Non-invasive Monitoring Technique Research Articles

Imaging Photoplethysmography (iPPG) in Head and Neck Reconstructive Surgery: A Novel Technique for Noninvasive Flap Perfusion Monitoring.

Evaluate imaging photoplethysmography (iPPG) as a novel noninvasive technique to assess flap perfusion in head and neck free flap reconstructive (FFR) surgeries. Intraoperative iPPG was performed in 17 patients undergoing FFR surgery. Imaging consisted of a 30-s video from which perfusion maps were extracted, providing detailed information about blood flow and pulsatility in the flap microvasculature. During each procedure, iPPG acquisitions were acquired representing distinct perfusion conditions of the flap (fully perfused/ischemic/reperfused). When possible, postoperative measurements were performed to assess flap recovery during the critical time period (3 days) and long-term follow-up (30 days). Perfusion maps, displaying iPPG amplitude and delay times, correlated strongly (p < 0.001) with the perfusion status of the tissue. One case of postoperative thrombosis, leading to flap failure, was identified with iPPG. After surgical revision in this case, flap perfusion was restored and confirmed by iPPG. Postoperative follow-up imaging allowed for objective visualization of flap recovery short term (3 days) and up to 30 days after the surgical procedure. This study shows that iPPG is suitable for objective and noninvasive assessment of flap perfusion in head and neck FFR surgery. In addition, postoperative monitoring shows potential for assessing flap perfusion in patients with increased risk of postoperative complications.

Plasmon-enhanced NIR-II photoluminescence of rare-earth oxide nanoprobes for high-sensitivity optical temperature sensing.

With ongoing advancements in photothermal therapy, achieving efficient tumor cell eradication while minimizing damage to healthy tissues necessitates a highly effective and non-invasive real-time temperature monitoring technique for human tissues. Herein, we report a near-infrared (NIR)-II optical temperature sensing nanoprobe featuring rare-earth-doped gadolinium oxide nanocrystals (RENCs) attached to the dumbbell mesoporous silica-coated gold nanorods (AuNRs). The composite nanoprobe presents an intense absorption in the NIR region, and NIR-II photoluminescence (PL) increases by 97.2 to 102-fold compared to pure RENCs upon 980 nm irradiation. The localized electric field generated through surface plasmon resonance effects of AuNRs demonstrated a dumbbell-shaped distribution that aligns with the structure of nanoprobes, maximizing the PL enhancement of RENCs. Moreover, the NIR-II emissions are changed with the rising temperature, with an exceptional relative sensitivity of 7.25% K-1 at 338 K based on PL lifetime, indicating the nanoprobe is highly potential for optical temperature sensing.

Exploring vocal biomarkers as non-invasive fine-tuning assays of cardiovascular health: heart failure model

Abstract Background Mobile health (mHealth) technologies offer a promising avenue for non-invasive heart failure monitoring through vocal biomarkers. The relationship between feature changes in anonymous and standardized voice recordings of heart failure patients and the NYHA scores of the same patients is assessed, aiming to identify vocal biomarkers as delicate indicators of chronic noncommunicable disease progression and/ or fluctuation. Methods In a cardiology outpatient setting, 55 chronic heart failure patients provided voice recordings via a mobile app, performing specific vocal tasks prospectively. We analyzed waveforms visually via Mel spectrograms and extracting features by audio processing, focusing on chroma centroids and tonnetz measurements across 147 clinical and laboratory parameters (Mean NYHA: 2.62, SD: 0.56). Applying Ordinary Least Squares (OLS) test, we assessed the relevance of spectral bandwidth mean (0.04), spectral bandwidth std (0.00), spectral roll-off std (0.011) between NYHA scores, given each p-score &amp;lt; 0.05. Results 147 voice recordings taken from 55 patients were analyzed to examine whether there were any correlations between changes in the voice recordings and NYHA scores. OLS test underscored the significance of spectral bandwidth mean (0.04), spectral bandwidth std (0.00), spectral roll-off std (0.011) between NYHA scores with p-value &amp;lt; 0.05. These findings show the potential role of multiple vocal features in reflecting heart failure progression and/or fluctuation. Analysis based on the clinical status progression from NYHA 2 to NYHA 3 over time showed notable changes in chroma centroids (mean = 0.006, std = 0.174, skew = 0.179, kurtosis = 0.205) and tonnetz (mean = 0.023, std = 0.131, skew = 0.023, kurtosis = 4.247). Conclusion This investigation highlights vocal biomarkers' potential in heart failure management, offering insights into non-invasive monitoring techniques and shows the importance of standardised recording procedures and patient engagement for data integrity. Future directions include quantifying vocal changes' correlation with clinical status and integrating additional parameters (e.g., edema, weight) to enhance predictive models for personalised care.Vocal features according to NYHA scores

Deep learning from head CT scans to predict elevated intracranial pressure.

Elevated intracranial pressure (ICP) resulting from severe head injury or stroke poses a risk of secondary brain injury that requires neurosurgical intervention. However, currently available noninvasive monitoring techniques for predicting ICP are not sufficiently advanced. We aimed to develop a minimally invasive ICP prediction model using simple CT images to prevent secondary brain injury caused by elevated ICP. We used the following three methods to determine the presence or absence of elevated ICP using midbrain-level CT images: (1) a deep learning model created using the Python (PY) programming language; (2) a model based on cistern narrowing and scaling of brainstem deformities and presence of hydrocephalus, analyzed using the statistical tool Prediction One (PO); and (3) identification of ICP by senior residents (SRs). We compared the accuracy of the validation and test data using fivefold cross-validation and visualized or quantified the areas of interest in the models. The accuracy of the validation data for the PY, PO, and SR methods was 83.68% (83.42%-85.13%), 85.71% (73.81%-88.10%), and 66.67% (55.96%-72.62%), respectively. Significant differences in accuracy were observed between the PY and SR methods. Test data accuracy was 77.27% (70.45%-77.2%), 84.09% (75.00%-85.23%), and 61.36% (56.82%-68.18%), respectively. Overall, the outcomes suggest that these newly developed models may be valuable tools for the rapid and accurate detection of elevated ICP in clinical practice. These models can easily be applied to other sites, as a single CT image at the midbrain level can provide a highly accurate diagnosis.

Ultrasonographic Udder Monitoring and Disease diagnosis in She-goat

Ultrasonography is an effective, non-invasive technique for regular monitoring of udder health to ensure the best quality of milk. The study was conducted to measure the diameter of gland cistern before and after milking and evaluate the effect of breeds, age, and seasons on udder diseases in she-goats. The diameter was measured through ultrasonography before and after milking and retrospective data (breed, age, season) were collected from the Veterinary Teaching Hospital, Mymensingh. Variation in gland cistern diameter (mm) was significant (p&lt;0.05), meaning that the gland cistern’s diameter changed more before and after milking. Results also revealed an insignificant (p&gt;0.05) difference between the left and right quarter gland cistern’s diameter independent of time. This study showed that the occurrence of udder diseases was higher in the Black Bengal breed (82%) compared to Jamuna Pari (18%). Mastitis mostly occurred at the age of between 2 to 3 years old (25.7%) and at the winter season (39.8%). All age groups had an affected incidence of 1.8% for udder wounds and showed higher occurrence during the summer season (5.3%). Furthermore, this study indicated that udder edema affection is more common in the group of goats younger than two years old and during the winter season (3.5%) This study concludes that normal physiological structures i.e. udder parenchyma, gland cistern and different pathological conditions such as mastitis, fibrosis can be easily monitored through transcutaneous ultrasonography. J Bangladesh Agril Univ 22(3): 396-403, 2024

Amino-Acid-Encoded Supramolecular Nanostructures for Persistent Bioluminescence Imaging of Tumor.

Bioluminescence imaging (BLI) is a powerful technique for noninvasive monitoring of biological processes and cell transplantation. Nonetheless, the application of D-luciferin, which is widely employed as a bioluminescent probe, is restricted in long-term in vivo tracking due to its short half-life. This study presents a novel approach using amino acid-encoded building blocks to accumulate and preserve luciferin within tumor cells, through a supramolecular self-assembly strategy. The building block platform called Cys(SEt)-X-CBT (CXCBT, with X representing any amino acid) utilizes a covalent-noncovalent hybrid self-assembly mechanism to generate diverse luciferin-containing nanostructures in tumor cells after glutathione reduction. These nanostructures exhibit efficient tumor-targeted delivery as well as sequence-dependent well-designed morphologies and prolonged bioluminescence performance. Among the selected amino acids (X = Glu, Lys, Leu, Phe), Cys(SEt)-Lys-CBT (CKCBT) exhibits the superior long-lasting bioluminescence signal (up to 72h) and good biocompatibility. This study demonstrates the potential of amino-acid-encoded supramolecular self-assembly as a convenient and effective method for developing BLI probes for long-term biological tracking and disease imaging.

Liquid Biopsy in Cancer Diagnosis-Treatment Methodology and Applications

The comprehensive review of liquid biopsy as a cutting-edge, non-invasive technique for cancer detection, diagnosis, and monitoring may be found in the document “Liquid Biopsy in Cancer Diagnosis – Treatment Methodology and Applications.” Via liquid biopsy, tumor-related indicators present in physiological fluids such as blood, urine, and saliva are examined, including exosomes, circulating tumor cells (CTCs), and circulating tumor DNA (ctDNA). Liquid biopsy enables regular, real-time sampling of tumor dynamics, in contrast to standard tissue biopsies, which are invasive and restricted to particular tumor regions. This renders it a potent instrument for the identification of cancer mutations and the tracking of tumor progression, particularly in cases of metastatic or elusive cancers. Technological developments like digital droplet PCR (ddPCR) and next-generation sequencing (NGS) have greatly improved the sensitivity and precision of ctDNA detection, making it possible to identify unusualexceptionally rare genetic mutations. This enhances the efficacy of tailored cancer treatment and monitoring. The genetic heterogeneity of cancers is captured by liquid biopsies, providing information on several tumor subclones that may be missed by traditional biopsies. The document also describes the use of different body fluids for different forms of cancer, such as head and neck and urological tumors, such as saliva, urine, and blood. Blood is the most often utilized medium for tracking genetic changes due to its ease of accessibility. Saliva and urine are useful in the detection of head and neck malignancies and bladder cancer, respectively. Liquid biopsy has several advantages, but it also has drawbacks, such as the requirement for standardized procedures, high prices, and differences in sensitivity and specificity between platforms.In order to overcome these obstacles and integrate liquid biopsy into standard clinical practice, the paper emphasizes the significance of more research and development. This will improve patient outcomes, early cancer detection, and personalized treatment.

Acoustic Feature Extraction Based on Wavelet Transform for Industrial Induction Motor-Driven Belt Conveyor Condition Monitoring

The industrial induction motor-driven belt conveyor is an essential component in manufacturing facilities. Any unexpected shutdown can lead to significant disruptions, resulting in financial losses amounting to thousands of dollars per hour. Unfortunately, efficient mechanisms for monitoring the conveyor's condition are often lacking. Therefore, it is crucial to ensure early, precise, and effective detection of malfunctions in belt conveyors. This necessitates the identification of distinctive anomalies stemming from initial damage to the rotating machinery and motor components. This paper presents a non-invasive acoustic monitoring technique designed specifically for industrial belt conveyors. The method employed relies on wavelet transform-based feature extraction, offering notable advantages in terms of classification accuracy, time efficiency, and the quantity of feature vectors required for classifier training.

Emotion Detection from EEG Signals Using Machine Deep Learning Models.

Detecting emotions is a growing field aiming to comprehend and interpret human emotions from various data sources, including text, voice, and physiological signals. Electroencephalogram (EEG) is a unique and promising approach among these sources. EEG is a non-invasive monitoring technique that records the brain's electrical activity through electrodes placed on the scalp's surface. It is used in clinical and research contexts to explore how the human brain responds to emotions and cognitive stimuli. Recently, its use has gained interest in real-time emotion detection, offering a direct approach independent of facial expressions or voice. This is particularly useful in resource-limited scenarios, such as brain-computer interfaces supporting mental health. The objective of this work is to evaluate the classification of emotions (positive, negative, and neutral) in EEG signals using machine learning and deep learning, focusing on Graph Convolutional Neural Networks (GCNN), based on the analysis of critical attributes of the EEG signal (Differential Entropy (DE), Power Spectral Density (PSD), Differential Asymmetry (DASM), Rational Asymmetry (RASM), Asymmetry (ASM), Differential Causality (DCAU)). The electroencephalography dataset used in the research was the public SEED dataset (SJTU Emotion EEG Dataset), obtained through auditory and visual stimuli in segments from Chinese emotional movies. The experiment employed to evaluate the model results was "subject-dependent". In this method, the Deep Neural Network (DNN) achieved an accuracy of 86.08%, surpassing SVM, albeit with significant processing time due to the optimization characteristics inherent to the algorithm. The GCNN algorithm achieved an average accuracy of 89.97% in the subject-dependent experiment. This work contributes to emotion detection in EEG, emphasizing the effectiveness of different models and underscoring the importance of selecting appropriate features and the ethical use of these technologies in practical applications. The GCNN emerges as the most promising methodology for future research.

Flexible nanomaterial sensors for non-invasive health monitoring

According to the Center for Disease Control (CDC), diabetes is the eighth leading cause of death in the United States, while about 1 in 5 patients are unaware that they have been affected. Current detection methods are crude, using rudimentary pricking methods that require needles and being unconventional for continuous tracking, this strongly discourages people from early detection and management. Therefore, developing a non-invasive glucose monitoring technique is of utmost importance. The primary objective of this research is to address the limitations of existing glucose monitoring techniques through the development of nanomaterial sensors seamlessly integrated into wearable devices. A novel two-dimensional material, GeSe, is chosen for its flexibility and optical responsivity. Flexible polarimetric sensors are developed using a facile direct-transfer method. These sensors aim to provide accurate and non-invasive monitoring of blood glucose levels through measuring the light polarization after interaction with glucose molecules. To ensure reliability, the sensors are calibrated using glucose solutions with known concentrations. A machine-learning algorithm is developed to improve measurement accuracy. Rigorous testing of the nanomaterial sensors is conducted in comparison to traditional blood glucose monitoring devices, employing controlled studies that evaluated their effectiveness across different objects. This approach not only establishes the viability of nanomaterials in transforming health monitoring but also highlights their potential to significantly improve the accuracy and convenience of blood glucose monitoring for individuals managing diabetes. The integration of nanomaterial-based sensors into wearable devices represents a noteworthy stride towards enhancing the efficiency and accessibility of healthcare technologies.

Advancing Glucose Monitoring: A Near-Infrared Device for Continuous Invasive Assessment

Diabetes is a chronic disease that affects millions of people worldwide, with the number of newly diagnosed cases continuing to rise at an alarming rate. Patients diagnosed with diabetes require routine monitoring of their blood glucose levels, which is commonly done through invasive and minimally invasive techniques. However, these methods are often painful, prone to infection, and costly in the long run. Therefore, it is necessary to develop non-invasive, pain-free, affordable glucose monitoring techniques. Near-infrared spectroscopy has emerged as a promising technique among the non-invasive techniques for glucose monitoring, with several studies reporting its efficacy in detecting glucose concentration. This study designed and developed a near-infrared system based on an 1150nm wavelength for continuous non-invasive glucose monitoring. The system gave the output voltages that vary with increased glucose concentrations. It was ascertained from the Clark error grid analysis that the glucose concentration falls within region A and region B. This implies that the device is suitable for measuring glucose concentration non-invasively. As per Bland Altman's analysis, the majority of the data points were within both limits of agreement except one data point that was outside the upper limit of agreement, indicating 93% degree of accuracy, and the residual plot indicates that the regression model for the system is appropriate for predicting non-invasive glucose measurements.

Intestinal ultrasound for intestinal Behçet disease reflects endoscopic activity and histopathological findings.

Intestinal Behçet disease is typically associated with ileocecal punched-out ulcers and significant morbidity and mortality. Intestinal ultrasound is a noninvasive imaging technique for disease monitoring. However, no previous reports have compared intestinal ultrasound with endoscopic ulcer activity or histopathological findings for intestinal Behçet disease. We evaluated the usefulness of intestinal ultrasound for assessing the activity of ileocecal ulcers in intestinal Behçet disease. We retrospectively compared intestinal ultrasound findings with 73 corresponding endoscopic images and 6 resected specimens. The intestinal ultrasound findings were assessed for 7 parameters (bowel wall thickness, vascularity [evaluated using the modified Limberg score with color Doppler], bowel wall stratification, white-plaque sign [strong hyperechogenic lines or spots], mesenteric lymphadenopathy, extramural phlegmons, and fistulas), and endoscopic ulcer activity was classified into active, healing, and scar stages. Histopathological findings were evaluated by consensus among experienced pathologists. Bowel wall thickness (P< 0.001), vascularity (P< 0.001), loss of bowel wall stratification (P= 0.015), and white-plague sign (P= 0.013) were significantly exacerbated in the endoscopic active ulcer stage. Receiver operating characteristic curve analysis revealed that a bowel wall thickness of > 5.5 mm (sensitivity 89.7%, specificity 85.3%) was potentially useful for detecting active lesions. When compared with histopathological findings, an increase in bowel wall thickness reflected the ulcer marginal ridge, and the white-plaque sign reflected the ulcer bottom. Intestinal ultrasound is useful for monitoring intestinal ulcer activity in intestinal Behçet disease.

Accuracy of Noninvasive Intracranial Pressure Monitoring in Patients with Subarachnoid Hemorrhage

Abstract Background: Subarachnoid hemorrhage (SAH) can lead to elevated intracranial pressure (ICP), which poses significant risks of morbidity and mortality. Monitoring ICP is crucial for the early detection and management of these complications in patients with aneurysmal SAH (aSAH). Traditional invasive ICP monitoring techniques, while effective, carry risks associated with infection and other complications. The Schmidt method, a noninvasive ICP monitoring technique, has emerged as a potential alternative to provide reliable measurements without the inherent risks of invasive methods. Methods: From January 2018 to December 2023, the authors performed both invasive (intraventricular catheters) and noninvasive (Schmidt method) ICP monitoring in patients with aSAH and Hunt and Hess (H and H) scores of 3–5. Patients requiring emergent craniotomy, with serious medical comorbidities or coagulopathies, or with noncompliance were excluded from the study. The accuracy and efficacy of the Schmidt method were evaluated by comparing the results of noninvasive monitoring with the results of invasive ICP monitoring. Results: Out of 238 cases with aSAH referred to our neurosurgical center, 105 had H and H scores of 3–5. Noninvasive ICP monitoring using the Schmidt method was performed in 86 cases (48 females, 38 males; mean age 68 ± 8 years). The mean ICP measured by the invasive method was 15.2 ± 6.2 mmHg, whereas the mean ICP measured by the noninvasive method was 16.6 ± 5.4 mmHg. The calculated root mean square error was 3.6 mmHg, and the average Pearson correlation between the estimated and real waveforms was 0.96. Conclusion: The findings of this study suggest that the application of the Schmidt method is a reliable and accurate approach for the assessment of ICP in patients with SAH, potentially eliminating the need for invasive ICP monitoring and reducing the associated risks.

Research on predicting photosynthetic pigments in tomato seedling leaves based on near-infrared hyperspectral imaging and machine learning

Conventional chemical approaches could be limited in monitoring the concentration of pigments in plants in high volumes. To overcome these limitations, researchers often turn to non-invasive, high-throughput, and real-time monitoring techniques, such as spectroscopy and hyperspectral imaging, which allow for the assessment of pigment concentration in plants without the need for destructive sampling and offer the ability to monitor large volumes of plants efficiently. This research focused on the utilization of machine learning in conjunction with hyperspectral imaging to develop models for predicting the concentration of three pigments, namely chlorophyll-a, chlorophyll-b, and carotenoids, in tomato seedlings. The sample tomato seedlings were sourced from two distinct varieties: the wild type and the Long Hypocotyl-5-deficient (HY5) type. The spectral data were acquired using a near-infrared (NIR) camera with a spectral range spanning approximately 900–1700 nm. Machine learning algorithms such as partial least squares regression (PLSR) and extreme learning machine (ELM) were utilized to explore the latent relationship between hyperspectral information and chemical measurements. In addition, principal component analysis (PCA), independent component analysis (ICA), and competitive adaptive reweighted sampling (CARS) methods were used to extracted informative wavelengths from the reflectance spectrum. And a comprehensive analysis regarding to spectroscopy was conducted to investigate the validity and efficiency of the results of feature extraction. The ELM model demonstrated the highest effectiveness, achieving R2 values of 0.86, 0.83, and 0.83 for chlorophyll-a, chlorophyll-b, and carotenoids, respectively, on the test set. By integrating the predictive models with classifiers such as Logistic Regression, Support Vector Classifier (SVC), and K-nearest Neighbors (KNN), tomato seedlings were categorized into wild type and HY5 type. The findings showed that the proposed approach efficiently predicted the concentration of the pigments in tomato seedlings and explored the feasibility of using these results to identify tomato gene types.

Development of a passive wireless sensor for fluidic detection and characterization utilizing the PCB-based coplanar electrode (PCE) configuration

During the global economic development, there's a growing focus on healthcare, especially in the advancement of medical diagnostic technologies, with a significant emphasis on glucose level evaluation. Glucose biosensors, predominantly electrochemical, have evolved over four generations, with the first three being enzyme-based and known for sensitivity and cost-effectiveness, albeit with limitations due to environmental susceptibility and reliance on enzyme activity. Recent advancements in non-invasive blood glucose monitoring, utilizing optical, microwave, and electrochemical techniques, offer diverse benefits without tissue penetration. Among these, impedance sensing stands out due to its flexibility and integration capability in handheld devices. This study proposes a wireless passive impedance method leveraging the inductor-capacitor (LC) sensing technique and PCB (Printed Circuit Board)-based coplanar electrode (PCE) configuration for fluidic sample detection. The proposed system integrates a two-coplanar-electrode layout with a square spiral inductor to assess fluidic conductivity and characterize various fluid types within samples. The effectiveness of this configuration was validated through experiments with NaCl and glucose solutions, confirming the feasibility of integrating PCB-based coplanar electrodes into conventional LC passive wireless sensing designs for fluidic detection and characterization.

Informing conservation management of the bilby (Macrotis lagotis) in the Pilbara: a review of research and future directions

For threatened species that occur across multi-use landscapes, a coordinated cross-tenure management approach is desirable to achieve long-term conservation outcomes. To provide a comprehensive understanding of the progress towards achieving conservation priorities for the greater bilby (Macrotis lagotis) in the Pilbara region of Western Australia (WA), and inform ongoing research and funding investment, we review a research program that has focused on the species over the past decade. Priorities were initially established at a stakeholder workshop in 2013, and research targeting key areas has greatly enhanced ecological knowledge of the bilby in the Pilbara and elsewhere in WA. Highlights include the development of a non-invasive scat DNA-based abundance monitoring technique, an improved understanding of habitat use and diet, including the importance of Acacia spp. that host root-dwelling larvae, and the threat posed by introduced predators. Proposed future research directions focus on better understanding habitat requirements and the association between key food resources and fire, evaluating genetic structure and gene flow at the landscape-scale and examining threat interactions. Management to mitigate a range of threats including introduced predators, grazing livestock/feral herbivores and fire is recommended, with consideration for an integrated approach.

Correlation of salivary hs-CRP With conventional cardiovascular risk markers and hba1c in prediabetic subjects

Prediabetes has attracted considerable attention because it increases the risk of developing diabetes and cardiovascular disease. Prediabetes is also associated with obesity and dyslipidemia. Chronic low-grade inflammation is a crucial element in the clinical course of metabolic syndrome, and cardiovascular and cerebrovascular diseases. C-Reactive Protein (CRP) is a biomarker of inflammation that tends to increase in patients with diabetes and prediabetes. Most serological markers of diabetes are detected using invasive techniques that cause anxiety and pain. Therefore, the use of noninvasive techniques for frequent biomarker monitoring is necessary. Salivary diagnostics is an upcoming field for sensitive biomarker detection, such as hs-CRP. HbA1C is a valuable glycemic risk marker and has proven to be a reliable test of time. Both HbA1C and CRP play pivotal roles in diabetes and prediabetes. hs-CRP is a measure of CRP level with greater accuracy. The lower limit of its measurement is 0.01 mg/L and the measurement is more than100 times as sensitive than the CRP measurement (lower limit 5 mg/L). Therefore, our study aimed to evaluate and establish a relationship between these factors in prediabetics. Since metabolic syndrome and prediabetes are the cause and effect of each other, correlating anthropometric measurements and lipid profile with HbA1C and hs-CRP will be our focus.

Non-Invasive Fetal Well-Being Monitoring Approaches: A Mini Review on Fetal Signal Separation Techniques

Fetal signal separation is vital in producing an accurate interpretation of the health condition of a fetal. In the context of a non-invasive fetal monitoring approach, the signals are acquired from the abdomen of pregnant women. As a result, a mix of maternal and fetal signals is obtained. These maternal and fetal signals are vague, as both signals are interchanged during the signal acquisition stage. Since the signals are overlapped, a signal separation technique must be employed to process the fetal signal for further analysis. This paper presents published studies on applying signal processing techniques involving fetal signal separation. These papers are obtained through a strategy known as the PRISMA technique. The online databases include ACM, Emerald Publishing, IEEE Explore Digital Library, Science Direct, Scopus, and Springer, with published years spanning from 2018 until 2022. Numerous separation techniques were found, such as adaptive filtering, blind source separation (BSS), and alternative approaches. Issues on the existing methods for fetal signal separation are discussed. In addition, the limitations and drawbacks of the research work involving existing fetal signal separation are reviewed in the paper. The potential direction of future research in this field is addressed as well. Based on this mini-review, it can be concluded that noise and ambiguity can still occur in the extracted fetal signals, even when signal processing techniques are applied. In the future, deep learning would be accommodating in improving the efficiency of extracting fetal signals obtained from the non-invasive fetal well-being monitoring technique. Meanwhile, apart from fetal heart rate (fHR) detection, fetal hypoxia can also be another important focus of study for improving fetal well-being monitoring.

Non-invasive Techniques for Muscle Fatigue Monitoring: A Comprehensive Survey

Muscle fatigue represents a complex physiological and psychological phenomenon that impairs physical performance and increases the risks of injury. It is important to continuously monitor fatigue levels for early detection and management of fatigue. The detection and classification of muscle fatigue also provide important information in human-computer interactions (HMI), sports injuries and performance, ergonomics, and prosthetic control. With this purpose in mind, this review first provides an overview of the mechanisms of muscle fatigue and its biomarkers and further enumerates various non-invasive techniques commonly used for muscle fatigue monitoring and detection in the literature, including electromyogram (EMG), which records the muscle electrical activity during muscle contractions, mechanomyogram (MMG), which records vibration signals of muscle fibers, near-infrared spectroscopy (NIRS), which measures the amount of oxygen in the muscle, ultrasound (US), which records signals of muscle deformation during muscle contractions. This review also introduces the principle and mechanism, parameters used for fatigue detection, application in fatigue detection, and advantages and disadvantages of each technology in detail. To conclude, the limitations/challenges that need to be addressed for future research in this area are presented.

Ensemble Deep Learning Models for Heart Failure Prediction from Real Healthcare Records

Cardiovascular diseases (CVDs) remain the leading cause of death globally, necessitating advancements in non-invasive and efficient monitoring techniques. "Retinabeat" proposes a novel approach to CVD monitoring by leveraging the diagnostic potential of retinal vascular patterns. Utilizing state-of-the-art retinal imaging combined with the VGG19 convolutional neural network, this system aims to detect and analyze subtle changes in the retinal vasculature that correlate with cardiovascular health. Preliminary studies suggest that these retinal indicators can serve as reliable markers of cardiovascular anomalies, providing a non-invasive, cost-effective, and readily accessible method for early detection and ongoing management of heart health. This paper details the development and validation of the Retinabeat system, evaluates its performance against traditional methods, and discusses the implications of integrating advanced image processing technologies in routine cardiovascular care. The outcomes highlight the system’s potential to enhance predictive diagnostics and personalize health monitoring, paving the way for broader applications in telemedicine and preventive healthcare. Keywords— Cardiovascular diseases, VGG19, telemedicine, Retinal images