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This paper presents a low-power, second-order composite source-follower-based filter architecture optimized for biomedical signal processing, particularly ECG and EEG applications. Source-follower-based filters are recommended in the literature for high-frequency applications due to their lower power consumption when compared to filters with alternative topologies. However, they are not suitable for biomedical applications requiring low cutoff frequencies as they are designed to operate in the saturation region. The major contribution in this work are the filter is made to operate in the weak inversion zone to reduce the area needed for the capacitor and the amount of power dissipated. Process variation is one of the major issues in the weak inversion regime. To overcome this, a unique method of compensating against fluctuations in process, voltage, and temperature is put forth based on magnitude comparison is another contribution. Key findings from post-layout simulations and experimental measurements demonstrate that the filter achieves a tunable cutoff frequency range of 0.5 Hz to 150 Hz, with a total power dissipation of only 6nW at 150 Hz. The design occupies a compact silicon area of 0.065 mm2 and offers a dynamic range of 75 dB. The measured results indicate that for a 300 mVpp signal swing, the top bound on THD is -40 dB. The filter's robustness against process, voltage, and temperature variations is validated through on-chip tuning using a current steering DAC, ensuring stable performance across different operating conditions. These results make the proposed filter a promising candidate for low-power biomedical devices. The recommended filter is developed and implemented using UMC-0.18μm CMOS technology with a 1.0V supply, and the IC is tapped out using an MPW run of Euro practice IC services. [ABSTRACT FROM AUTHOR]

This paper presents a preliminary study delving into the application of machine learning-based methods for optimizing parameter selection in filtering techniques. The authors focus on exploring the efficacy of two prominent filtering methods: smoothing and cascade filters, known for their profound impact on enhancing the quality of brain signals. The study specifically examines signals acquired through functional near-infrared spectroscopy (fNIRS), a noninvasive neuroimaging modality offering valuable insights into brain activity. Through meticulous analysis, the research underscores the potential of machine learning approaches in discerning optimal parameters for filtering, thereby leading to a significant enhancement in the quality and reliability of fNIRS-derived signals. The results demonstrate the effectiveness of machine learning-based methods in optimizing parameter selection for filtering techniques, particularly in the context of fNIRS signals. By leveraging these approaches, the study achieves notable improvements in the quality and reliability of brain signal data. This work sheds light on promising avenues for refining neuroimaging methodologies and advancing the field of signal processing in neuroscience. The successful application of machine learning-based techniques highlights their potential for optimizing neuroimaging data processing, ultimately contributing to a deeper understanding of brain function. [ABSTRACT FROM AUTHOR]

Electroencephalography (EEG) can reflect changes in brain activity under different states. The electrical signals of the brain are observed to exhibit varying amplitudes and frequencies. These variations are closely linked to different states of consciousness, influencing the internal and external behaviors, emotions, and learning performance of humans. The assessment of personal level of attention, which refers to the ability to consciously focus on something, can also be facilitated by these signals. Research on brain attention aids in the understanding of the mechanisms underlying human cognition and behavior. Based on the characteristics of EEG signals, this research identifies the most effective method for detecting brain attention by adapting various preprocessing and machine learning techniques. The results of our analysis on a publicly available dataset indicate that KNN with the feature importance feature extraction method performed the best, achieving 99.56% accuracy, 99.67% recall, and 99.44% precision with a rapid training time. [ABSTRACT FROM AUTHOR]

The growing field of smart textiles has captivated researchers, focusing on advancing functionalities to enhance human well-being and elevate daily comfort. Wearable sensors, integral to healthcare, hold immense promise for real-time biomedical monitoring, presenting a transformative potential for disease management and enhanced patient outcomes. Within this domain, textile-based wearable sensors have emerged as a particularly promising technology, boasting advantages such as comfort, flexibility, and noninvasiveness. This article provides a meticulous overview of the design and development of textile-based wearable sensors for real-time biomedical monitoring. A comprehensive literature review explores existing wearable sensor technologies, emphasizing the advantages and limitations specific to textile-based sensors. The discussion encompasses considerations for sensor design, selection, and integration into wearable systems, delving into the evaluation of various sensor modalities, textile materials, and fabrication techniques. Signal processing techniques, essential for extracting pertinent biomedical information, and data analysis methods for real-time monitoring are scrutinized. Biocompatibility, comfort, and user acceptance factors are conscientiously considered, alongside thorough discussions on calibration procedures and accuracy assessment methods to ensure the reliability of measurements. The article further explores potential applications of textile-based sensors in real-time biomedical monitoring, encompassing vital signs monitoring, activity tracking, and disease detection. Human factors and user studies are critically examined to comprehend user acceptance, informing design improvements tailored to user needs. Lastly, the article discusses future research directions and challenges, including considerations for durability, washability, and scalability. This comprehensive review aspires to equip researchers and practitioners with invaluable insights into the nuanced realm of textile-based wearable sensors for real-time biomedical monitoring. By fostering advancements in the field, this review aims to facilitate the seamless translation of this cutting-edge technology into clinical practice. Graphical abstract of Textile-Based Wearable Sensors for Biomedical. [ABSTRACT FROM AUTHOR]

Power spectral density is a crucial tool in the field of signal processing, mainly in biomedical signal processing. Power spectral density is also one of the most widely used tools in real-time applications. Therefore, further research should be prioritized in the hardware implementation of power spectral density. In this paper, two techniques are introduced in the implementation of power spectral density, mainly focusing on the Fourier transform block. The approaches introduced are the adoption of the Coordinate Rotation Digital Computer algorithm-based fast Fourier transform and the Coordinate Rotation Digital Computer algorithm-based sliding discrete Fourier transform. The other blocks in modified Welch’s architecture are also enhanced using pipelining and approximate distributed arithmetic methods. The introduction of all these techniques has led to an improvement in power and area. There is almost a 36% decrease in the number of lookup tables when compared to the existing methodology. With regards to power, there is almost 44% and 16% decrease in these two architectures, respectively. The overall architectures were synthesized using Xilinx Vivado 19.1, and the language used was Verilog Hardware Description Language. [ABSTRACT FROM AUTHOR]

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]

The present study investigates the effect of wearable sensor placements and the use of various machine learning (ML) algorithms for human gait pattern recognition based on temporal gait speeds using wearable multichannel sensor data. Therefore, classifying human gait from features extracted from biomechanical sensor signals and evaluating the effect of using these sensors on gait biomechanics can be successfully achieved with a machine learning approach. In this study, firstly, IMU (Inertial Measurement Unit) and GON (Goniometer) sensor features were extracted for machine learning input using the sliding windows method, and these features were applied to ML classifiers to classify human gait speed. Our experimental findings show that multi- and fusion sensor models provide superior classification performance compared to single sensor models, and the highest accuracy is achieved with the 'FUS09' fusion sensor model and SVM-based classifier. The classification accuracies of the FUS10 fusion sensor model, where all sensor parameters were combined, the FUS09 model, where the GON_ANK and IMU_Ft parameters in this model were excluded, are 0.895 and 0.901, respectively. Consequently, wearable biomechanical sensor data and machine learning approach can be easily preferred in multiple human activity recognition. [ABSTRACT FROM AUTHOR]

This study investigates the effectiveness of amplitude transformation in enhancing the performance and robustness of Multiscale Fuzzy Entropy for Alzheimer's disease detection using electroencephalography signals. Multiscale Fuzzy Entropy is a complexity measure particularly sensitive to intra- and inter-subject variations in signal amplitude, as well as the selection of key parameters such as embedding dimension (m) and similarity criterion (r), which often result in inconsistent outcomes when applied to multivariate data, such as electroencephalography signals. To address these challenges and to generalize the possibility of adopting Multiscale Fuzzy Entropy as a diagnostic tool for Alzheimer's disease, this research explores amplitude transformation preprocessing on electroencephalography signals in Multiscale Fuzzy Entropy calculation across varying parameters. The statistical analysis of the obtained results demonstrates that amplitude transformation preprocessing significantly enhances Multiscale Fuzzy Entropy's ability to detect Alzheimer's disease, achieving higher and more consistent significant comparison percentages, with an average of 73.2% across all parameter combinations, compared with only one raw data combination exceeding 65%. Clustering analysis corroborates these findings, showing that amplitude transformation improves the differentiation between Alzheimer's disease patients and healthy subjects. These results highlight the potential of amplitude transformation to stabilize Multiscale Fuzzy Entropy performance, making it a more reliable tool for early Alzheimer's disease detection. [ABSTRACT FROM AUTHOR]

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In this study, we present a method for continuously estimating emotional valence levels using a marked point process representation of features extracted from respiration amplitude signals. The amplitude of the breath, time of inhalation, and inhalation rate are used to label individuals breaths as potential pleasant or unpleasant valence events using an unsupervised k-means clustering algorithm. We generate two marked point processes consisting of both location and magnitude of inferred valence events corresponding to pleasant and unpleasant (high and low) changes in valence. A state-space model is then used to model high and low valence states based on the occurrence of events indicative of either state in each marked point process. The resulting high valence and low valence states are combined to yield a single estimate of valence level. The algorithm is tested on a dataset containing 23 participants viewing emotion-eliciting video clips. The estimation results for high and low periods, as identified by self-reported ratings, are then compared using a Wilcoxon signed rank test, showing that the method is capable of distinguishing high and low valence periods. The estimated valence level is generally able to capture the trends of the self-reported ratings for most subjects, but fails to fully capture rapid and drastic changes in valence. Continuously estimating valence levels can have applications in the monitoring of patients with mental disorders, such as clinical depression, or multimedia recommendation to identify trends and better develop control strategies to regulate emotions.

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.

Introduction: Multi-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. Methods: We 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. Discussion: Combined, these design considerations overcome data-bottlenecks by allowing on-chip feature extraction and subsequent compression. [ABSTRACT FROM AUTHOR]

The classification of ECG signals is a critical process because it guides the diagnosis of the proper treatment process for the patient. However, any form of disturbance with ECG signals can be highly conspicuous because of the mechanics involved in data acquisition from living beings, which has a significant impact on the classification procedure. The purpose of this research work is to advance ECG signal classification results by employing numerous denoising methods and, in turn, boost the accuracy of cardiovascular diagnoses. To simulate realistic conditions, we added various types of noise to ECG data, including Gaussian, salt and pepper, speckle, uniform, and exponential noise. To overcome the interference of noise from environments in the obtained ECG signals, we employed wavelet transform, median filter, Gaussian filter, and the hybrid of the wavelet and median filters. The proposed hybrid denoising method has better results than the other methods because of the use of wavelet multi-scale analysis and the ability of the median filter to avoid the loss of vital ECG characteristics. Thus, despite a certain proximity in the values, the hybrid method is significantly more accurate and reliable, as evidenced by the mean squared error (MSE), mean absolute error (MAE), R-squared, and Pearson correlation coefficient. More specifically, the hybrid approach provided an MSE of 0.0012 and an MAE of 0.025, the R-squared value for this study was 0.98, and the Pearson correlation coefficient was 0.99, which provides a very good resemblance to the original ECG confirmation. The proposed classification model is based on the modified lightweight CNN or MLCNN that was trained using the noisy and the denoised data. The findings demonstrated that by applying the denoised data, the testing accuracy, precision, recall, and F1 scores achieved 0.92, 0.91, 0.90, and 0.91 for the datasets, while the noisy data achieved 0.80, 0.78, 0.82, and 0.80, respectively. In this study, the signal quality and denoising methods were found to enhance ECG signal classification and diagnostic accuracy while encouraging proper preprocessing in future studies and applications for real-time ECG for cardiac care. [ABSTRACT FROM AUTHOR]

A Field Programmable Gate Array (FPGA) is a semiconductor device based around a Configurable Logic Blocks (CLB) matrix connected by programmable interconnects. FPGA has numerous applications in biomedical signal processing due to its flexible programming and low power consumption. An Electrocardiogram (ECG) is a medical test used to determine heart rates, cardiac activity, and classify arrhythmias. All pass filters have Low Pass Filter (LPF), High Pass Filter (HPF), Band Stop Filter (BSF), and Band Pass Filter (BPF) to produce the exact amplitude in peak detection. However, the separate coefficient for individual filters increases the area in traditional all-pass filters. To overcome this issue, a Reconfigurable All-Pass Filter (RAPF) which considers a single coefficient for all filters and minimizes memory usage, consuming less area and power is employed. The LPF coefficient is utilized to perform the LPF operation, and then the two's complement of this LPF coefficient is computed to produce the HPF coefficient. Next, the two's complement is fed into the Hilbert Transform (HT) to produce the BSF coefficient and determine BSF operations. A RAPF is designed to perform these operations effectively. The RAPF performance is determined using Register, Look Up Table (LUT), Global Buffer (BUFG), Digital Signal Processing (DSP), Power, and Flip Flop (FF). RAPF consumes lesser power of 34 mW for Artix 7 XC7A200TFBG676-2 FPGA device, as opposed to the existing technique, Single Node Reservoir Computing (SNRC) using cumulative mean filter. [ABSTRACT FROM AUTHOR]

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]

Both short-term and long-term intracranial pressure (ICP) monitoring is indicated for a number of neurological pathologies. The clinical gold standard for ICP monitoring is invasive and involves inserting a pressure sensor into the brain tissue or cerebral spinal fluid space. Such sensors can only be used for a limited time due to the risk of infection and sensor degradation. Our aim was to develop a method for long-term non-invasive ICP monitoring after the removal of invasive ICP sensor. Arterial blood pressure (ABP) and cerebral blood flow velocity (FV) signals were used as inputs to an artificial autoencoder neural network. The network was trained with invasively measured ICP. Following the training phase, the network's outputs were used for estimating ICP based on ABP and FV only. The method was verified on clinical data from 98 traumatic brain injury patients. The proposed procedure managed to recover ICP using FV and ABP measurements. The median value of the Pearson correlation between the recovered and the reference ICP signals was 0.7, and the root mean square error was 3.9 mmHg with an interquartile range of less than 5 mmHg. An additional feature of our algorithm is that it not only outputs an ICP estimate, but also provides a confidence level. [ABSTRACT FROM AUTHOR]

Wearable biomechanical sensor signals can be used to precisely recognize human lower extremity movements based upon gait parameters such as walking speed, which is an increasingly important field with a significant role in biomedical studies. In this study, human walking patterns were classified using wearable biomechanical sensors and machine learning and time series analysis techniques. Accurate classification of level-ground gait patterns of IMU, digital goniometer (GON) and electromyography (EMG) sensor data is of great importance in informing physicians and medical device innovators working in this discipline. For this study, an open access dataset recorded from four unilaterally placed IMUs, three GONs and eleven EMG sensors in 22 subjects at different walking speeds was used. The sliding time window method was used to extract features in the first part of biomedical signal processing. Then, the effects of various window lengths and single or multiple sensor models on machine learning classification performance are compared. The results of this study showed that the QSVM classifier and IMU-based sensor with a window length of 1000 (5s) had the highest classification accuracy of 0.954 to classify human gait at different walking speeds based on the proposed method. In addition, it is seen that the classifiers have different classification accuracy for the seven sensor models used. QSVM has higher accuracy in gait recognition compared to WNN and ESKNN classifiers. In particular, the accuracy (0.961) in the experiment using the IMU and GON multiple sensor and QSVM classifier is the highest among other sensor combinations and classifiers. When QSVM classification and gait recognition were compared, the accuracies were found as IMU (0.954), GON (0.827) and EMG (0.735) sensor models, respectively. Then, in dual sensor combination models, the highest accuracy was achieved in IMU-GON (0.961), IMU-EMG (0.895) and GON-EMG (0.776) sensor models, respectively. Finally, the accuracy of the IMU-GON-EMG model, in which all three sensors are included, is 0.919. The findings of this study showed that IMU sensor models improved the classification performance in level-ground gait pattern recognition, and their use together with GON sensor models contributed positively to this performance. It has been found that EMG sensor models show lower classification performance compared to IMU sensor modelsg the necessary precautions were beneficial in terms of protecting the health of the employees. [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]

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]

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]

This paper presents high-frequency universal filter applications based on a voltage differential buffered amplifier (VDBA) using 32 nm fin field effect transistor (FinFET) technology. FinFET technology is a promising alternative to complementary metal-oxide-semiconductor (CMOS) technology to avoid the problems caused by the decrease in transistor size as the technology evolves. In addition to the manufacturing process being similar to CMOS technology, FinFET technology offers many advantages, such as reduced short channel effects, higher drain current, reduced static leakage current, faster switching time, lower supply voltage, lower power consumption, and higher efficiency. The VDBA active circuit block, which has high input impedance and low output impedance, is preferred for high-frequency and highbandwidth applications. It is advantageous to design active filter circuits using VDBA because of its superior features, such as lower power consumption, higher bandwidth, wider range linearity, and the ability to implement the proposed circuits without external resistors. In this study, FinFET-based VDBA and filter application are simulated with the Spice simulation programme using 32 nm PTM technology parameters. Simulation results using 32 nm FinFET technology are compared with those using 0.18 μm TSMC technology. It is concluded that 32 nm FinFET technology reduces power consumption by 98.8 % and increases bandwidth by 145 times. The successful results show that FinFET technology is superior to CMOS technology in analogue circuit design. FinFET-based VDBA circuits and filters will be more advantageous in the design of signal processing and biomedical applications. [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]

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]

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]