Wearable laser Doppler flowmetry for non-invasive assessment of diabetic foot microcirculation: methodological considerations and clinical implications.

Significance: Type 2 diabetes mellitus (T2DM) is a global health concern with significant implications for vascular health. The current evaluation methods cannot achieve effective, portable, and quantitative evaluation of foot microcirculation. Aim: We aim to use a wearable device laser Doppler flowmetry (LDF) to evaluate the foot microcirculation of T2DM patients at rest. Approach: Eleven T2DM patients and twelve healthy subjects participated in this study. The wearable LDF was used to measure the blood flows (BFs) for regions of the first metatarsal head (M1), fifth metatarsal head (M5), heel, and dorsal foot. Typical wavelet analysis was used to decompose the five individual control mechanisms: endothelial, neurogenic, myogenic, respiratory, and heart components. The mean BF and sample entropy (SE) were calculated, and the differences between diabetic patients and healthy adults and among the four regions were compared. Results: Diabetic patients showed significantly reduced mean BF in the neurogenic (p=0.044) and heart (p=0.001) components at the M1 and M5 regions (p=0.025) compared with healthy adults. Diabetic patients had significantly lower SE in the neurogenic (p=0.049) and myogenic (p=0.032) components at the M1 region, as well as in the endothelial (p<0.001) component at the M5 region and in the myogenic component at the dorsal foot (p=0.007), compared with healthy adults. The SE in the myogenic component at the dorsal foot was lower than at the M5 region (p=0.050) and heel area (p=0.041). Similarly, the SE in the heart component at the dorsal foot was lower than at the M5 region (p=0.017) and heel area (p=0.028) in diabetic patients. Conclusions: This study indicated the potential of using the novel wearable LDF device for tracking vascular complications and implementing targeted interventions in T2DM patients.

Quantification of attenuation and speckle features from endoscopic OCT images for the diagnosis of human brain glioma.

Application of optical coherence tomography (OCT) in neurosurgery mostly includes the discrimination between intact and malignant tissues aimed at the detection of brain tumor margins. For particular tissue types, the existing approaches demonstrate low performance, which stimulates the further research for their improvement. The analysis of speckle patterns of brain OCT images is proposed to be taken into account for the discrimination between human brain glioma tissue and intact cortex and white matter. The speckle properties provide additional information of tissue structure, which could help to increase the efficiency of tissue differentiation. The wavelet analysis of OCT speckle patterns was applied to extract the power of local brightness fluctuations in speckle and its standard deviation. The speckle properties are analysed together with attenuation ones using a set of ex vivo brain tissue samples, including glioma of different grades. Various combinations of these features are considered to perform linear discriminant analysis for tissue differentiation. The results reveal that it is reasonable to include the local brightness fluctuations at first two wavelet decomposition levels in the analysis of OCT brain images aimed at neurosurgical diagnosis.

Improved filtering methods to suppress cardiovascular contamination in electrical impedance tomography recordings.

Objective.Electrical impedance tomography (EIT) produces clinical useful visualization of the distribution of ventilation inside the lungs. The accuracy of EIT-derived parameters can be compromised by the cardiovascular signal. Removal of these artefacts is challenging due to spectral overlapping of the ventilatory and cardiovascular signal components and their time-varying frequencies. We designed and evaluated advanced filtering techniques and hypothesized that these would outperform traditional low-pass filters.Approach.Three filter techniques were developed and compared against traditional low-pass filtering: multiple digital notch filtering (MDN), empirical mode decomposition (EMD) and the maximal overlap discrete wavelet transform (MODWT). The performance of the filtering techniques was evaluated (1) in the time domain (2) in the frequency domain (3) by visual inspection. We evaluated the performance using simulated contaminated EIT data and data from 15 adult and neonatal intensive care unit patients.Main result.Each filter technique exhibited varying degrees of effectiveness and limitations. Quality measures in the time domain showed the best performance for MDN filtering. The signal to noise ratio was best for DLP, but at the cost of a high relative and removal error. MDN outbalanced the performance resulting in a good SNR with a low relative and removal error. MDN, EMD and MODWT performed similar in the frequency domain and were successful in removing the high frequency components of the data.Significance.Advanced filtering techniques have benefits compared to traditional filters but are not always better. MDN filtering outperformed EMD and MODWT regarding quality measures in the time domain. This study emphasizes the need for careful consideration when choosing a filtering approach, depending on the dataset and the clinical/research question.

Selecting EEG channels and features using multi-objective optimization for accurate MCI detection: validation using leave-one-subject-out strategy.

Effective management of dementia requires the timely detection of mild cognitive impairment (MCI). This paper introduces a multi-objective optimization approach for selecting EEG channels (and features) for the purpose of detecting MCI. Firstly, each EEG signal from each channel is decomposed into subbands using either variational mode decomposition (VMD) or discrete wavelet transform (DWT). A feature is then extracted from each subband using one of the following measures: standard deviation, interquartile range, band power, Teager energy, Katz's and Higuchi's fractal dimensions, Shannon entropy, sure entropy, or threshold entropy. Different machine learning techniques are used to classify the features of MCI cases from those of healthy controls. The classifier's performance is validated using leave-one-subject-out (LOSO) cross-validation (CV). The non-dominated sorting genetic algorithm (NSGA)-II is designed with the aim of minimizing the number of EEG channels (or features) and maximizing classification accuracy. The performance is evaluated using a publicly available online dataset containing EEGs from 19 channels recorded from 24 participants. The results demonstrate a significant improvement in performance when utilizing the NSGA-II algorithm. By selecting only a few appropriate EEG channels, the LOSO CV-based results show a significant improvement compared to using all 19 channels. Additionally, the outcomes indicate that accuracy can be further improved by selecting suitable features from different channels. For instance, by combining VMD and Teager energy, the SVM accuracy obtained using all channels is 74.24%. Interestingly, when only five channels are selected using NSGA-II, the accuracy increases to 91.56%. The accuracy is further improved to 95.28% when using only 8 features selected from 7 channels. This demonstrates that by choosing informative features or channels while excluding noisy or irrelevant information, the impact of noise is reduced, resulting in improved accuracy. These promising findings indicate that, with a limited number of channels and features, accurate diagnosis of MCI is achievable, which opens the door for its application in clinical practice.

Long-term effects of climate factors on dengue fever over a 40-year period.

BACKGROUND: Dengue fever stands as one of the most extensively disseminated mosquito-borne infectious diseases worldwide. While numerous studies have investigated its influencing factors, a gap remains in long-term analysis, impeding the identification of temporal patterns, periodicity in transmission, and the development of effective prevention and control strategies. Thus, we aim to analyze the periodicity of dengue fever incidence and explore the association between various climate factors and the disease over an extended time series. METHODS: By utilizing monthly dengue fever cases and climate data spanning four decades (1978-2018) in Guangdong province, China, we employed wavelet analysis to detect dengue fever periodicity and analyze the time-lag relationship with climate factors. Additionally, Geodetector q statistic was employed to quantify the explanatory power of each climate factor and assess interaction effects. RESULTS: Our findings revealed a prolonged transmission period of dengue fever over the 40-year period, transitioning from August to November in the 1970s to nearly year-round in the 2010s. Moreover, we observed lags of 1.5, 3.5, and 3 months between dengue fever and temperature, relative humidity, and precipitation, respectively. The explanatory power of precipitation, temperature, relative humidity, and the Oceanic Niño Index (ONI) on dengue fever was determined to be 18.19%, 12.04%, 11.37%, and 5.17%, respectively. Dengue fever exhibited susceptibility to various climate factors, with notable nonlinear enhancement arising from the interaction of any two variables. Notably, the interaction between precipitation and humidity yielded the most significant effect, accounting for an explanatory power of 75.32%. CONCLUSIONS: Consequently, future prevention and control strategies for dengue fever should take into account these climate changes and formulate corresponding measures accordingly. In regions experiencing the onset of high temperatures, humidity, and precipitation, it is imperative to initiate mosquito prevention and control measures within a specific window period of 1.5 months.

S-WD-EEMD: A hybrid framework for imbalanced sEMG signal analysis in diagnosis of human knee abnormality.

The diagnosis of human knee abnormalities using the surface electromyography (sEMG) signal obtained from lower limb muscles with machine learning is a major problem due to the noisy nature of the sEMG signal and the imbalance in data corresponding to healthy and knee abnormal subjects. To address this challenge, a combination of wavelet decomposition (WD) with ensemble empirical mode decomposition (EEMD) and the Synthetic Minority Oversampling Technique (S-WD-EEMD) is proposed. In this study, a hybrid WD-EEMD is considered for the minimization of noises produced in the sEMG signal during the collection, while the Synthetic Minority Oversampling Technique (SMOTE) is considered to balance the data by increasing the minority class samples during the training of machine learning techniques. The findings indicate that the hybrid WD-EEMD with SMOTE oversampling technique enhances the efficacy of the examined classifiers when employed on the imbalanced sEMG data. The F-Score of the Extra Tree Classifier, when utilizing WD-EEMD signal processing with SMOTE oversampling, is 98.4%, whereas, without the SMOTE oversampling technique, it is 95.1%.

STN localization using local field potentials based on wavelet packet features and stacking ensemble learning.

BACKGROUND: DBS entails the insertion of an electrode into the patient brain, enabling Subthalamic nucleus (STN) stimulation. Accurate delineation of STN borders is a critical but time-consuming task, traditionally reliant on the neurosurgeon experience in deciphering the intricacies of microelectrode recording (MER). While clinical outcomes of MER have been satisfactory, they involve certain risks to patient safety. Recently, there has been a growing interest in exploring the potential of local field potentials (LFP) due to their correlation with the STN motor territory. METHOD: A novel STN detection system, integrating LFP and wavelet packet transform (WPT) with stacking ensemble learning, is developed. Initial steps involve the inclusion of soft thresholding to increase robustness to LFP variability. Subsequently, non-linear WPT features are extracted. Finally, a unique ensemble model, comprising a dual-layer structure, is developed for STN localization. We harnessed the capabilities of support vector machine, Decision tree and k-Nearest Neighbor in conjunction with long short-term memory (LSTM) network. LSTM is pivotal for assigning adequate weights to every base model. RESULTS: Results reveal that the proposed model achieved a remarkable accuracy and F1-score of 89.49% and 91.63%. COMPARISON WITH EXISTING METHODS: Ensemble model demonstrated superior performance when compared to standalone base models and existing meta techniques. CONCLUSION: This framework is envisioned to enhance the efficiency of DBS surgery and reduce the reliance on clinician experience for precise STN detection. This achievement is strategically significant to serve as an invaluable tool for refining the electrode trajectory, potentially replacing the current methodology based on MER.

Discrete Wavelet Transform based ECG classification using gcForest: A deep ensemble method.

BACKGROUND: Cardiovascular diseases (CVDs) are the leading global cause of mortality, necessitating advanced diagnostic tools for early detection. The electrocardiogram (ECG) is pivotal in diagnosing cardiac abnormalities due to its non-invasive nature. OBJECTIVE: This study aims to propose a novel approach for ECG signal classification, addressing the challenges posed by the complexity of ECG signals associated with various diseases. METHODS: Our method integrates Discrete Wavelet Transform (DWT) for feature extraction, capturing salient features of cardiovascular diseases. Subsequently, the gcForest model is employed for efficient classification. The approach is tested on the MIT-BIH Arrhythmia Database. RESULTS: The proposed method demonstrates promising results on the MIT-BIH Arrhythmia Database, achieving a test accuracy of 98.55%, recall of 98.48%, precision of 98.44%, and an F1 score of 98.46%. Additionally, the model exhibits robustness and low sensitivity to hyper-parameters. CONCLUSION: The combined use of DWT and the gcForest model proves effective in ECG signal classification, showcasing high accuracy and reliability. This approach holds potential for improving early detection of cardiovascular diseases, contributing to enhanced cardiac healthcare.

Correlation Analysis of Multi-Scale Ictal EEG Signals in Juvenile Myoclonic Epilepsy.

BACKGROUND: To explore the time-frequency structure and cross-scale coupling of electroencephalography (EEG) signals during seizure in juvenile myoclonic epilepsy (JME), correlations between different leads, as well as dynamic evolution in epileptic discharge, progression and end of seizure were examined. METHODS: EEG data were obtained for 10 subjects with JME and 10 normal controls and were decomposed using gauss continuous wavelet transform (CWT). The phase amplitude coupling (PAC) relationship between the 11th (4.57 Hz) and 17th (0.4 Hz) scale was investigated. Correlations were examined between the 11th and 17th scale EEG signals in different leads during seizure, using multi-scale cross correlation analysis. RESULTS: The time-frequency structure of JME subjects showed strong rhythmic activity in the 11th and 17th scales and a close PAC was identified. Correlation analysis revealed that the ictal JME correlation first increased in the anterior head early in seizure and gradually expanded to the posterior head. CONCLUSION: PAC was exhibited between the 11th and 17th scales during JME seizure. The results revealed that the correlation in the anterior leads was higher than the posterior leads. In the perictal period, the 17th scale EEG signal preceded the 11th scale signal and remained for some time after a seizure. This suggests that the 17th scale signal may play an important role in JME seizure.

LRFNet: A real-time medical image fusion method guided by detail information.

Multimodal medical image fusion (MMIF) technology plays a crucial role in medical diagnosis and treatment by integrating different images to obtain fusion images with comprehensive information. Deep learning-based fusion methods have demonstrated superior performance, but some of them still encounter challenges such as imbalanced retention of color and texture information and low fusion efficiency. To alleviate the above issues, this paper presents a real-time MMIF method, called a lightweight residual fusion network. First, a feature extraction framework with three branches is designed. Two independent branches are used to fully extract brightness and texture information. The fusion branch enables different modal information to be interactively fused at a shallow level, thereby better retaining brightness and texture information. Furthermore, a lightweight residual unit is designed to replace the conventional residual convolution in the model, thereby improving the fusion efficiency and reducing the overall model size by approximately 5 times. Finally, considering that the high-frequency image decomposed by the wavelet transform contains abundant edge and texture information, an adaptive strategy is proposed for assigning weights to the loss function based on the information content in the high-frequency image. This strategy effectively guides the model toward preserving intricate details. The experimental results on MRI and functional images demonstrate that the proposed method exhibits superior fusion performance and efficiency compared to alternative approaches. The code of LRFNet is available at https://github.com/HeDan-11/LRFNet.

Respiration and Heart Rate Monitoring in Smart Homes: An Angular-Free Approach with an FMCW Radar.

This paper proposes a new approach for wide angle monitoring of vital signs in smart home applications. The person is tracked using an indoor radar. Upon detecting the person to be static, the radar automatically focuses its beam on that location, and subsequently breathing and heart rates are extracted from the reflected signals using continuous wavelet transform (CWT) analysis. In this way, leveraging the radar's on-chip processor enables real-time monitoring of vital signs across varying angles. In our experiment, we employ a commercial multi-input multi-output (MIMO) millimeter-wave FMCW radar to monitor vital signs within a range of 1.15 to 2.3 m and an angular span of -44.8 to +44.8 deg. In the Bland-Altman plot, the measured results indicate the average difference of -1.5 and 0.06 beats per minute (BPM) relative to the reference for heart rate and breathing rate, respectively.

Association of Visual-Based Signals with Electroencephalography Patterns in Enhancing the Drowsiness Detection in Drivers with Obstructive Sleep Apnea.

Individuals with obstructive sleep apnea (OSA) face increased accident risks due to excessive daytime sleepiness. PERCLOS, a recognized drowsiness detection method, encounters challenges from image quality, eyewear interference, and lighting variations, impacting its performance, and requiring validation through physiological signals. We propose visual-based scoring using adaptive thresholding for eye aspect ratio with OpenCV for face detection and Dlib for eye detection from video recordings. This technique identified 453 drowsiness (PERCLOS ≥ 0.3 || CLOSDUR ≥ 2 s) and 474 wakefulness episodes (PERCLOS < 0.3 and CLOSDUR < 2 s) among fifty OSA drivers in a 50 min driving simulation while wearing six-channel EEG electrodes. Applying discrete wavelet transform, we derived ten EEG features, correlated them with visual-based episodes using various criteria, and assessed the sensitivity of brain regions and individual EEG channels. Among these features, theta-alpha-ratio exhibited robust mapping (94.7%) with visual-based scoring, followed by delta-alpha-ratio (87.2%) and delta-theta-ratio (86.7%). Frontal area (86.4%) and channel F4 (75.4%) aligned most episodes with theta-alpha-ratio, while frontal, and occipital regions, particularly channels F4 and O2, displayed superior alignment across multiple features. Adding frontal or occipital channels could correlate all episodes with EEG patterns, reducing hardware needs. Our work could potentially enhance real-time drowsiness detection reliability and assess fitness to drive in OSA drivers.

Hybrid WT-CNN-GRU-based model for the estimation of reservoir water quality variables considering spatio-temporal features.

Water quality indicators (WQIs), such as chlorophyll-a (Chl-a) and dissolved oxygen (DO), are crucial for understanding and assessing the health of aquatic ecosystems. Precise prediction of these indicators is fundamental for the efficient administration of rivers, lakes, and reservoirs. This research utilized two unique DL algorithms-namely, convolutional neural network (CNNs) and gated recurrent units (GRUs)-alongside their amalgamation, CNN-GRU, to precisely gauge the concentration of these indicators within a reservoir. Moreover, to optimize the outcomes of the developed hybrid model, we considered the impact of a decomposition technique, specifically the wavelet transform (WT). In addition to these efforts, we created two distinct machine learning (ML) algorithms-namely, random forest (RF) and support vector regression (SVR)-to demonstrate the superior performance of deep learning algorithms over individual ML ones. We initially gathered WQIs from diverse locations and varying depths within the reservoir using an AAQ-RINKO device in the study area to achieve this. It is important to highlight that, despite utilizing diverse data-driven models in water quality estimation, a significant gap persists in the existing literature regarding implementing a comprehensive hybrid algorithm. This algorithm integrates the wavelet transform, convolutional neural network (CNN), and gated recurrent unit (GRU) methodologies to estimate WQIs accurately within a spatiotemporal framework. Subsequently, the effectiveness of the models that were developed was assessed utilizing various statistical metrics, encompassing the correlation coefficient (r), root mean square error (RMSE), mean absolute error (MAE), and Nash-Sutcliffe efficiency (NSE) throughout both the training and testing phases. The findings demonstrated that the WT-CNN-GRU model exhibited better performance in comparison with the other algorithms by 13% (SVR), 13% (RF), 9% (CNN), and 8% (GRU) when R-squared and DO were considered as evaluation indices and WQIs, respectively.

A single-joint multi-task motor imagery EEG signal recognition method based on Empirical Wavelet and Multi-Kernel Extreme Learning Machine.

BACKGROUND: In the pursuit of finer Brain-Computer Interface commands, research focus has shifted towards classifying EEG signals for multiple tasks. While single-joint multitasking motor imagery provides support, distinguishing between EEG signals from the same joint remains challenging due to their similar brain spatial distribution. NEW METHOD: We designed experiments involving three motor imagery tasks-wrist extension, wrist flexion, and wrist abduction-with six participants. Based on this, a single-joint multi-task motor imagery EEG signal recognition method using Empirical Wavelet Decomposition and Multi-Kernel Extreme Learning Machine is proposed. This method employs Empirical Wavelet Decomposition (EWT) for modal decomposition, screening, and reconstruction of raw EEG signals, feature extraction using Common Spatial Patterns (CSP), and classification using Multi-Kernel Extreme Learning Machine (MKELM). RESULTS: After EWT processing, differences in time and frequency characteristics between EEG signals of different classes were enhanced, with the MKELM model achieving an average recognition accuracy of 91.93 %. COMPARISON WITH OTHER METHODS AND CONCLUSIONS: We compared EWT with Empirical Mode Decomposition (EMD), Variational Mode Decomposition (VMD), Local Mean Decomposition (LMD), and Wavelet Packet Decomposition (WPD). The results showed that the differences between various types of EEG signals processed by EWT were the most pronounced. The MKELM model outperformed traditional machine learning models such as Extreme Learning Machine (ELM), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Linear Discriminant Analysis (LDA) in terms of recognition performance, and also exhibited faster training speeds than deep learning models such as Bayesian Convolutional Neural Network (BCNN) and Attention-based Dual-scale Fusion Convolutional Neural Network (ADFCNN). In summary, the proposed method provides a new approach for achieving finer Brain-Computer Interface commands.

Hi-gMISnet: generalized medical image segmentation using DWT based multilayer fusion and dual mode attention into high resolutionpGAN.

Objective.Automatic medical image segmentation is crucial for accurately isolating target tissue areas in the image from background tissues, facilitating precise diagnoses and procedures. While the proliferation of publicly available clinical datasets led to the development of deep learning-based medical image segmentation methods, a generalized, accurate, robust, and reliable approach across diverse imaging modalities remains elusive.Approach.This paper proposes a novel high-resolution parallel generative adversarial network (pGAN)-based generalized deep learning method for automatic segmentation of medical images from diverse imaging modalities. The proposed method showcases better performance and generalizability by incorporating novel components such as partial hybrid transfer learning, discrete wavelet transform (DWT)-based multilayer and multiresolution feature fusion in the encoder, and a dual mode attention gate in the decoder of the multi-resolution U-Net-based GAN. With multi-objective adversarial training loss functions including a unique reciprocal loss for enforcing cooperative learning inpGANs, it further enhances the robustness and accuracy of the segmentation map.Main results.Experimental evaluations conducted on nine diverse publicly available medical image segmentation datasets, including PhysioNet ICH, BUSI, CVC-ClinicDB, MoNuSeg, GLAS, ISIC-2018, DRIVE, Montgomery, and PROMISE12, demonstrate the proposed method's superior performance. The proposed method achieves mean F1 scores of 79.53%, 88.68%, 82.50%, 93.25%, 90.40%, 94.19%, 81.65%, 98.48%, and 90.79%, respectively, on the above datasets, surpass state-of-the-art segmentation methods. Furthermore, our proposed method demonstrates robust multi-domain segmentation capabilities, exhibiting consistent and reliable performance. The assessment of the model's proficiency in accurately identifying small details indicates that the high-resolution generalized medical image segmentation network (Hi-gMISnet) is more precise in segmenting even when the target area is very small.Significance.The proposed method provides robust and reliable segmentation performance on medical images, and thus it has the potential to be used in a clinical setting for the diagnosis of patients.

A comparison of wavelet-based action potential detection from the NeuroAmp and the Iowa Bioengineering Nerve Traffic Analysis system.

Microneurographic recordings of muscle sympathetic nerve activity (MSNA) reflect postganglionic sympathetic axonal activity directed toward the skeletal muscle vasculature. Recordings are typically evaluated for spontaneous bursts of MSNA; however, the filtering and integration of raw neurograms to obtain multiunit bursts conceals the underlying c-fiber discharge behavior. The continuous wavelet transform with matched mother wavelet has permitted the assessment of action potential discharge patterns, but this approach uses a mother wavelet optimized for an amplifier that is no longer commercially available (University of Iowa Bioengineering Nerve Traffic Analysis System; Iowa NTA). The aim of this project was to determine the morphology and action potential detection performance of mother wavelets created from the commercially available NeuroAmp (ADinstruments), from distinct laboratories, compared with a mother wavelet generated from the Iowa NTA. Four optimized mother wavelets were generated in a two-phase iterative process from independent datasets, collected by separate laboratories (one Iowa NTA, three NeuroAmp). Action potential extraction performance of each mother wavelet was compared for each of the NeuroAmp-based datasets. The total number of detected action potentials was not significantly different across wavelets. However, the predictive value of action potential detection was reduced when the Iowa NTA wavelet was used to detect action potentials in NeuroAmp data, but not different across NeuroAmp wavelets. To standardize approaches, we recommend a NeuroAmp-optimized mother wavelet be used for the evaluation of sympathetic action potential discharge behavior when microneurographic data are collected with this system.NEW & NOTEWORTHY The morphology of custom mother wavelets produced across laboratories using the NeuroAmp was highly similar, but distinct from the University of Iowa Bioengineering Nerve Traffic Analysis System. Although the number of action potentials detected was similar between collection systems and mother wavelets, the predictive value differed. Our data suggest action potential analysis using the continuous wavelet transform requires a mother wavelet optimized for the collection system.

Understanding the Role of Self-Attention in a Transformer Model for the Discrimination of SCD From MCI Using Resting-State EEG.

The identification of EEG biomarkers to discriminate Subjective Cognitive Decline (SCD) from Mild Cognitive Impairment (MCI) conditions is a complex task which requires great clinical effort and expertise. We exploit the self-attention component of the Transformer architecture to obtain physiological explanations of the model's decisions in the discrimination of 56 SCD and 45 MCI patients using resting-state EEG. Specifically, an interpretability workflow leveraging attention scores and time-frequency analysis of EEG epochs through Continuous Wavelet Transform is proposed. In the classification framework, models are trained and validated with 5-fold cross-validation and evaluated on a test set obtained by selecting 20% of the total subjects. Ablation studies and hyperparameter tuning tests are conducted to identify the optimal model configuration. Results show that the best performing model, which achieves acceptable results both on epochs' and patients' classification, is capable of finding specific EEG patterns that highlight changes in the brain activity between the two conditions. We demonstrate the potential of attention weights as tools to guide experts in understanding which disease-relevant EEG features could be discriminative of SCD and MCI.

Quantifying local field potential dynamics with amplitude and frequency stability between ON and OFF medication and stimulation in Parkinson's disease.

Neural oscillations are critical to understanding the synchronisation of neural activities and their relevance to neurological disorders. For instance, the amplitude of beta oscillations in the subthalamic nucleus has gained extensive attention, as it has been found to correlate with medication status and the therapeutic effects of continuous deep brain stimulation in people with Parkinson's disease. However, the frequency stability of subthalamic nucleus beta oscillations, which has been suggested to be associated with dopaminergic information in brain states, has not been well explored. Moreover, the administration of medicine can have inverse effects on changes in frequency and amplitude. In this study, we proposed a method based on the stationary wavelet transform to quantify the amplitude and frequency stability of subthalamic nucleus beta oscillations and evaluated the method using simulation and real data for Parkinson's disease patients. The results suggest that the amplitude and frequency stability quantification has enhanced sensitivity in distinguishing pathological conditions in Parkinson's disease patients. Our quantification shows the benefit of combining frequency stability information with amplitude and provides a new potential feedback signal for adaptive deep brain stimulation.

Wavelet scattering transform and entropy features in fluorescence spectral signal analysis for cervical cancer diagnosis.

Cervical cancer is a prevalent malignant tumor within the female reproductive system and is regarded as a prominent cause of female mortality on a global scale. Timely and precise detection of various phases of cervical cancer holds the potential to substantially enhance both the rate of successful treatment and the duration of patient survival. Fluorescence spectroscopy is a highly sensitive method for detecting the biochemical changes that arise during cancer progression. In our study, fluorescence spectral data is collected from a diverse group of 110 subjects. The potential of the scattering transform technique for the purpose of cancer detection is explored. The processed signal undergoes an initial decomposition into scattering coefficients using the wavelet scattering transform (WST). Subsequently, the scattering coefficients are subjected to computation for fuzzy entropy, dispersion entropy, phase entropy, and spectral entropy, for effectively characterizing the fluorescence spectral signals. These combined features generated through the proposed approach are then fed to 1D convolutional neural network (CNN) classifier to classify them into normal, pre-cancerous, and cancerous categories, thereby evaluating the effectiveness of the proposed methodology. We obtained mean classification accuracy of 97% using 5-fold cross-validation. This demonstrates the potential of combining WST and entropic features for analyzing fluorescence spectroscopy signals using 1D CNN classifier that enables early cancer detection in contrast to prevailing diagnostic methods.

Exploring the potential of pretrained CNNs and time-frequency methods for accurate epileptic EEG classification: a comparative study.

Prompt diagnosis of epilepsy relies on accurate classification of automated electroencephalogram (EEG) signals. Several approaches have been developed to characterize epileptic EEG data; however, none of them have exploited time-frequency data to evaluate the effect of tweaking parameters in pretrained frameworks for EEG data classification. This study compares the performance of several pretrained convolutional neural networks (CNNs) namely, AlexNet, GoogLeNet, MobileNetV2, ResNet-18 and SqueezeNet for the localization of epilepsy EEG data using various time-frequency data representation algorithms. Continuous wavelet transform (CWT), empirical Fourier decomposition (EFD), empirical mode decomposition (EMD), empirical wavelet transform (EWT), and variational mode decomposition (VMD) were exploited for the acquisition of 2D scalograms from 1D data. The research evaluates the effect of multiple factors, including noisy versus denoised scalograms, different optimizers, learning rates, single versus dual channels, model size, and computational time consumption. The benchmark Bern-Barcelona EEG dataset is used for testing purpose. Results obtained show that the combination of MobileNetV2, Continuous Wavelet Transform (CWT) and Adam optimizer at a learning rate of 10-4, coupled with dual-data channels, provides the best performance metrics. Specifically, these parameters result in optimal sensitivity, specificity, f1-score, and classification accuracy, with respective values of 96.06%, 96.15%, 96.08%, and 96.10%. To further corroborate the efficacy of opted pretrained models on exploited Signal Decomposition (SD) algorithms, the classifiers are also being simulated on Temple University database at pinnacle modeling composition. A similar pattern in the outcome readily validate the findings of our study and robustness of deep learning models on epilepsy EEG scalograms.The conclusions drawn emphasize the potential of pretrained CNN-based models to create a robust, automated system for diagnosing epileptiform. Furthermore, the study offers insights into the effectiveness of varying time-frequency techniques and classifier parameters for classifying epileptic EEG data.