signals in biomedical signal processing algorithms and machine learning methods for automated diagnosis of this widespread health problem is a popular research topic. In this paper, the Particle Swarm Optimization (PSO) technique is implemented to tune the parameters of Tunable Q-Factor Wavelet Transform (TQWT) and the new generation feature generator Hamsi Hash Function (Hamsi-Pat) is used to obtain the characteristics of the signal. Sub-signals of 10 s obtained from the original ECG signal are divided into their sub-bands of 25 levels with PSO and TQWT. Each of these low pass filters generates 536 dimensional features by applying Hamsi-Pat and statistical methods. Then, all these features are combined and 536 × 25 = 13400-dimensional feature set is obtained. The features in the set are reduced and the best of them are selected by using the Iterative Neighborhood Component Analysis (INCA) method. Finally, the k-Nearest Neighbors (kNN) classification method is applied to the best features according to the City Block measurement criterion. All studies cited to compare the results in this paper also use the MIT-BIH Arrhythmia ECG database. Hence, the difference could be observed in the used techniques. In contrast to the existing studies, this study shows its superior performance by classifying all 17 classes simultaneously by applying a "fused" approach. The method in the paper reached 98.5% classification accuracy on the 17 classes of the MIT-BIH Arrhythmia ECG database. The results indicate that the proposed method showed better rates from the existing studies related to arrhythmia diagnosis using ECG signals in the literature. [ABSTRACT FROM AUTHOR]

Emotion recognition plays an essential role in human–human interaction since it is a key to understanding the emotional states and reactions of human beings when they are subject to events and engagements in everyday life. Moving towards human–computer interaction, the study of emotions becomes fundamental because it is at the basis of the design of advanced systems to support a broad spectrum of application areas, including forensic, rehabilitative, educational, and many others. An effective method for discriminating emotions is based on ElectroEncephaloGraphy (EEG) data analysis, which is used as input for classification systems. Collecting brain signals on several channels and for a wide range of emotions produces cumbersome datasets that are hard to manage, transmit, and use in varied applications. In this context, the paper introduces the Empátheia system, which explores a different EEG representation by encoding EEG signals into images prior to their classification. In particular, the proposed system extracts spatio-temporal image encodings, or atlases, from EEG data through the Processing and transfeR of Interaction States and Mappings through Image-based eNcoding (PRISMIN) framework, thus obtaining a compact representation of the input signals. The atlases are then classified through the Empátheia architecture, which comprises branches based on convolutional, recurrent, and transformer models designed and tuned to capture the spatial and temporal aspects of emotions. Extensive experiments were conducted on the Shanghai Jiao Tong University (SJTU) Emotion EEG Dataset (SEED) public dataset, where the proposed system significantly reduced its size while retaining high performance. The results obtained highlight the effectiveness of the proposed approach and suggest new avenues for data representation in emotion recognition from EEG signals. [ABSTRACT FROM AUTHOR]

Summary: In this paper, a low‐power transconductor‐capacitor (Gm‐C) filter with programmable cutoff frequency is designed for electroencephalogram (EEG), electrocardiogram (ECG), and surface electromyography (sEMG) biosignals. The second‐order Butterworth low pass filter (LPF) is designed as an interface between an instrumentation amplifier (IA) and an analog‐to‐digital converter (ADC). The proposed Gm‐C filter suppresses out‐of‐band components and also plays an anti‐aliasing role. With the variable transconductance based on flipped voltage follower (FVF), the cutoff frequencies of 100 Hz, 560 Hz, and 1.5 kHz are obtained for EEG, ECG, and sEMG, respectively. This structure is highly accurate by using a robust and low‐power common mode feedback (CMFB). With 0.8 V supply voltage, the total power consumption of the filter is only 56 nW, 100 nW, and 221 nW for cutoff frequencies of 100 Hz, 560 Hz, and 1.5 kHz, respectively. The proposed Gm‐C filter is simulated in TSMC 65 nm CMOS technology and occupies an area of 0.12 mm2. The figure of merit (FOM) of the proposed filter is equal to 1.9 pW/pole. [ABSTRACT FROM AUTHOR]

We proposed mathematical models for the electrocardiogram and photoplethysmogram signals with functionality to preset the pattern of synchronization between the phases of the low-frequency oscillations, which are related to the sympathetic neuronal control of circulation. The simulated phase difference reproduced the statistical and spectral characteristics of the experimental data, including the alternating horizontal and sloped sections, corresponding to the intervals of synchronous and asynchronous behavior. Using the proposed model, we tested and tuned an algorithm for detection of the phase synchronization between the parts of the sympathetic control of circulation, improving both sensitivity and specificity of the algorithm. [ABSTRACT FROM AUTHOR]

Frequency-selective filters are usually required at the receiving end of a communication system to change the frequency range of a band-pass filter (BPF), which is used to extract the broadcast signal. In this study, we have designed such frequency-selective filters using a novel bulk-driven current differential transconductance amplifier (BDCDTA) as its fundamental building block. This BDCDTA is first used to create a biquad filter, and the designed filter is then reconfigured for frequency selection. The proposed frequency-selective filter structures have the capability of providing a constant gain based on the communication system's requirements. The suggested structures with CMOS 180 nm technology parameters are validated using PSPICE and the Cadence Virtuoso design environment. The theory is confirmed by pre and post-layout simulation. PVT corner and Monte-Carlo analysis demonstrate its applicability for a broad range of applications and provide a range for its use under various conditions. This design is suitable for low voltage and low power applications due to the bulk-driven transistors, and the suggested structures are designed especially for medium wave communication and biomedical signal processing. [ABSTRACT FROM AUTHOR]

The relevance of the interaction between Humanities-enhanced learning using immersive environments and simultaneous physiological signal analysis contributes to the development of Neurohumanities and advancements in applications of Digital Humanities. The present dataset consists of recordings from 24 participants divided in two groups (12 participants in each group) engaging in simulated learning scenarios, traditional learning, and partially immersive learning experiences. Data recordings from each participant contain recordings of physiological signals and psychometric data collected from applied questionnaires. Physiological signals include electroencephalography, real-time engagement and emotion recognition calculation by a Python EEG acquisition code, head acceleration, electrodermal activity, blood volume pressure, inter-beat interval, and temperature. Before the acquisition of physiological signals, participants were asked to fill out the General Health Questionnaire and Trait Meta-Mood Scale. In between recording sessions, participants were asked to fill out Likert-scale questionnaires regarding their experience and a Self-Assessment Manikin. At the end of the recording session, participants filled out the ITC Sense of Presence Inventory questionnaire for user experience. The dataset can be used to explore differences in physiological patterns observed between different learning modalities in the Humanities. Dataset: The data generated from this experimentation are available for download on Figshare https://doi.org/10.6084/m9.figshare.24777084 , accessed on 28 January 2024. Dataset License: The dataset is made available under the Creative Commons 4.0 (CC BY) license. [ABSTRACT FROM AUTHOR]

Preterm neonates are at risk of long-term neurodevelopmental impairments due to disruption of natural brain development. Electroencephalography (EEG) analysis can provide insights into brain development of preterm neonates. This study aims to explore the use of microstate (MS) analysis to evaluate global brain dynamics changes during maturation in preterm neonates with normal neurodevelopmental outcome. The dataset included 135 EEGs obtained from 48 neonates at varying postmenstrual ages (26.4 to 47.7 weeks), divided into four age groups. For each recording we extracted a 5-minute epoch during quiet sleep (QS) and during non-quiet sleep (NQS), resulting in eight groups (4 age group x 2 sleep states). We compared MS maps and corresponding (map-specific) MS metrics across groups using group-level maps. Additionally, we investigated individual map metrics. Four group-level MS maps accounted for approximately 70% of the global variance and showed non-random syntax. MS topographies and transitions changed significantly when neonates reached 37 weeks. For both sleep states and all MS maps, MS duration decreased and occurrence increased with age. The same relationships were found using individual maps, showing strong correlations (Pearson coefficients up to 0.74) between individual map metrics and post-menstrual age. Moreover, the Hurst exponent of the individual MS sequence decreased with age. The observed changes in MS metrics with age might reflect the development of the preterm brain, which is characterized by formation of neural networks. Therefore, MS analysis is a promising tool for monitoring preterm neonatal brain maturation, while our study can serve as a valuable reference for investigating EEGs of neonates with abnormal neurodevelopmental outcomes. [ABSTRACT FROM AUTHOR]

Cardiovascular diseases remain the leading cause of global mortality, resulting in the loss of 17.9 million lives annually, as reported by the World Health Organization (WHO). This study focuses on the classification of human heart-related sounds into normal or pathological categories. The PhysioNet Computing in Cardiology (CinC) 2016 and 2022 reference datasets, also known as PhysioNet 2016 and PhysioNet 2022 respectively, have been employed to examine the technique suggested in this research work. These benchmark datasets are comprised of 3,200 and 3,168 Phonocardiogram (PCG) recordings, respectively. In the current research, Mel spectrograms hold special significance in reducing the dimensions of a raw audio signal without causing any loss of important data, thus making it more manageable for processing. The work proposes a classification system based on the UNet architecture, which processes transformed spectrograms of the PCG signals. The augmented spectrograms have yielded the best results. Specifically, on the PhysioNet 2016 dataset, the proposed model has achieved an accuracy of 96.05%, specificity of 98.82%, and F1 score as 0.91. As the focus of this study has been to develop a novel architecture for classification and not data cleaning, the model has attained an accuracy of 56.80%, specificity of 59.29%, with F1 score as 0.58 on the PhysioNet 2022 dataset which is perceived to be a noisy dataset. [ABSTRACT FROM AUTHOR]

This study explores the important role of assessing force levels in accurately controlling upper limb movements in human–computer interfaces. It uses a new method that combines entropy to improve the recognition of force levels. This research aims to differentiate between different levels of isometric contraction forces using electroencephalogram (EEG) signal analysis. It integrates eight different entropy measures: power spectrum entropy (PSE), singular spectrum entropy (SSE), logarithmic energy entropy (LEE), approximation entropy (AE), sample entropy (SE), fuzzy entropy (FE), alignment entropy (PE), and envelope entropy (EE). The findings emphasize two important advances: first, including a wide range of entropy features significantly improves classification efficiency; second, the fusion entropy method shows exceptional accuracy in classifying isometric contraction forces. It achieves an accuracy rate of 91.73% in distinguishing between 15% and 60% maximum voluntary contraction (MVC) forces, along with 69.59% accuracy in identifying variations across 15%, 30%, 45%, and 60% MVC. These results illuminate the efficacy of employing fusion entropy in EEG signal analysis for isometric contraction detection, heralding new opportunities for advancing motor control and facilitating fine motor movements through sophisticated human–computer interface technologies. [ABSTRACT FROM AUTHOR]

The design of hand‐held portable devices for cardiovascular health monitoring based on the analysis of electrocardiogram (ECG) is a hot topic of research nowadays. Digital filters, possibly designed using digital multipliers, are a critical module in the design of such analysis systems. This paper presents the ASIC design of a 64th‐order digital low‐pass FIR filter to be used in ECG denoising applications. The FIR filter is optimized for usage in battery‐operated portable systems, wherein power consumption and on‐chip area are crucial parameters of concern. These challenges are met by employing a novel multiplier architecture, which we design by hybridizing the two popular digital multiplication algorithms, namely, the Vedic multiplication algorithm and the Wallace tree multiplication algorithm. The proposed multiplier consumes lower area and power consumption than either of the aforementioned conventional multiplication algorithms. This is established through FPGA‐based implementations on Artix‐7 FPGA, for the various wordlengths. The area and power optimizations are further exercised by two proposed techniques, namely, elimination of redundant multipliers, and power gating using the proposed zero‐detector circuit. RTL‐to‐GDSII flow has been completed using Cadence digital design and sign‐off tools for SCL 180 nm technology. The results indicate that the proposed filter architecture occupies 16.38% lower area and 79.58% lower power consumption than the contemporary designs described in the literature. [ABSTRACT FROM AUTHOR]

This paper addresses the enhancement of modern security through the integration of electrocardiograms (ECGs) into biometric authentication systems. As technology advances, the demand for reliable identity authentication systems has grown, given the rise in breaches associated with traditional techniques that rely on unique biological and behavioral traits. These techniques are emerging as more reliable alternatives. Among the biological features used for authentication, ECGs offer unique advantages, including resistance to forgery, real-time detection, and continuous identification ability. A key contribution of this work is the introduction of a variant of the ECG time-slicing technique that outperforms existing ECG-based authentication methods. By leveraging machine learning algorithms and tailor-made compact data learning techniques, this research presents a more robust, reliable biometric authentication system. The findings could lead to significant advancements in network information security, with potential applications across various internet and mobile services. [ABSTRACT FROM AUTHOR]

Significance: Accurate spatial registration of probes (e.g., optodes and electrodes) for measurement of brain activity is a crucial aspect in many neuroimaging modalities. It may increase measurement precision and enable the transition from channel-based calculations to volumetric representations. Aim: This technical note evaluates the efficacy of a commercially available infrared three-dimensional (3D) scanner under actual experimental (or clinical) conditions and provides guidelines for its use. Method: We registered probe positions using an infrared 3D scanner and validated them against magnetic resonance imaging (MRI) scans on five volunteer participants. Results: Our analysis showed that with standard cap fixation, the average Euclidean distance of probe position among subjects could reach up to 43 mm, with an average distance of 15.25 mm [standard deviation (SD) = 8.0]. By contrast, the average distance between the infrared 3D scanner and the MRI-acquired positions was 5.69 mm (SD = 1.73), while the average difference between consecutive infrared 3D scans was 3.43 mm (SD = 1.62). The inter-optode distance, which was fixed at 30 mm, was measured as 29.28 mm (SD = 1.12) on the MRI and 29.43 mm (SD = 1.96) on infrared 3D scans. Our results demonstrate the high accuracy and reproducibility of the proposed spatial registration method, making it suitable for both functional near-infrared spectroscopy and electroencephalogram studies. Conclusions: The 3D infrared scanning technique for spatial registration of probes provides economic efficiency, simplicity, practicality, repeatability, and high accuracy, with potential benefits for a range of neuroimaging applications. We provide practical guidance on anonymization, labeling, and post-processing of acquired scans. [ABSTRACT FROM AUTHOR]

Circuit-integrated electromagnets are fundamental building blocks for on-chip signal transduction, modulation, and tunability, with specific applications in environmental and biomedical micromagnetometry. A primary challenge for improving performance is pushing quality limitations while minimizing size and fabrication complexity and retaining spatial capabilities. Recent efforts have exploited highly involved three-dimensional synthesis, advanced insulation, and exotic material compositions. Here, we present a rapid nanofabrication process that employs electron beam dose control for high-turn-density diamond-embedded flat spiral coils; these coils achieve efficient on-chip electromagnetic-to-optical signal conversion. Our fabrication process relies on fast 12.3 s direct writing on standard poly(methyl methacrylate) as a basis for the metal lift-off process. Prototypes with 70 micrometer overall diameters and 49–470 nm interturn spacings with corresponding inductances of 12.3–12.8 nH are developed. We utilize optical micromagnetometry to demonstrate that magnetic field generation at the center of the structure effectively correlates with finite element modeling predictions. Further designs based on our process can be integrated with photolithography to broadly enable optical magnetic sensing and spin-based computation. [ABSTRACT FROM AUTHOR]

This editorial discusses the need for innovative healthcare solutions to meet the complex needs of the growing elderly population. It emphasizes the importance of emerging technologies such as artificial intelligence, immersive technologies, and the Internet of Things in the healthcare sector. The document provides a summary of research papers submitted to the topic, including studies on video-based ambient assisted living technologies and evaluating individuals' processing speed. The research papers explore advancements in cross-disciplinary studies in fields such as ambient assistive technology, immersive technology, artificial intelligence, machine learning, deep learning, signal processing, healthcare solutions, and medicine. The papers contribute to the understanding of cognitive processing, motor functioning, and brain rehabilitation, and highlight the potential of artificial intelligence-driven applications in healthcare. [Extracted from the article]

Algorithms for QRS detection are fundamental in the ECG interpretive processing chain. They must meet several challenges, such as high reliability, high temporal accuracy, high immunity to noise, and low computational complexity. Unfortunately, the accuracy expressed by missed or redundant events statistics is often the only parameter used to evaluate the detector's performance. In this paper, we first notice that statistics of true positive detections rely on researchers' arbitrary selection of time tolerance between QRS detector output and the database reference. Next, we propose a multidimensional algorithm evaluation method and present its use on four example QRS detectors. The dimensions are (a) influence of detection temporal tolerance, tested for values between 8.33 and 164 ms; (b) noise immunity, tested with an ECG signal with an added muscular noise pattern and signal-to-noise ratio to the effect of "no added noise", 15, 7, 3 dB; and (c) influence of QRS morphology, tested on the six most frequently represented morphology types in the MIT-BIH Arrhythmia Database. The multidimensional evaluation, as proposed in this paper, allows an in-depth comparison of QRS detection algorithms removing the limitations of existing one-dimensional methods. The method enables the assessment of the QRS detection algorithms according to the medical device application area and corresponding requirements of temporal accuracy, immunity to noise, and QRS morphology types. The analysis shows also that, for some algorithms, adding muscular noise to the ECG signal improves algorithm accuracy results. [ABSTRACT FROM AUTHOR]

Brushstroke segmentation algorithms are critical in computer-based analysis of fine motor control via handwriting, drawing, or tracing tasks. Current segmentation approaches typically rely only on one type of feature, either spatial, temporal, kinematic, or pressure. We introduce a segmentation algorithm that leverages both spatiotemporal and pressure features to accurately identify brushstrokes during a tracing task. The algorithm was tested on both a clinical and validation dataset. Using validation trials with incorrectly identified brushstrokes, we evaluated the impact of segmentation errors on commonly derived biomechanical features used in the literature to detect graphomotor pathologies. The algorithm exhibited robust performance on validation and clinical datasets, effectively identifying brushstrokes while simultaneously eliminating spurious, noisy data. Spatial and temporal features were most affected by incorrect segmentation, particularly those related to the distance between brushstrokes and in-air time, which experienced propagated errors of 99% and 95%, respectively. In contrast, kinematic features, such as velocity and acceleration, were minimally affected, with propagated errors between 0 to 12%. The proposed algorithm may help improve brushstroke segmentation in future studies of handwriting, drawing, or tracing tasks. Spatial and temporal features derived from tablet-acquired data should be considered with caution, given their sensitivity to segmentation errors and instrumentation characteristics. [ABSTRACT FROM AUTHOR]