Brain microstructural changes already occur in the earliest phases of Alzheimer's disease (AD) as evidenced in diffusion magnetic resonance imaging (dMRI) literature. This study investigates the potential of the novel dMRI Apparent Measures Using Reduced Acquisitions (AMURA) as imaging markers for capturing such tissue modifications.Tract-based spatial statistics (TBSS) and support vector machines (SVMs) based on different measures were exploited to distinguish between amyloid-beta/tau negative (A[Formula: see text]-/tau-) and A[Formula: see text]+/tau+ or A[Formula: see text]+/tau- subjects. Moreover, eXplainable Artificial Intelligence (XAI) was used to highlight the most influential features in the SVMs classifications and to validate the results by seeing the explanations' recurrence across different methods.TBSS analysis revealed significant differences between A[Formula: see text]-/tau- and other groups in line with the literature. The best SVM classification performance reached an accuracy of 0.73 by using advanced measures compared to more standard ones. Moreover, the explainability analysis suggested the results' stability and the central role of the cingulum to show early sign of AD.By relying on SVM classification and XAI interpretation of the outcomes, AMURA indices can be considered viable markers for amyloid and tau pathology. Clinical impact: This pre-clinical research revealed AMURA indices as viable imaging markers for timely AD diagnosis by acquiring clinically feasible dMR images, with advantages compared to more invasive methods employed nowadays.

Low-cost, portable RGB-D cameras with integrated motion tracking functionality enable easy-to-use 3D motion analysis without requiring expensive facilities and specialized personnel. However, the accuracy of existing systems is insufficient for most clinical applications, particularly when applied to children. In previous work, we developed an RGB-D camera-based motion tracking method and showed that it accurately captures body joint positions of children and young adults in 3D. In this study, the validity and accuracy of clinically relevant motion parameters that were computed from kinematics of our motion tracking method are evaluated in children and young adults.

This study introduces a novel system that can simulate diverse mechanical properties of the human chest to enhance the experience of CPR training by reflecting realistic chest conditions of patients.

The objective of this study was to develop a sound recognition-based cardiopulmonary resuscitation (CPR) training system that is accessible, cost-effective, easy-to-maintain and provides accurate CPR feedback. Beep-CPR, a novel device with accordion squeakers that emit high-pitched sounds during compression, was developed. The sounds emitted by Beep-CPR were recorded using a smartphone, segmented into 2-second audio fragments, and then transformed into spectrograms. A total of 6,065 spectrograms were generated from approximately 40 minutes of audio data, which were then randomly split into training, validation, and test datasets. Each spectrogram was matched with the depth, rate, and release velocity of the compression measured at the same time interval by the ZOLL X Series monitor/defibrillator. Deep learning models utilizing spectrograms as input were trained using transfer learning based on EfficientNet to predict the depth (Depth model), rate (Rate model), and release velocity (Recoil model) of compressions. Results: The mean absolute error (MAE) for the Depth model was 0.30 cm (95% confidence interval [CI]: 0.27-0.33). The MAE of the Rate model was 3.6/min (95% CI: 3.2-3.9). For the Recoil model, the MAE was 2.3 cm/s (95% CI: 2.1-2.5). External validation of the models demonstrated acceptable performance across multiple conditions, including the utilization of a newly-manufactured device, a fatigued device, and evaluation in an environment with altered spatial dimensions. We have developed a novel sound recognition-based CPR training system, that accurately measures compression quality during training. Significance: Beep-CPR is a cost-effective and easy-to-maintain solution that can improve the efficacy of CPR training by facilitating decentralized at-home training with performance feedback.

This research aims to extract human gait parameters from floor vibrations. The proposed approach provides an innovative methodology on occupant activity, contributing to a broader understanding of how human movements interact within their built environment.

Non-contact respiratory rate estimation (RR) is highly desirable for infants because of their sensitive skin. We propose a novel RGB video-based RR estimation method for infants in the neonatal intensive care unit (NICU) that can accurately measure the RR contact-less.

Rehabilitation devices, such as traditional rigid exoskeletons or exosuits, have been widely used to rehabilitate upper limb function post-stroke. In this paper, we have developed an exosuit with four degrees of freedom to enable users to involve more joints in the rehabilitation process. Additionally, a hybrid electroencephalogram-based (EEG-based) control approach has been developed to promote active user engagement and provide more control commands.The hybrid EEG-based control approach includes steady-state visual evoked potential (SSVEP) paradigm and motor imagery (MI) paradigm. Firstly, the rehabilitation movement was selected by SSVEP paradigm, and the multivariate variational mode decomposition (MVMD) and canonical correlation analysis (CCA) method was used for SSVEP EEG recognition; then, the motion intention was obtained by MI paradigm, and the convolutional neural network (CNN) and long short-term memory network (LSTM) were used to build a CNN-LSTM model for MI EEG recognition; finally, the recognition results were translated into control commands of Bowden cables to achieve multi-degree-of-freedom rehabilitation.Experimental results show that the average classification accuracy of the CNN-LSTM model reaches to 90.07% ± 2.23%, and the overall accuracy of the hybrid EEG-based control approach reaches to 85.26% ± 1.95%. The twelve subjects involved in the usability assessment demonstrated an average system usability scale (SUS) score of 81.25 ± 5.82. Additionally, four participants who underwent a 35-day rehabilitation training demonstrated an average 10.33% increase in range of motion (ROM) across 4 joints, along with a 11.35% increase in the average electromyography (EMG) amplitude of the primary muscle involved.The exosuit demonstrates good accuracy in control, exhibits favorable usability, and shows certain efficacy in multi-joint rehabilitation. Our study has taken into account the neuroplastic principles, aiming to achieve active user engagement while introducing additional degrees of freedom, offering novel ideas and methods for potential brain-computer interface (BCI)-based rehabilitation strategies and hardware development.Clinical impact: Our study presents an exosuit with four degrees of freedom for stroke rehabilitation, enabling multi-joint movement and improved motor recovery. The hybrid EEG-based control approach enhances active user engagement, offering a promising strategy for more effective and user-driven rehabilitation, potentially improving clinical outcomes.Clinical and Translational Impact Statement: By developing an exosuit and a hybrid EEG-based control approach, this study enhances stroke rehabilitation through better user engagement and multi-joint capabilities. These innovations consider neuroplasticity principles, integrating rehabilitation theory with rehabilitation device.

Delirium, an acute confusional state, affects 20-80% of patients in Intensive Care Units (ICUs), one in three medically hospitalized patients, and up to 50% of all patients who have had surgery. Its development is associated with short- and long-term morbidity, and increased risk of death. Yet, we lack any rapid, objective, and automated method to diagnose delirium. Here, we detail the prospective deployment of a novel dual-camera contextual eye-tracking platform. We then use the data from this platform to contemporaneously classify delirium.

Nocturnal enuresis is a bothersome condition that affects many children and their caregivers. Post-voiding systems is of little value in training a child into a correct voiding routing while existing pre-voiding systems suffer from several practical limitations, such as cumbersome hardware, assuming individual bladder shapes being universal, and being sensitive to sensor placement error. Methods: A low-voltage ultrasound system with machine learning has been developed in estimating bladder filling status. A custom-made flexible 1D transducer array has been excited by low-voltage coded pulses with a pulse compression technique for an enhanced signal-to-noise ratio. In order to minimize the negative influence of possible transducer misplacement, a multiple-position training strategy using machine learning has been adopted in this work. Three popular classification methods, KNN, SVM and sparse coding, have been utilized to classify the acquired different volumes ranging from 100 ml to 300 ml into two categories: low volume and high volume. The low-volume category requires no further action while the high-volume category triggers an alarm to alert the child and caregiver. Results: When the sensor placement is ideal, i.e., the position of the practical sensor placement is on spot with the trained position, the precision and recall of the classification using sparse coding are [Formula: see text] and [Formula: see text], respectively. Even if the transducer array is misplaced by up to 4.5 mm away from the ideal location, the proposed system is able to maintain high classification accuracy (precision [Formula: see text] and recall [Formula: see text]). Category: Early/Pre-Clinical Research Clinical and Translational Impact: The proposed ultrasound sensor system for nocturnal enuresis is of significant clinical and translational value as it addresses two major issues that limit the wide adoption of similar devices. Firstly, it offers enhanced safety as the entire system has been implemented in the lowvoltage domain. Secondly, the system features ample tolerance to sensor misplacement while maintaining high classification accuracy. These features combined provide a much more user-friendly environment for children and their caregivers than existing devices.

Neuroimaging genetics represents a multivariate approach aimed at elucidating the intricate relationships between high-dimensional genetic variations and neuroimaging data. Predominantly, existing methodologies revolve around Sparse Canonical Correlation Analysis (SCCA), a framework we expand to 1) encompass multiple imaging modalities and 2) promote the simultaneous identification of structurally linked features across imaging modalities. The structurally linked brain regions were assessed using diffusion tensor imaging, which quantifies the presence of neuronal fibers, thereby grounding our approach in biologically well-founded prior knowledge within the SCCA model. In our proposed structurally linked SCCA framework, we leverage T1-weighted MRI and functional MRI (fMRI) time series data to delineate both the structural and functional characteristics of the brain. Genetic variations, specifically single nucleotide polymorphisms (SNPs), are also incorporated as a genetic modality. Validation of our methodology was conducted using a simulated dataset and large-scale normative data from the Human Connectome Project (HCP). Our approach demonstrated superior performance compared to existing methods on simulated data and revealed interpretable gene-imaging associations in the real dataset. Thus, our methodology lays the groundwork for elucidating the genetic underpinnings of brain structure and function, thereby providing novel insights into the field of neuroscience. Our code is available at https://github.com/mungegg.

Attention-deficit/hyperactivity disorder (ADHD) is a childhood-onset neurodevelopmental disorder with a prevalence ranging from 6.1 to 9.4%. The main symptoms of ADHD are inattention, hyperactivity, impulsivity, and even destructive behaviors that may have a long-term negative influence on learning performance or social relationships. Early diagnosis and treatment provide the best chance of reducing and managing symptoms. Currently, ADHD diagnosis relies on behavioral observations and ratings by clinicians and parents. Medical diagnosis of ADHD was reported to be delayed because of a global shortage of well-trained clinicians, the heterogeneous nature of ADHD, and combined comorbidities. Therefore, alternative ways to increase the efficiency of early diagnosis are needed. Previous studies used behavioral and neurophysiological data to assess patients with ADHD, yielding an accuracy range from 56.6% to 92%. Several factors were shown to affect the detection rate, including methods and tasks used and the number of electroencephalogram (EEG) channels. Given that children with ADHD have difficulty sustaining attention, in this study, we tested whether data from multiple tasks with different difficulties and prolonged experiment times can probe the levels of brain resources engaged during task performance and increase ADHD detection. Specifically, we proposed a Deep Neural Network-based (DNN) fusion model of multiple tasks to enhance the detection of ADHD.

Podocyte degenerative changes are common in various kidney diseases, and their accurate identification is crucial for pathologists to diagnose and treat such conditions. However, this can be a difficult task, and previous attempts to automate the identification of podocytes have not been entirely successful. To address this issue, this study proposes a novel approach that combines pathologists' expertise with an automated classifier to enhance the identification of podocytopathies. The study involved building a new dataset of renal glomeruli images, some with and others without podocyte degenerative changes, and developing a convolutional neural network (CNN) based classifier. The results showed that our automated classifier achieved an impressive 90.9% f-score. When the pathologists used as an auxiliary tool to classify a second set of images, the medical group's average performance increased significantly, from [Formula: see text]% to [Formula: see text]% of f-score. Fleiss' kappa agreement among the pathologists also increased from 0.59 to 0.83. Conclusion: These findings suggest that automating this task can bring benefits for pathologists to correctly identify images of glomeruli with podocyte degeneration, leading to improved individual accuracy while raising agreement in diagnosing podocytopathies. This approach could have significant implications for the diagnosis and treatment of kidney diseases. Clinical impact: The approach presented in this study has the potential to enhance the accuracy of medical diagnoses for detecting podocyte abnormalities in glomeruli, which serve as biomarkers for various glomerular diseases.

Advancements in integrated circuit (IC) technology have accelerated the miniaturization of body-worn sensors and systems, enabling long-term health monitoring. Wearable electrocardiogram (ECG), finger photoplethysmogram (PPG), and wrist-worn PPG have shown great success and significantly improved life quality. Chest-based PPG has the potential to extract multiple vital signs but requires ultra-high dynamic range (DR) IC to read out the small PPG signal among large respiration and artifacts inherent in daily life. This paper presents a dedicated high DR system for wearable chest PPG applications with a small form factor. The whole measurement system is integrated on a 20 cm2 PCB board. We have formulated a comprehensive evaluation protocol to validate the system with on-body chest PPG measurement in the workspace environment. First, chest PPG data was obtained from 6 adults and compared to data from a standard ECG patch. This system showed an average absolute deviation (AD) of 0.41 beats per minute, achieving > 99.53% heart rate (HR) accuracy. Second, chest PPG was recorded and compared to conventional PPG finger clip and PPG wristband, also showing > 98.6% HR matching and an absolute deviation in the standard deviation of NN intervals (SDNN) of < 12.8 ms for HRV monitoring within the protocol. Moreover, it successfully derives other vital parameters such as respiration rate and blood oxygen level (SpO2), showing the advancement among all these three reference modalities. This system can pave the way for new application areas, such as chest patches, to monitor chronic heart and respiratory diseases.

Vital signs are important indicators to evaluate the health status of patients. Channel state information (CSI) can sense the displacement of the chest wall caused by cardiorespiratory activity in a non-contact manner. Due to the influence of clutter, DC components, and respiratory harmonics, it is difficult to detect reliable heartbeat signals. To address this problem, this paper proposes a robust and novel method for simultaneously extracting breath and heartbeat signals using software defined radios (SDR). Specifically, we model and analyze the signal and propose singular value decomposition (SVD)-based clutter suppression method to enhance the vital sign signals. The DC is estimated and compensated by the circle fitting method. Then, the heartbeat signal and respiratory signal are obtained by the modified variational modal decomposition (VMD). The experimental results demonstrate that the proposed method can accurately separate the respiratory signal and the heartbeat signal from the filtered signal. The Bland-Altman analysis shows that the proposed system is in good agreement with the medical sensors. In addition, the proposed system can accurately measure the heart rate variability (HRV) within 0.5m. In summary, our system can be used as a preferred contactless alternative to traditional contact medical sensors, which can provide advanced patient-centered healthcare solutions.

Optimized deep brain stimulation (DBS) is fast becoming a therapy of choice for the treatment of Parkinson's disease (PD). However, the post-operative optimization (aimed at maximizing patient clinical benefits and minimizing adverse effects) of all possible DBS parameter settings using the standard-of-care clinical protocol requires numerous clinical visits, which substantially increases the time to optimization per patient (TPP), patient cost burden and limit the number of patients who can undergo DBS treatment. The TPP is further elongated in electrodes with stimulation directionality or in diseases with latency in clinical feedback. In this work, we proposed a deep learning and fMRI-based pipeline for DBS optimization that can potentially reduce the TPP from ~1 year to a few hours during a single clinical visit.

The equivalent electrical circuit approach is explored to improve a bioimpedance-based transducer for measuring the bioavailability of synthetic insulin already presented in previous studies. In particular, the electrical parameter most sensitive to the variation of insulin amount injected was identified. Eggplants were used to emulate human electrical behavior under a quasi-static assumption guaranteed by a very low measurement time compared to the estimated insulin absorption time. Measurements were conducted with the EVAL-AD5940BIOZ by applying a sinusoidal voltage signal with an amplitude of 100 mV and acquiring impedance spectra in the range [1-100] kHz. 14 units of insulin were gradually administered using a Lilly's Insulin Pen having a 0.4 cm long needle. Modified Hayden's model was adopted as a reference circuit and the electrical component modeling the extracellular fluids was found to be the most insulin-sensitive parameter. The trnasducer achieves a state-of-the-art sensitivity of 225.90 ml1. An improvement of 223 % in sensitivity, 44 % in deterministic error, 7 % in nonlinearity, and 42 % in reproducibility was achieved compared to previous experimental studies. The clinical impact of the transducer was evaluated by projecting its impact on a Smart Insulin Pen for real-time measurement of insulin bioavailability. The wide gain in sensitivity of the bioimpedance-based transducer results in a significant reduction of the uncertainty of the Smart Insulin Pen. Considering the same improvement in in-vivo applications, the uncertainty of the Smart Insulin Pen is decreased from [Formula: see text]l to [Formula: see text]l.Clinical and Translational Impact Statement: A Smart Insulin Pen based on impedance spectroscopy and equivalent electrical circuit approach could be an effective solution for the non-invasive and real-time measurement of synthetic insulin uptake after subcutaneous administration.

Children worldwide are becoming increasingly inactive, leading to significant wellness challenges. Initial findings from our research team indicate that robots could potentially provide a more effective approach (compared to other age-appropriate toys) for encouraging physical activity in children. However, the basis of this past work relied on either interactions with groups of children (making it challenging to isolate specific factors that influenced activity levels) or a preliminary version of results of the present study (which centered on just a single more exploratory method for assessing child movement). This paper delves into more controlled interactions involving a single robot and a child participant, while also considering observations over an extended period to mitigate the influence of novelty on the study outcomes. We discuss the outcomes of a two-month-long deployment, during which [Formula: see text] participants engaged with our custom robot, GoBot, in weekly sessions. During each session, the children experienced three different conditions: a teleoperated robot mode, a semi-autonomous robot mode, and a control condition in which the robot was present but inactive. Compared to our past related work, the results expanded our findings by confirming with greater clout (based on multiple data streams, including one more robust measure compared to the past related work) that children tended to be more physically active when the robot was active, and interestingly, there were no significant differences between the teleoperated and semi-autonomous modes in terms of our study measures. These insights can inform future applications of assistive robots in child motor interventions, including the guiding of appropriate levels of autonomy for these systems. This study demonstrates that incorporating robotic systems into play environments can boost physical activity in young children, indicating potential implementation in settings crafted to enhance children's physical movement.

Slow and deep breathing (SDB) is a relaxation technique that can increase vagal activity. Respiratory sinus arrhythmia (RSA) serves as an index of vagal function usually quantified by the high-frequency power of heart rate variability (HRV). However, the low breathing rate during SDB results in deviations when estimating RSA by HRV. Besides, the impact of the inspiration-expiration (I: E) ratio and guidelines ways (fixed breathing rate or intelligent guidance) on SDB is not yet clear. In our study, 30 healthy people (mean age = 26.5 years, 17 females) participated in three SDB modes, including 6 breaths per minute (bpm) with an I:E ratio of 1:1/ 1:2, and intelligent guidance mode (I:E ratio of 1:2 with guiding to gradually lower breathing rate to 6 bpm). Parameters derived from HRV, multimodal coupling analysis (MMCA), Poincaré plot, and detrended fluctuation analysis were introduced to examine the effects of SDB exercises. Besides, multiple machine learning methods were applied to classify breathing patterns (spontaneous breathing vs. SDB) after feature selection by max-relevance and min-redundancy. All vagal-activity markers, especially MMCA-derived RSA, statistically increased during SDB. Among all SDB modes, breathing at 6 bpm with a 1:1 I:E ratio activated the vagal function the most statistically, while the intelligent guidance mode had more indicators that still significantly increased after training, including SDRR and MMCA-derived RSA, etc. About the classification of breathing patterns, the Naive Bayes classifier has the highest accuracy (92.2%) with input features including LFn, CPercent, pNN50, [Formula: see text], SDRatio, [Formula: see text], and LF. Our study proposed a system that can be applied to medical devices for automatic SDB identification and real-time feedback on the training effect. We demonstrated that breathing at 6 bpm with an I:E ratio of 1:1 performed best during the training phase, while intelligent guidance mode had a more long-lasting effect.

The integration of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) can facilitate the advancement of brain-computer interfaces (BCIs). However, existing research in this domain has grappled with the challenge of the efficient selection of features, resulting in the underutilization of the temporal richness of EEG and the spatial specificity of fNIRS data.To effectively address this challenge, this study proposed a deep learning architecture called the multimodal DenseNet fusion (MDNF) model that was trained on two-dimensional (2D) EEG data images, leveraging advanced feature extraction techniques. The model transformed EEG data into 2D images using a short-time Fourier transform, applied transfer learning to extract discriminative features, and consequently integrated them with fNIRS-derived spectral entropy features. This approach aimed to bridge existing gaps in EEG-fNIRS-based BCI research by enhancing classification accuracy and versatility across various cognitive and motor imagery tasks.Experimental results on two public datasets demonstrated the superiority of our model over existing state-of-the-art methods.Thus, the high accuracy and precise feature utilization of the MDNF model demonstrates the potential in clinical applications for neurodiagnostics and rehabilitation, thereby paving the method for patient-specific therapeutic strategies.

Non-sustained supraventricular tachycardia (nsSVT) is associated with a higher risk of developing atrial fibrillation (AF), and, therefore, detection of nsSVT can improve AF screening efficiency. However, the detection is challenged by the lower signal quality of ECGs recorded using handheld devices and the presence of ectopic beats which may mimic the rhythm characteristics of nsSVT.

To achieve precise Couinaud liver segmentation in preoperative planning for hepatic surgery, accommodating the complex anatomy and significant variations, optimizing surgical approaches, reducing postoperative complications, and preserving liver function.This research presents a novel approach to automating liver segmentation by identifying seven key anatomical landmarks using portal venous phase images from contrast-enhanced magnetic resonance imaging (CE-MRI). By employing a multi-task learning framework, we synchronized the detection of these landmarks with the segmentation process, resulting in accurate and robust delineation of the Couinaud segments.To comprehensively validate our model, we included multiple patient types in our test set-those with normal livers, diffuse liver diseases, and localized liver lesions-under varied imaging conditions, including two field strengths, two devices, and two contrast agents. Our model achieved an average Dice Similarity Coefficient (DSC) of 85.29%, surpassing the next best-performing models by 3.12%.Our research presents a pioneering automated approach for segmenting Couinaud segments using CE-MRI. By correlating landmark detection with segmentation, we enhance surgical planning precision. This method promises improved clinical outcomes by accurately adapting to anatomical variability and reducing potential postoperative complications.Clinical impact: The application of this technique in clinical settings is poised to enhance the precision of liver surgical planning. This could lead to more tailored surgical interventions, minimization of operative risks, and preservation of healthy liver tissue, culminating in improved patient outcomes and potentially lowering the incidence of postoperative complications.Clinical and Translational Impact Statement: This research offers a novel automated liver segmentation technique, enhancing preoperative planning and potentially reducing complications, which may translate into better postoperative outcomes in hepatic surgery.