Arterial blood pressure (ABP) monitoring may permit the early diagnosis and management of cardiovascular disease (CVD); however, existing methods for measuring ABP outside clinic use inconvenient cuff sphygmomanometry, or do not estimate continuous waveforms. This study proposes a novel deep learning model DeepCNAP estimating BP waveform from noninvasively measured photoplethysmography (PPG) signal in real-time. was designed through combination convolutional networks self-attention. The...

In neurointensive care units (NICUs), particularly in cases involving traumatic brain injury (TBI), swift and accurate decision-making is critical because of rapidly changing patient conditions the risk secondary injury. The use artificial intelligence (AI) NICU can enhance clinical decision support provide valuable assistance these complex scenarios. This article aims to a comprehensive review current status future prospects AI utilization NICU, along with challenges that must be overcome...

Military personnel face an increased risk of developing mental disorders owing to the stressful environments they encounter. Effective stress management strategies are crucial mitigate this risk. Mindfulness training (MT) is promising as a approach in such demanding settings. This study uses quantitative investigate impact MT on relationship between autonomic nervous system (ANS) and emotional regulation. The evaluated effectiveness reducing among 86 military personnel. Participants were...

ABSTRACT Mindfulness training (MT) has been shown to be effective at managing emotions and stress. However, the underlying neural mechanism of MT is yet unclear attempts explore effects on both psychological factors performance outcomes remain unexplored. Physiological questionnaire, measures, EEG‐based Functional connectivity (FC) in fronto‐limbic network were analyzed ( N = 39) control 43) groups. Statistical analyses conducted evaluate group‐wise differences, within‐group longitudinal...

Purpose: Cerebral hyperperfusion syndrome (CHS) is a postoperative complication in moyamoya disease (MMD). However, limited studies have investigated the association between preoperative hemodynamic features and CHS. In this study, we aimed to identify predictors of CHS MMD using clinical data. Patients Methods: retrospective analyzed data from 72 hemispheres 56 adult patients with who underwent combined bypass surgery. Hemodynamic were extracted region interest on arterial spin-labeling...

Intracranial hypertension (IH) following acute phase traumatic brain injury (TBI) is associated with high mortality. Objective: This study proposes a novel parameter that may identify potentially life-threatening IH (LTH) event and designs machine learning model to predict LTH. Continuous recordings of intracranial pressure (ICP) arterial blood (ABP) from 273 TBI patients were used as the development dataset. The pressure-time dose (PTD) reactivity index (PRx) calculated for each event, an...

The purpose of this study was to identify whether the distribution Hounsfield Unit (HU) values across intracranial area in computed tomography (CT) images can be used as an effective diagnostic tool for determining severity cerebral edema pediatric traumatic brain injury (TBI) patients. CT images, medical records and radiology reports on 70 patients were collected. Based Marshall classification, grouped mild (n = 37) or severe 33). Automated quantitative analysis using unenhanced applied...

Periventricular lucency (PVL) is often observed in the hydrocephalic brain on CT or MRI. Earlier studies have proposed extravasation of ventricular CSF into periventricular white matter transependymal absorption as possible causes PVL hydrocephalus. However, there insufficient evidence for either theory to be conclusive.A finite element (FE) model with detailed anatomical geometry was constructed investigate mechanism The initiation hydrocephalus modeled by applying a transmantle pressure...

Failure of cerebral autoregulation and subsequent hypoperfusion is common during the acute phase traumatic brain injury (TBI). The cerebrovascular pressure-reactivity index (PRx) indirectly reflects has been used to derive optimal perfusion pressure (CPP). This study provides a method for use combination PRx, CPP, intracranial (ICP) better evaluate extent first 24 hours after TBI, allowing more accurate prediction mortality risk.

Heart failure (HF) is the terminal stage of all heart disease and leading cause mortality. A reliable prognostic model for predicting mortality in patients with HF can help to support better decisions clinical practice. Many attempts have been made increase reliability using electronic health record (EHR), but it still not known which oversampling method efficient imbalanced insufficient EHR dataset. This study performed a comparative analysis renowned methods (i.e., synthetic minority...

OBJECT Brain deformation can be seen in hydrocephalus and idiopathic intracranial hypertension (IIH) via medical images. The phenomenology of local effects, brain shift, raised pressure herniation are textbook concepts. However, there still uncertainties regarding the specific processes that occur when tissue is subject to mechanical stress different temporal spatial profiles 2 neurological disorders. Moreover, recent studies suggest IIH may diseases with opposite pathogenesis. Nevertheless,...

The classification of sleep stages is a pivotal aspect diagnosing disorders and evaluating quality. However, the conventional manual scoring process, conducted by clinicians, time-consuming prone to human bias. Recent advancements in deep learning have substantially propelled automation stage classification. Nevertheless, challenges persist, including need for large datasets with labels inherent biases human-generated annotations. This paper introduces NeuroNet, self-supervised (SSL)...

This study aims to develop maximal voluntary isometric contraction (MVIC) and submaximal (subMVIC) methods assess the reliability of developed for in-bed healthy individuals patients with subacute stroke. The electromyography (EMG) activities from lower-limb muscles including tensor fascia lata (TFL), rectus femoris (RF), tibialis anterior (TA), gastrocnemius (GC) on both sides were recorded during MVIC subMVIC using surface EMG sensors in 20 stroke patients. In inter-trial reliability,...

Obstructive sleep apnea (OSA) is associated with an increased risk of adverse outcomes, including mortality. Machine-learning algorithms have shown potential in predicting clinical outcomes patients OSA. This study aimed to develop and evaluate a machine-learning algorithm for 10- 15-year all-cause mortality Patients OSA were stratified into deceased alive groups based on outcomes. Various sleep-related features analyzed, objective measures the heart-rate variability during various stages....

Hemodynamic instability and cardiovascular events heavily affect the prognosis of traumatic brain injury. Physiological signals are monitored to detect these events. However, often riddled with faulty readings, which jeopardize reliability clinical parameters obtained from signals. A machine-learning model for elimination artifactual shows promising results improving signal quality. actual impact improvements on performance after artifacts is not well studied.The arterial blood pressure 99...

Sleep is closely related to physical and mental health quality of life, accurately evaluating sleep remains a major research topic in fields. Conventional methods evaluation involve the use polysomnography (PSG), which continuously records physiological changes during sleep. With recorded data, quality, stage or sleep-related disorders can be diagnosed via manual inspection by trained experts. However, practice time-consuming, labor-intensive yields high inter-and intra-rater variability. To...

Brain-computer interface (BCI) based on electroencephalogram (EEG) is a promising technology, allowing computers to estimate human intentions. Intention recognition tool such as motor imagery (MI) with high reliability one of the major challenges in BCI field. Recently, researchers have attempted use transfer learning for various datasets, but studies showed low classification accuracy. This study aimed increase accuracy MI through sequential single dataset. EEG-MI data 9 subjects from...

Classification of sleep stages is important for diagnosis and treatment disorder. Manual classification performed by experts burdensome time-consuming. This study proposes a novel model stage classification. EEG EOG signals 153 healthy subjects was used. The proposed ensembles two EEGNet-BiLSTM models which learn respectively. Compared to the existing models, yielded approximately 82% accuracy 0.78 k-value, whereas ensemble showed 90% 0.80 k-value. superior in terms consistency compared...