Towards Quantifying Stress in Patients with a History of Myocardial Infarction: Validating ECG-Derived Patch Features.

Patients with prior myocardial infarction (MI) have an increased risk of experiencing a secondary event which is exacerbated by mental stress. Our team has developed a miniaturized patch with the capability to capture electrocardiogram (ECG), seismocardiogram (SCG) and photoplethysmogram (PPG) signals which may provide multimodal information to characterize stress responses within the post-MI population in ambulatory settings. As ECG-derived features have been shown to be informative in assessing the risk of MI, a critical first step is to ensure that the patch ECG features agree with gold-standard devices, such as the Biopac. However, this is yet to be done in this population. We, thus, performed a comparative analysis between ECG-derived features (heart rate (HR) and heart rate variability (HRV)) of the patch and Biopac in the context of stress. Our dataset contained post-MI and healthy control subjects who participated in a public speaking challenge. Regression analyses for patch and Biopac HR and HRV features (RMSSD, pNN50, SD1/SD2, and LF/HF) were all significant (p<0.001) and had strong positive correlations (r>0.9). Additionally, Bland-Altman analyses for most features showed tight limits of agreement: 0.999 bpm (HR), 11.341 ms (RMSSD), 0.07% (pNN50), 0.146 ratio difference (SD1/SD2), 0.750 ratio difference (LF/HF).Clinical relevance- This work demonstrates that ECG-derived features obtained from the patch and Biopac are in agreement, suggesting the clinical utility of the patch in deriving quantitative metrics of physiology during stress in post-MI patients. This has the potential to improve post-MI patients' outcomes, but needs to be further evaluated.

Vision-based detection and quantification of maternal sleeping position in the third trimester of pregnancy in the home setting-Building the dataset and model.

In 2021, the National Guideline Alliance for the Royal College of Obstetricians and Gynaecologists reviewed the body of evidence, including two meta-analyses, implicating supine sleeping position as a risk factor for growth restriction and stillbirth. While they concluded that pregnant people should be advised to avoid going to sleep on their back after 28 weeks' gestation, their main critique of the evidence was that, to date, all studies were retrospective and sleeping position was not objectively measured. As such, the Alliance noted that it would not be possible to prospectively study the associations between sleeping position and adverse pregnancy outcomes. Our aim was to demonstrate the feasibility of building a vision-based model for automated and accurate detection and quantification of sleeping position throughout the third trimester-a model with the eventual goal to be developed further and used by researchers as a tool to enable them to either confirm or disprove the aforementioned associations. We completed a Canada-wide, cross-sectional study in 24 participants in the third trimester. Infrared videos of eleven simulated sleeping positions unique to pregnancy and a sitting position both with and without bed sheets covering the body were prospectively collected. We extracted 152,618 images from 48 videos, semi-randomly down-sampled and annotated 5,970 of them, and fed them into a deep learning algorithm, which trained and validated six models via six-fold cross-validation. The performance of the models was evaluated using an unseen testing set. The models detected the twelve positions, with and without bed sheets covering the body, achieving an average precision of 0.72 and 0.83, respectively, and an average recall ("sensitivity") of 0.67 and 0.76, respectively. For the supine class with and without bed sheets covering the body, the models achieved an average precision of 0.61 and 0.75, respectively, and an average recall of 0.74 and 0.81, respectively.

Transcutaneous auricular Vagus Nerve Stimulation and Median Nerve Stimulation reduce acute stress in young healthy adults: a single-blind sham-controlled crossover study.

Stress is a major determinant of health and wellbeing. Conventional stress management approaches do not account for the daily-living acute changes in stress that affect quality of life. The combination of physiological monitoring and non-invasive Peripheral Nerve Stimulation (PNS) represents a promising technological approach to quantify stress-induced physiological manifestations and reduce stress during everyday life. This study aimed to evaluate the effectiveness of three well-established transcutaneous PNS modalities in reducing physiological manifestations of stress compared to a sham: auricular and cervical Vagus Nerve Stimulation (taVNS and tcVNS), and Median Nerve Stimulation (tMNS). Using a single-blind sham-controlled crossover study with four visits, we compared the stress mitigation effectiveness of taVNS, tcVNS, and tMNS, quantified through physiological markers derived from five physiological signals peripherally measured on 19 young healthy volunteers. Participants underwent three acute mental and physiological stressors while receiving stimulation. Blinding effectiveness was assessed via subjective survey. taVNS and tMNS relative to sham resulted in significant changes that suggest a reduction in sympathetic outflow following the acute stressors: Left Ventricular Ejection Time Index (LVETI) shortening (tMNS: p = 0.007, taVNS: p = 0.015) and Pre-Ejection Period (PEP)-to-LVET ratio (PEP/LVET) increase (tMNS: p = 0.044, taVNS: p = 0.029). tMNS relative to sham also reduced Pulse Pressure (PP; p = 0.032) and tonic EDA activity (tonicMean; p = 0.025). The nonsignificant blinding survey results suggest these effects were not influenced by placebo. taVNS and tMNS effectively reduced stress-induced sympathetic arousal in wearable-compatible physiological signals, motivating their future use in novel personalized stress therapies to improve quality of life.

Non-contact temperature control and stereo digital image correlation for high-temperature testing of miniature tubular specimens.

Component failures very often occur due to high temperature and multiaxial stress states arising at critical component locations. To imitate such loading conditions, a multiaxial miniature testing system (MMTS) with axial, torsional, and internal pressurization capabilities for high-temperature testing of miniature tubular specimens has been developed. Among many challenges of developing the MMTS, uniform heating, temperature measurement and control, and surface strain measurement on a miniature tubular specimen at high temperatures have significant difficulties. This paper addresses two significant challenges: first, the development of a non-contact temperature control system using infrared thermography to uniformly heat a miniature specimen of 1 mm outer diameter (OD), and second, the development of a stereo digital image correlation (stereo-DIC) setup for strain measurement on the miniature specimen subjected to high temperature. The developed control system maintains the test temperature through a closed feedback loop and employs a fail-safe mechanism to protect the MMTS load frame components against unanticipated temperature rises. The thermocouple wire-size effect on the measured temperature was examined for three different wire sizes: 0.05, 0.25, and 0.5 mm for accurate emissivity determination required for infrared thermography. Emissivities of the specimen surface at different high temperatures were experimentally determined. Inherent error analysis of the developed high-temperature stereo-DIC setup showed acceptable strain measurement uncertainty. The effectiveness of the developed non-contact temperature control system and high-temperature stereo-DIC setup has been verified by performing tensile testing of a 1 mm OD specimen at 500 °C.

Depth of anesthesia prediction via EEG signals using convolutional neural network and ensemble empirical mode decomposition.

According to a recently conducted survey on surgical complication mortality rate, 47% of such cases are due to anesthetics overdose. This indicates that there is an urgent need to moderate the level of anesthesia. Recently deep learning (DL) methods have played a major role in estimating the depth of Anesthesia (DOA) of patients and has played an essential role in control anesthesia overdose. In this paper, Electroencephalography (EEG) signals have been used for the prediction of DOA. EEG signals are very complex signals which may require months of training and advanced signal processing techniques. It is a point of debate whether DL methods are an improvement over the already existing traditional EEG signal processing approaches. One of the DL algorithms is Convolutional neural network (CNN) which is very popular algorithm for object recognition and is widely growing its applications in processing hierarchy in the human visual system. In this paper, various decomposition methods have been used for extracting the features EEG signal. After acquiring the necessary signals values in image format, several CNN models have been deployed for classification of DOA depending upon their Bispectral Index (BIS) and the signal quality index (SQI). The EEG signals were converted into the frequency domain using and Empirical Mode Decomposition (EMD), and Ensemble Empirical Mode Decomposition (EEMD). However, because of the inter mode mixing observed in EMD method; EEMD have been utilized for this study. The developed CNN models were used to predict the DOA based on the EEG spectrum images without the use of handcrafted features which provides intuitive mapping with high efficiency and reliability. The best trained model gives an accuracy of 83.2%. Hence, this provides further scope and research which can be carried out in the domain of visual mapping of DOA using EEG signals and DL methods.

Risk factors for self-harm repetition in adolescents: A systematic review.

Self-harm behavior can begin in early adolescence, with the highest rates of self-harm, between 1990 and 2000 in England, being among adolescents aged 16 to 24 years and there being considerable risk of fatal and non-fatal repetition. Previous systematic reviews have identified risk factors for self-harm in adolescents, but not for the repetition of this behavior. The aim of this review was to synthesise the psychological, psychosocial and sociodemographic risk factors for self-harm repetition in adolescents. By searching four databases, 27 studies were identified and included in the review. Several psychological (e.g., psychiatric morbidity, features of previous self-harm, psychological distress), psychosocial (e.g., alcohol misuse, poor family and peer relationships) and sociodemographic (e.g., age, gender and ethnicity) risk factors were identified for self-harm repetition in adolescents. Several risk factors across all categories for self-harm overlapped with that of self-harm repetition, such as depression, alcohol misuse and female gender. The clinical implications of these findings for practitioners were discussed. As was the case with prior reviews in this area, comparability between studies was limited and a meta-analysis was not possible due to considerable heterogeneity in outcome definitions, measures and methodologies.

Role of dipstick albuminuria in progression of paediatric chronic kidney disease.

OBJECTIVE: Renal function of patients with chronic kidney disease (CKD) is typically evaluated by detecting proteinuria because it is a major predictor of CKD progression. In paediatric patients with CKD, urine albumin-to-creatinine ratio (ACR) is used to detect CKD progression, which is similar to urine protein-to-creatinine ratio (PCR). However, facilities for evaluation of urine ACR and urine PCR may not be widely available. To date, this is the first study that investigated the predictive value of baseline dipstick albuminuria for 1-year and 3-year CKD progression in Indonesian children. We assessed the association between baseline level of dipstick albuminuria and CKD progression in paediatric patients. METHODS: This retrospective cohort study was conducted at the Cipto Mangunkusumo Hospital (CMH) involving 43 children with CKD between 2016 and 2019. The patients were followed up for 1 year and 3 years after enrolment. Risk ratios (RR) for 1-year and 3-year CKD progression were calculated using Fisher's exact test. RESULTS: The RR for 1-year CKD progression in children with baseline dipstick albuminuria <2+ was 2.16 (95% CI: 1.13-4.14, p = 0.02), and the corresponding RR for 3-year CKD progression in these children was 1.70 (95% CI: 0.73-3.97, p=0.21). CONCLUSIONS: Dipstick albuminuria was not associated with 1-year and 3-year CKD progression in children.

Topological Descriptors for Parkinson's Disease Classification and Regression Analysis.

At present, the vast majority of human subjects with neurological disease are still diagnosed through in-person assessments and qualitative analysis of patient data. In this paper, we propose to use Topological Data Analysis (TDA) together with machine learning tools to automate the process of Parkinson's disease classification and severity assessment. An automated, stable, and accurate method to evaluate Parkinson's would be significant in streamlining diagnoses of patients and providing families more time for corrective measures. We propose a methodology which incorporates TDA into analyzing Parkinson's disease postural shifts data through the representation of persistence images. Studying the topology of a system has proven to be invariant to small changes in data and has been shown to perform well in discrimination tasks. The contributions of the paper are twofold. We propose a method to 1) classify healthy patients from those afflicted by disease and 2) diagnose the severity of disease. We explore the use of the proposed method in an application involving a Parkinson's disease dataset comprised of healthy-elderly, healthy-young and Parkinson's disease patients. Our code is available at https://github.com/itsmeafra/Sublevel-Set-TDA.

Effective Management of Peritoneal Dialysis-Associated Hydrothorax in a Child: A Case Report.

Peritoneal dialysis (PD) confers many advantages, including a better quality of life for children with end-stage renal disease; however, the procedure is associated with several complications, including pleuroperitoneal leaks. Here, we report an unusual case of hydrothorax caused by long-term PD in a child, which was further complicated by pneumonia. A 9-year-old boy who had received CAPD for 22 months presented with dyspnea, swelling, and increased body weight. Chest tube drainage yielded 500 mL of transudative fluid. Computed tomography peritoneography revealed increased outflow from the peritoneum to the pleural cavity. PD was suspended, and hemodialysis (HD) was initiated. Video-assisted thoracoscopic surgery was performed; however, because the patient had pneumonia during hospitalization, pleural adhesions with a septated appearance occurred. This resulted in difficulties identifying pleuroperitoneal fistula (PPF). Right pleural effusion resolved following pleurodesis using bleomycin. Regular HD was performed for 10 weeks, and PD was subsequently reinitiated. There was no recurrence of hydrothorax during long-term follow-up. We suspect that the underlying mechanism of hydrothorax in our patient was associated with a PPF that formed either due to a congenital diaphragmatic defect or an acquired defect, resulting in dialysate leakage. Our case demonstrates that a temporary switch from PD to HD, accompanied by pleurodesis, may help resolve hydrothorax that occurs as a complication of long-term PD.