A machine-learning approach for stress detection using wearable sensors in free-living environments.

Stress is a psychological condition resulting from the body's response to challenging situations, which can negatively impact physical and mental health if experienced over prolonged periods. Early detection of stress is crucial to prevent chronic health problems. Wearable sensors offer an effective solution for continuous and real-time stress monitoring due to their non-intrusive nature and ability to monitor vital signs, e.g., heart rate and activity. Typically, most existing research has focused on data collected in controlled environments. Yet, our study aims to propose a machine learning-based approach for detecting stress in a free-living environment using wearable sensors. We utilized the SWEET dataset, which includes data from 240 subjects collected via electrocardiography (ECG), skin temperature (ST), and skin conductance (SC). We assessed four machine learning models, i.e., K-Nearest Neighbors (KNN), Support Vector Classification (SVC), Decision Tree (DT), Random Forest (RF), and XGBoost (XGB) in four different settings. This study evaluates the performance of various machine learning models for stress classification using the SWEET dataset. The analysis included two binary classification scenarios (with and without SMOTE) and two multi-class classification scenarios (with and without SMOTE). The Random Forest model demonstrated superior performance in the binary classification without SMOTE, achieving an accuracy of 98.29 % and an F1-score of 97.89 %. For binary classification with SMOTE, the K-Nearest Neighbors model performed best, with an accuracy of 95.70 % and an F1-score of 95.70 %. In the three-level classification without SMOTE, the Random Forest model again excelled, achieving an accuracy of 97.98 % and an F1-score of 97.22 %. For three-level classification with SMOTE, XGBoost showed the highest performance, with an accuracy and F1-score of 98.98 %. These results highlight the effectiveness of different models under various conditions, emphasizing the importance of model selection and preprocessing techniques in enhancing classification performance.

Optical-coherence-tomography-based deep-learning scatterer-density estimator using physically accurate noise model.

We demonstrate a deep-learning-based scatterer density estimator (SDE) that processes local speckle patterns of optical coherence tomography (OCT) images and estimates the scatterer density behind each speckle pattern. The SDE is trained using large quantities of numerically simulated OCT images and their associated scatterer densities. The numerical simulation uses a noise model that incorporates the spatial properties of three types of noise, i.e., shot noise, relative-intensity noise, and non-optical noise. The SDE's performance was evaluated numerically and experimentally using two types of scattering phantom and in vitro tumor spheroids. The results confirmed that the SDE estimates scatterer densities accurately. The estimation accuracy improved significantly when compared with our previous deep-learning-based SDE, which was trained using numerical speckle patterns generated from a noise model that did not account for the spatial properties of noise.

Neural-network based high-speed volumetric dynamic optical coherence tomography.

We demonstrate deep-learning neural network (NN)-based dynamic optical coherence tomography (DOCT), which generates high-quality logarithmic-intensity-variance (LIV) DOCT images from only four OCT frames. The NN model is trained for tumor spheroid samples using a customized loss function: the weighted mean absolute error. This loss function enables highly accurate LIV image generation. The fidelity of the generated LIV images to the ground truth LIV images generated using 32 OCT frames is examined via subjective image observation and statistical analysis of image-based metrics. Fast volumetric DOCT imaging with an acquisition time of 6.55 s/volume is demonstrated using this NN-based method.

AI-based tool for early detection of Alzheimer's disease.

In the context of Alzheimer's disease (AD), timely identification is paramount for effective management, acknowledging its chronic and irreversible nature, where medications can only impede its progression. Our study introduces a holistic solution, leveraging the hippocampus and the VGG16 model with transfer learning for early AD detection. The hippocampus, a pivotal early affected region linked to memory, plays a central role in classifying patients into three categories: cognitively normal (CN), representing individuals without cognitive impairment; mild cognitive impairment (MCI), indicative of a subtle decline in cognitive abilities; and AD, denoting Alzheimer's disease. Employing the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, our model undergoes training enriched by advanced image preprocessing techniques, achieving outstanding accuracy (testing 98.17 %, validation 97.52 %, training 99.62 %). The strategic use of transfer learning fortifies our competitive edge, incorporating the hippocampus approach and, notably, a progressive data augmentation technique. This innovative augmentation strategy gradually introduces augmentation factors during training, significantly elevating accuracy and enhancing the model's generalization ability. The study emphasizes practical application with a user-friendly website, empowering radiologists to predict class probabilities, track disease progression, and visualize patient images in both 2D and 3D formats, contributing significantly to the advancement of early AD detection.

Erratum: Deep convolutional neural networks-based scatterer density and resolution estimators in optical coherence tomography: erratum.

[This corrects the article on p. 168 in vol. 13, PMID: 35154862.].

Label-free visualization and quantification of the drug-type-dependent response of tumor spheroids by dynamic optical coherence tomography.

We demonstrate label-free dynamic optical coherence tomography (D-OCT)-based visualization and quantitative assessment of patterns of tumor spheroid response to three anti-cancer drugs. The study involved treating human breast adenocarcinoma (MCF-7 cell-line) with paclitaxel (PTX), tamoxifen citrate (TAM), and doxorubicin (DOX) at concentrations of 0 (control), 0.1, 1, and 10 µM for 1, 3, and 6 days. In addition, fluorescence microscopy imaging was performed for reference. The D-OCT imaging was performed using a custom-built OCT device. Two algorithms, namely logarithmic intensity variance (LIV) and late OCT correlation decay speed (OCDS[Formula: see text]) were used to visualize the tissue dynamics. The spheroids treated with 0.1 and 1 µM TAM appeared similar to the control spheroid, whereas those treated with 10 µM TAM had significant structural corruption and decreasing LIV and OCDS[Formula: see text] over treatment time. The spheroids treated with PTX had decreasing volumes and decrease of LIV and OCDS[Formula: see text] signals over time at most PTX concentrations. The spheroids treated with DOX had decreasing and increasing volumes over time at DOX concentrations of 1 and 10 µM, respectively. Meanwhile, the LIV and OCDS[Formula: see text] signals decreased over treatment time at all DOX concentrations. The D-OCT, particularly OCDS[Formula: see text], patterns were consistent with the fluorescence microscopic patterns. The diversity in the structural and D-OCT results among the drug types and among the concentrations are explained by the mechanisms of the drugs. The presented results suggest that D-OCT is useful for evaluating the difference in the tumor spheroid response to different drugs and it can be a useful tool for anti-cancer drug testing.

Contactless remote monitoring of sleep: evaluating the feasibility of an under-mattress sensor mat in a real-life deployment.

Sleep is so important, particularly for the elderly. The lack of sleep may increase the risk of cognitive decline. Similarly, it may also increase the risk of Alzheimer's disease. Nonetheless, many people underestimate the importance of getting enough rest and sleep. In-laboratory polysomnography is the gold-standard method for assessing the quality of sleep. This method is considered impractical in the clinical environment, seen as labour-intensive and expensive owing to its specialised equipment, leading to long waiting lists. Hence, user-friendly (remote and non-intrusive) devices are being developed to help patients monitor their sleep at home. In this paper, we first discuss commercially-available non-wearable devices that measure sleep, in which we highlight the features associated with each device, including sensor type, interface, outputs, dimensions, power supply, and connectivity. Second, we evaluate the feasibility of a non-wearable device in a free-living environment. The deployed device comprises a sensor mat with an integrated micro-bending multimode fibre. Raw sensor data were gathered from five senior participants living in a senior activity centre over a few to several weeks. We were able to analyse the participants' sleep quality using various sleep parameters deduced from the sensor mat. These parameters include the wake-up time, bedtime, the time in bed, nap time. Vital signs, namely heart rate, respiratory rate, and body movements, were also reported to detect abnormal sleep patterns. We have employed pre-and post-surveys reporting each volunteer's sleep hygiene to confirm the proposed system's outcomes for detecting the various sleep parameters. The results of the system were strongly correlated with the surveys for reporting each sleep parameter. Furthermore, the system proved to be highly effective in detecting irregular patterns that occurred during sleep.

Renal tubular function and morphology revealed in kidney without labeling using three-dimensional dynamic optical coherence tomography.

Renal tubule has distinct metabolic features and functional activity that may be altered during kidney disease. In this paper, we present label-free functional activity imaging of renal tubule in normal and obstructed mouse kidney models using three-dimensional (3D) dynamic optical coherence tomography (OCT) ex vivo. To create an obstructed kidney model, we ligated the ureter of the left kidney for either 7 or 14 days. Two different dynamic OCT (DOCT) methods were implemented to access the slow and fast activity of the renal tubules: a logarithmic intensity variance (LIV) method and a complex-correlation-based method. Three-dimensional DOCT data were acquired with a 1.3 [Formula: see text]m swept-source OCT system and repeating raster scan protocols. In the normal kidney, the renal tubule appeared as a convoluted pipe-like structure in the DOCT projection image. Such pipe-like structures were not observed in the kidneys subjected to obstruction of the ureter for several days. Instead of any anatomical structures, a superficial high dynamics appearance was observed in the perirenal cortex region of the obstructed kidneys. These findings suggest that volumetric LIV can be used as a tool to investigate kidney function during kidney diseases.

Label-free drug response evaluation of human derived tumor spheroids using three-dimensional dynamic optical coherence tomography.

This study aims at demonstrating label-free drug-response-patterns assessment of different tumor spheroids and drug types by dynamic optical coherence tomography (D-OCT). The study involved human breast cancer (MCF-7) and colon cancer (HT-29) spheroids. The MCF-7 and HT-29 spheroids were treated with paclitaxel (Taxol; PTX) and the active metabolite of irinotecan SN-38, respectively. The drugs were applied with 0 (control), 0.1, 1, and 10 µM concentrations and the treatment durations were 1, 3, and 6 days. A swept-source OCT microscope equipped with a repeated raster scanning protocol was used to scan the spheroids. Logarithmic intensity variance (LIV) and late OCT correlation decay speed (OCDS[Formula: see text]) algorithms were used to visualize the tumor spheroid dynamics. LIV and OCDS[Formula: see text] images visualized different response patterns of the two types of spheroids. In addition, spheroid morphology, LIV, and OCDS[Formula: see text] quantification showed different time-courses among the spheroid and drug types. These results may indicate different action mechanisms of the drugs. The results showed the feasibility of D-OCT for the evaluation of drug response patterns of different cell spheroids and drug types and suggest that D-OCT can perform anti-cancer drug testing.

Theoretical model for en face optical coherence tomography imaging and its application to volumetric differential contrast imaging.

A new formulation of the lateral imaging process of point-scanning optical coherence tomography (OCT) and a new differential contrast method designed by using this formulation are presented. The formulation is based on a mathematical sample model called the dispersed scatterer model (DSM), in which the sample is represented as a material with a spatially slowly varying refractive index and randomly distributed scatterers embedded in the material. It is shown that the formulation represents a meaningful OCT image and speckle as two independent mathematical quantities. The new differential contrast method is based on complex signal processing of OCT images, and the physical and numerical imaging processes of this method are jointly formulated using the same theoretical strategy as in the case of OCT. The formula shows that the method provides a spatially differential image of the sample structure. This differential imaging method is validated by measuring in vivo and in vitro samples.

Label-free intratissue activity imaging of alveolar organoids with dynamic optical coherence tomography.

An organoid is a three-dimensional (3D) in vitro cell culture emulating human organs. We applied 3D dynamic optical coherence tomography (DOCT) to visualize the intratissue and intracellular activities of human induced pluripotent stem cells (hiPSCs)-derived alveolar organoids in normal and fibrosis models. 3D DOCT data were acquired with an 840-nm spectral domain optical coherence tomography with axial and lateral resolutions of 3.8 µm (in tissue) and 4.9 µm, respectively. The DOCT images were obtained by the logarithmic-intensity-variance (LIV) algorithm, which is sensitive to the signal fluctuation magnitude. The LIV images revealed cystic structures surrounded by high-LIV borders and mesh-like structures with low LIV. The former may be alveoli with a highly dynamics epithelium, while the latter may be fibroblasts. The LIV images also demonstrated the abnormal repair of the alveolar epithelium.

Label-free metabolic imaging of non-alcoholic-fatty-liver-disease (NAFLD) liver by volumetric dynamic optical coherence tomography.

Label-free metabolic imaging of non-alcoholic fatty liver disease (NAFLD) mouse liver is demonstrated ex vivo by dynamic optical coherence tomography (OCT). The NAFLD mouse is a methionine choline-deficient (MCD)-diet model, and two mice fed the MCD diet for 1 and 2 weeks are involved in addition to a normal-diet mouse. The dynamic OCT is based on repeating raster scan and logarithmic intensity variance (LIV) analysis that enables volumetric metabolic imaging with a standard-speed (50,000 A-lines/s) OCT system. Metabolic domains associated with lipid droplet accumulation and inflammation are clearly visualized three-dimensionally. Particularly, the normal-diet liver exhibits highly metabolic vessel-like structures of peri-vascular hepatic zones. The 1-week MCD-diet liver shows ring-shaped highly metabolic structures formed with lipid droplets. The 2-week MCD-diet liver exhibits fragmented vessel-like structures associated with inflammation. These results imply that volumetric LIV imaging is useful for visualizing and assessing NAFLD abnormalities.

Non-destructive characterization of adult zebrafish models using Jones matrix optical coherence tomography.

The zebrafish is a valuable vertebrate animal model in pre-clinical cancer research. A Jones matrix optical coherence tomography (JM-OCT) prototype operating at 1310 nm and an intensity-based spectral-domain OCT setup at 840 nm were utilized to investigate adult wildtype and a tumor-developing zebrafish model. Various anatomical features were characterized based on their inherent scattering and polarization signature. A motorized translation stage in combination with the JM-OCT prototype enabled large field-of-view imaging to investigate adult zebrafish in a non-destructive way. The diseased animals exhibited tumor-related abnormalities in the brain and near the eye region. The scatter intensity, the attenuation coefficients and local polarization parameters such as the birefringence and the degree of polarization uniformity were analyzed to quantify differences in tumor versus control regions. The proof-of-concept study in a limited number of animals revealed a significant decrease in birefringence in tumors found in the brain and near the eye compared to control regions. The presented work showed the potential of OCT and JM-OCT as non-destructive, high-resolution, and real-time imaging modalities for pre-clinical research based on zebrafish.

Deep convolutional neural network-based scatterer density and resolution estimators in optical coherence tomography.

We present deep convolutional neural network (DCNN)-based estimators of the tissue scatterer density (SD), lateral and axial resolutions, signal-to-noise ratio (SNR), and effective number of scatterers (ENS, the number of scatterers within a resolution volume). The estimators analyze the speckle pattern of an optical coherence tomography (OCT) image in estimating these parameters. The DCNN is trained by a large number (1,280,000) of image patches that are fully numerically generated in OCT imaging simulation. Numerical and experimental validations were performed. The numerical validation shows good estimation accuracy as the root mean square errors were 0.23%, 3.65%, 3.58%, 3.79%, and 6.15% for SD, lateral and axial resolutions, SNR, and ENS, respectively. The experimental validation using scattering phantoms (Intralipid emulsion) shows reasonable estimations. Namely, the estimated SDs were proportional to the Intralipid concentrations, and the average estimation errors of lateral and axial resolutions were 1.36% and 0.68%, respectively. The scatterer density estimator was also applied to an in vitro tumor cell spheroid, and a reduction in the scatterer density during cell necrosis was found.

Multicontrast investigation of in vivo wildtype zebrafish in three development stages using polarization-sensitive optical coherence tomography.

SIGNIFICANCE: The scattering and polarization characteristics of various organs of in vivo wildtype zebrafish in three development stages were investigated using a non-destructive and label-free approach. The presented results showed a promising first step for the usability of Jones-matrix optical coherence tomography (JM-OCT) in zebrafish-based research. AIM: We aim to visualize and quantify the scatter and polarization signatures of various zebrafish organs for larvae, juvenile, and young adult animals in vivo in a non-invasive and label-free way. APPROACH: A custom-built polarization-sensitive JM-OCT setup in combination with a motorized translation stage was utilized to investigate live zebrafish. Depth-resolved scattering (intensity and attenuation coefficient) and polarization (birefringence and degree of polarization uniformity) properties were analyzed. OCT angiography (OCT-A) was utilized to investigate the vasculature label-free and non-destructively. RESULTS: The scatter and polarization signatures of the zebrafish organs such as the eye, gills, and muscles were investigated. The attenuation coefficient and birefringence changes between 1- and 2-month-old animals were evaluated in selected organs. OCT-A revealed the vasculature of in vivo larvae and juvenile zebrafish in a label-free manner. CONCLUSIONS: JM-OCT offers a rapid, label-free, non-invasive, tissue specific, and three-dimensional imaging tool to investigate in vivo processes in zebrafish in various development stages.

Three-dimensional dynamics optical coherence tomography for tumor spheroid evaluation.

We present a completely label-free three-dimensional (3D) optical coherence tomography (OCT)-based tissue dynamics imaging method for visualization and quantification of the metabolic and necrotic activities of tumor spheroid. Our method is based on a custom 3D scanning protocol that is designed to capture volumetric tissue dynamics tomography images only in a few tens of seconds. The method was applied to the evaluation of a tumor spheroid. The time-course viability alteration and anti-cancer drug response of the spheroid were visualized qualitatively and analyzed quantitatively. The similarity between the OCT-based dynamics images and fluorescence microscope images was also demonstrated.

Label-free functional and structural imaging of liver microvascular complex in mice by Jones matrix optical coherence tomography.

We demonstrate label-free imaging of the functional and structural properties of microvascular complex in mice liver. The imaging was performed by a custom-built Jones-matrix based polarization sensitive optical coherence tomography (JM-OCT), which is capable of measuring tissue's attenuation coefficient, birefringence, and tiny tissue dynamics. Two longitudinal studies comprising a healthy liver and an early fibrotic liver model were performed. In the healthy liver, we observed distinctive high dynamics beneath the vessel at the initial time point (0 h) and reappearance of high dynamics at 32-h time point. In the early fibrotic liver model, we observed high dynamics signal that reveals a clear network vascular structure by volume rendering. Longitudinal time-course imaging showed that these high dynamics signals faded and decreased over time.

Optical coherence tomography-based tissue dynamics imaging for longitudinal and drug response evaluation of tumor spheroids.

We present optical coherence tomography (OCT)-based tissue dynamics imaging method to visualize and quantify tissue dynamics such as subcellular motion based on statistical analysis of rapid-time-sequence OCT signals at the same location. The analyses include logarithmic intensity variance (LIV) method and two types of OCT correlation decay speed analysis (OCDS). LIV is sensitive to the magnitude of the signal fluctuations, while OCDSs including early- and late-OCDS (OCDS e and OCDS l , respectively) are sensitive to the fast and slow tissue dynamics, respectively. These methods were able to visualize and quantify the longitudinal necrotic process of a human breast adenocarcinoma spheroid and its anti-cancer drug response. Additionally, the effects of the number of OCT signals and the total acquisition time on dynamics imaging are examined. Small number of OCT signals, e.g., five or nine suffice for dynamics imaging when the total acquisition time is suitably long.

A New Approach for Detecting Sleep Apnea Using a Contactless Bed Sensor: Comparison Study.

BACKGROUND: At present, there is an increased demand for accurate and personalized patient monitoring because of the various challenges facing health care systems. For instance, rising costs and lack of physicians are two serious problems affecting the patient's care. Nonintrusive monitoring of vital signs is a potential solution to close current gaps in patient monitoring. As an example, bed-embedded ballistocardiogram (BCG) sensors can help physicians identify cardiac arrhythmia and obstructive sleep apnea (OSA) nonintrusively without interfering with the patient's everyday activities. Detecting OSA using BCG sensors is gaining popularity among researchers because of its simple installation and accessibility, that is, their nonwearable nature. In the field of nonintrusive vital sign monitoring, a microbend fiber optic sensor (MFOS), among other sensors, has proven to be suitable. Nevertheless, few studies have examined apnea detection. OBJECTIVE: This study aims to assess the capabilities of an MFOS for nonintrusive vital signs and sleep apnea detection during an in-lab sleep study. Data were collected from patients with sleep apnea in the sleep laboratory at Khoo Teck Puat Hospital. METHODS: In total, 10 participants underwent full polysomnography (PSG), and the MFOS was placed under the patient's mattress for BCG data collection. The apneic event detection algorithm was evaluated against the manually scored events obtained from the PSG study on a minute-by-minute basis. Furthermore, normalized mean absolute error (NMAE), normalized root mean square error (NRMSE), and mean absolute percentage error (MAPE) were employed to evaluate the sensor capabilities for vital sign detection, comprising heart rate (HR) and respiratory rate (RR). Vital signs were evaluated based on a 30-second time window, with an overlap of 15 seconds. In this study, electrocardiogram and thoracic effort signals were used as references to estimate the performance of the proposed vital sign detection algorithms. RESULTS: For the 10 patients recruited for the study, the proposed system achieved reasonable results compared with PSG for sleep apnea detection, such as an accuracy of 49.96% (SD 6.39), a sensitivity of 57.07% (SD 12.63), and a specificity of 45.26% (SD 9.51). In addition, the system achieved close results for HR and RR estimation, such as an NMAE of 5.42% (SD 0.57), an NRMSE of 6.54% (SD 0.56), and an MAPE of 5.41% (SD 0.58) for HR, whereas an NMAE of 11.42% (SD 2.62), an NRMSE of 13.85% (SD 2.78), and an MAPE of 11.60% (SD 2.84) for RR. CONCLUSIONS: Overall, the recommended system produced reasonably good results for apneic event detection, considering the fact that we are using a single-channel BCG sensor. Conversely, satisfactory results were obtained for vital sign detection when compared with the PSG outcomes. These results provide preliminary support for the potential use of the MFOS for sleep apnea detection.

Comparative analysis of heart failure management in two different media rural versus urban. (ALBAPIC Study). / Análisis comparativo del manejo de la insuficiencia cardiaca en 2medios diferentes: rural versus urbano. (Estudio ALBAPIC).

OBJECTIVE: To know the epidemiological, clinical and therapeutic characteristics of patients with a diagnosis of HF treated in primary care of 2Health Areas of Albacete, Zone 5 A (characteristics of the Urban Center) and Casas Ibañez (characteristics of the Rural Center) as well as to highlight The main differences between the two. METHOD: Descriptive and cross-sectional study, corresponding to the first phase of the ALBAPIC study. All patients in the area who met the inclusion criteria have been registered: Having a diagnosis of HF in the TURRIANO program (consultation program in Primary Care of Castilla la Mancha). Demographic-anthropometric and clinical characteristics, analytical data, complementary diagnostic examinations, therapeutic guidelines and hospitalizations were recorded for 12 months prior to inclusion. A physical examination and electrocardiographic and biochemical controls were performed at the inclusion visit. RESULTS: 384 patients diagnosed with HF in both Health Zone (161 in urban areas and 223 in rural areas) have participated. Average age 82.24±10.51 years (81.24±9.59 years in urban areas and 83.37±11 years in rural areas with significant differences P<.005, 54.3% are women (54% in urban areas and 54.7% in rural areas) We have an incidence of CI of 1% in urban areas and 1.8% in rural areas. The prevalence of CVRF has that hypertension above all and dyslipidemia are the most frequent, with differences depending on the environment in which they live. In the rural environment there are higher rates of heart disease. Patients with HF have a high number of concomitant chronic diseases, being between 4 and 6 more than 60% of cases in the urban environment and between 1 and 4 in the rural environment. Approximately 14% also have an oncological disease in the urban environment compared to 21% in the rural. According to the exploration and analytical data, the main variables are acceptably controlled, the lipid parameters in the rural center being worse controlled. The average number of drugs prescribed by each patient was 6.3 in rural and 7.2 urban. As for the treatments they are taking, it is observed that diuretics and statins. CONCLUSIONS: There is an acceptable control of cardiovascular risk factors in both media, there being differences in the diagnostic methods and treatments used.