Biomedical Engineering Letters最新文献

The scoring of sleep stages is one of the essential tasks in sleep analysis. Since a manual procedure requires considerable human and financial resources, and incorporates some subjectivity, an automated approach could result in several advantages. There have been many developments in this area, and in order to provide a comprehensive overview, it is essential to review relevant recent works and summarise the characteristics of the approaches, which is the main aim of this article. To achieve it, we examined articles published between 2018 and 2022 that dealt with the automated scoring of sleep stages. In the final selection for in-depth analysis, 125 articles were included after reviewing a total of 515 publications. The results revealed that automatic scoring demonstrates good quality (with Cohen's kappa up to over 0.80 and accuracy up to over 90%) in analysing EEG/EEG + EOG + EMG signals. At the same time, it should be noted that there has been no breakthrough in the quality of results using these signals in recent years. Systems involving other signals that could potentially be acquired more conveniently for the user (e.g. respiratory, cardiac or movement signals) remain more challenging in the implementation with a high level of reliability but have considerable innovation capability. In general, automatic sleep stage scoring has excellent potential to assist medical professionals while providing an objective assessment.

Sleep is an essential part of our lives and daily sleep monitoring is crucial for maintaining good health and well-being. Traditionally, the gold standard method for sleep monitoring is polysomnography using various sensors attached to the body; however, it is limited with regards to long-term sleep monitoring in a home environment. Recent advancements in wearable and nearable technology have made it possible to monitor sleep at home. In this review paper, the technologies that are currently available for sleep stages and sleep disorder monitoring at home are reviewed using wearable and nearable devices. Wearables are devices that are worn on the body, while nearables are placed near the body. These devices can accurately monitor sleep stages and sleep disorder in a home environment. In this study, the benefits and limitations of each technology are discussed, along with their potential to improve sleep quality.

Among the various sleep modulation methods for improving sleep, three methods using noninvasive stimulation during sleep have been reviewed and summarized. The first method involves noninvasive direct brain stimulation to induce a current directly in the brain cortex. Electrically or magnetically applied stimulations trigger electrical events such as slow oscillations or sleep spindles, which can also be recorded by an electroencephalogram. The second method involves sensory stimulation during sleep, which provides stimulation through the sensory pathway to invoke equivalent brain activity like direct brain stimulation. Olfactory, vestibular, and auditory stimulation methods have been used, resulting in several sleep-modulating effects, which are characteristic and depend on the experimental paradigm. The third method is to modulate sleep by shifting the autonomic balance affecting sleep homeostasis. To strengthen parasympathetic dominance, stimulation was applied to decrease heart rate by synchronizing the heart rhythm. These noninvasive stimulation methods can strengthen slow-wave sleep, consolidate declarative or procedural memory, and modify sleep macrostructure. These stimulation methods provide evidence and possibility for sleep modulation in our daily life as an alternative method for the treatment of disturbed sleep and enhancing sleep quality and performance beyond the average level.

Sleep apnea (SLA) is a respiratory-related sleep disorder that affects a major proportion of the population. The gold standard in sleep testing, polysomnography, is costly, inconvenient, and unpleasant, and it requires a skilled professional to score. Multiple researchers have suggested and developed automated scoring processes with less detectors and automated classification algorithms to resolve these problems. An automatic detection system will allow for a high diagnosis rate and the analysis of additional patients. Deep learning (DL) is achieving high priority due to the availability of databases and recently developed methods. As the most up-and-coming technique for classification and generative tasks, DL has shown its significant potential in 2-dimensional clinical image processing studies. However, physiological information collected as 1-dimensional data has yet to be effectively extracted from this new approach to achieve the needed medical goals. So, in this study, we review the most recent studies in the field of DL applied to physiological data based on pulse oxygen saturation, electrocardiogram, airflow, and sound signal. A total of 47 articles from different journals and publishing houses that were published between 2012 and 2022 were identified. The primary objective of this work is to perform a comprehensive analysis to analyze, classify, and compare the main characteristics of deep-learning algorithms applied in physiological data processing for SLA detection. Overall, our analysis provides comprehensive and detailed information for researchers looking to add to this field. The data input source, objective, DL network, training framework, and database references are the critical factors of the DL approach examined. These are the most critical variables that influence system performance. We categorized the relevant research studies in physiological sensor data analysis using the DL approach based on (1) Physiological sensor data aspects, like signal types, sampling frequency, and window size; and (2) DL model perspectives, such as learning structure and input data types.

Supplementary information: The online version contains supplementary material available at 10.1007/s13534-023-00297-5.

Purpose: External electric fields can regulate the neural network and change the excitability of the in-vivo cerebral cortex. Here, to prove the effect of alternating electric fields on the synaptic plasticity of ex-vivo tissues, the regular changes in the synaptic structure under alternating electric fields were studied.

Methods: This study applied alternating electric fields with a peak voltage of 20 V and frequencies of 5, 20, 50, and 80 Hz to the porcine cerebral cortex. Relying on transmission electron microscopy (TEM), the ultrastructure of synapses was observed, and the curvature radius of post-synaptic density (PSD) and the synaptic gap distance was quantified.

Results: The results indicated that under alternating electric fields, the average synaptic curvature of the PSD decreased by 30-59% with increasing frequency, and the average synaptic gap distance became narrower.

Conclusion: In ex-vivo brain tissue, synaptic plasticity can be regulated by alternating electric fields of different frequencies. This study can provide reference data for the storage and regulation of ex-vivo organs, as well as comparable data for in-vivo studies.

Accidents related to the administration of intravenous (IV) medication, such as drug overdose/underdose, drug/patient mis-identification, and delayed bag exchange, occur consistently in clinical fields. Several previous studies have suggested various contact-sensing and image-processing methodologies; however, most of them can increase the workload of nursing staffs during the long-term, continuous monitoring. In this study, we proposed a smart IV pole that can monitor the infusion status of up to four IV medications (patient/drug identification, and liquid residue) with various sizes and hanging positions to reduce IV-related accidents and improve patient safety with the least additional workload; the system consists of 12 cameras, one code scanner, and four controllers. Two types of deep learning models for automated camera selection (CNN-1) and liquid residue monitoring (CNN-2), and three drug residue estimation equations were implemented. The experimental results demonstrated that the accuracy of identification code-checking (60 tests) was 100%. The classification accuracy and the mean inference time of CNN-1 (1200 tests) were 100% and 140 ms. The mean average precision and the mean inference time of CNN-2 (300 tests) were 0.94 and 144 ms. The average error rates between the alarm setting (20, 30, and 40 mL) and the actual drug residue when the alarm first generated were 4.00%, 7.33%, and 4.50% for a 1,000 mL bag; 6.00%, 4.67%, and 2.50% for a 500 mL bag; and 3.00%, 6.00%, and 3.50% for a 100 mL bag, respectively. Our results suggest that the implemented AI-based prototype IV pole is a potential tool for reducing IV-related accidents and improving in-hospital patient safety.

Supplementary information: The online version contains supplementary material available at 10.1007/s13534-023-00292-w.

Brain-computer interfaces (BCI) translate brain signals into artificial output to restore or replace natural central nervous system (CNS) functions. Multiple processes, including sensorimotor integration, decision-making, motor planning, execution, and updating, are involved in any movement. For example, a BCI may be better able to restore naturalistic motor behaviors if it uses signals from multiple brain areas and decodes natural behaviors' cognitive and motor aspects. This review provides an overview of the preliminary information necessary to plan a BCI project focusing on intracortical implants in primates. Since the brain structure and areas of non-human primates (NHP) are similar to humans, exploring the result of NHP studies will eventually benefit human BCI studies. The different types of BCI systems based on the target cortical area, types of signals, and decoding methods will be discussed. In addition, various successful state-of-the-art cases will be reviewed in more detail, focusing on the general algorithm followed in the real-time system. Finally, an outlook for improving the current BCI research studies will be debated.

The vestibular system (VS) is a sensory system that has a vital function in human life by serving to maintain balance. In this study, multifractal detrended fluctuation analysis (MFDFA) is applied to insole pressure sensor data collected from subjects in order to extract features to identify diseases related to VS dysfunction. We use the multifractal spectrum width as the feature to distinguish between healthy and diseased people. It is observed that multifractal behavior is more dominant and thus the spectrum is wider for healthy subjects, where we explain the reason as the long-range correlations of the small and large fluctuations of the time series for this group. We directly process the instantaneous pressure values to extract features in contrast to studies in the literature where gait analysis is based on investigation of gait dynamics (stride time, stance time, etc.) requiring long walking time. Thus, as the main innovation of this work, we detrend the data to give meaningful information even for a relatively short walk. Extracted feature set was input to fundamental classification algorithms where the Support-Vector-Machine (SVM) performed best with an average accuracy of 98.2% for the binary classification as healthy or suffering. This study is a substantial part of a big project where we finally aim to identify the specific VS disease that causes balance disorder and also determine the stage of the disease, if any. Within this scope, the achieved performance gives high motivation to work more deeply on the issue.

Monocular depth estimation from camera images is very important for surrounding scene evaluation in many technical fields from automotive to medicine. However, traditional triangulation methods using stereo cameras or multiple views with the assumption of a rigid environment are not applicable for endoscopic domains. Particularly in cystoscopies it is not possible to produce ground truth depth information to directly train machine learning algorithms for using a monocular image directly for depth prediction. This work considers first creating a synthetic cystoscopic environment for initial encoding of depth information from synthetically rendered images. Next, the task of predicting pixel-wise depth values for real images is constrained to a domain adaption between the synthetic and real image domains. This adaptation is done through added gated residual blocks in order to simplify the network task and maintain training stability during adversarial training. Training is done on an internally collected cystoscopy dataset from human patients. The results after training demonstrate the ability to predict reasonable depth estimations from actual cystoscopic videos and added stability from using gated residual blocks is shown to prevent mode collapse during adversarial training.

[This corrects the article DOI: 10.1007/s13534-022-00232-0.].