The feasibility of predicting impending malignant ventricular arrhythmias by using nonlinear features of short heartbeat intervals

Autonomic neuronal modulations in cardiac arrhythmias: Current concepts and emerging therapies

The pathophysiology of atrial fibrillation and ventricular tachycardia that result in cardiac arrhythmias is related to the sustained complicated mechanisms of the autonomic nervous system. Atrial fibrillation is when the heart beats irregularly, and ventricular arrhythmias are rapid and inconsistent heart rhythms, which involves many factors including the autonomic nervous system. It's a complex topic that requires careful exploration. Cultivation of speculative knowledge on atrial fibrillation; the irregular rhythm of the heart and ventricular arrhythmias; rapid oscillating waves resulting from mistakenly inconsistent P waves, and the inclusion of an autonomic nervous system is an inconceivable approach toward clinical intricacies. Autonomic modulation, therefore, acquires new expansions and conceptions of appealing therapeutic intelligence to prevent cardiac arrhythmia. Notably, autonomic modulation uses the neural tissue's flexibility to cause remodeling and, hence, provide therapeutic effects. In addition, autonomic modulation techniques included stimulation of the vagus nerve and tragus, renal denervation, cardiac sympathetic denervation, and baroreceptor activation treatment. Strong preclinical evidence and early human studies support the annihilation of cardiac arrhythmias by sympathetic and parasympathetic systems to transmigrate the cardiac myocytes and myocardium as efficient determinants at the cellular and physiological levels. However, the goal of this study is to draw attention to these promising early pre-clinical and clinical arrhythmia treatment options that use autonomic modulation as a therapeutic modality to conquer the troublesome process of irregular heart movements. Additionally, we provide a summary of the numerous techniques for measuring autonomic tone such as heart rate oscillations and its association with cutaneous sympathetic nerve activity appear to be substitute indicators and predictors of the outcome of treatment.

Unfolded protein response pathways in stroke patients: a comprehensive landscape assessed through machine learning algorithms and experimental verification

BACKGROUND

The unfolding protein response is a critical biological process implicated in a variety of physiological functions and disease states across eukaryotes. Despite its significance, the role and underlying mechanisms of the response in the context of ischemic stroke remain elusive. Hence, this study endeavors to shed light on the mechanisms and role of the unfolding protein response in the context of ischemic stroke.

METHODS

In this study, mRNA expression patterns were extracted from the GSE58294 and GSE16561 datasets in the GEO database. The screening and validation of protein response-related biomarkers in stroke patients, as well as the analysis of the immune effects of the pathway, were carried out. To identify the key genes in the unfolded protein response, we constructed diagnostic models using both random forest and support vector machine-recursive feature elimination methods. The internal validation was performed using a bootstrapping approach based on a random sample of 1,000 iterations. Lastly, the target gene was validated by RT-PCR using clinical samples. We utilized two algorithms, CIBERSORT and MCPcounter, to investigate the relationship between the model genes and immune cells. Additionally, we performed uniform clustering of ischemic stroke samples based on expression of genes related to the UPR pathway and analyzed the relationship between different clusters and clinical traits. The weighted gene co-expression network analysis was conducted to identify the core genes in various clusters, followed by enrichment analysis and protein profiling for the hub genes from different clusters.

RESULTS

Our differential analysis revealed 44 genes related to the UPR pathway to be statistically significant. The integration of both machine learning algorithms resulted in the identification of 7 key genes, namely ATF6, EXOSC5, EEF2, LSM4, NOLC1, BANF1, and DNAJC3. These genes served as the foundation for a diagnostic model, with an area under the curve of 0.972. Following 1000 rounds of internal validation via randomized sampling, the model was confirmed to exhibit high levels of both specificity and sensitivity. Furthermore, the expression of these genes was found to be linked with the infiltration of immune cells such as neutrophils and CD8 T cells. The cluster analysis of ischemic stroke samples revealed three distinct groups, each with differential expression of most genes related to the UPR pathway, immune cell infiltration, and inflammatory factor secretion. The weighted gene co-expression network analysis showed that all three clusters were associated with the unfolded protein response, as evidenced by gene enrichment analysis and the protein landscape of each cluster. The results showed that the expression of the target gene in blood was consistent with the previous analysis.

CONCLUSION

The study of the relationship between UPR and ischemic stroke can help to better understand the underlying mechanisms of the disease and provide new targets for therapeutic intervention. For example, targeting the UPR pathway by blocking excessive autophagy or inducing moderate UPR could potentially reduce tissue injury and promote cell survival after ischemic stroke. In addition, the results of this study suggest that the use of UPR gene expression levels as biomarkers could improve the accuracy of early diagnosis and prognosis of ischemic stroke, leading to more personalized treatment strategies. Overall, this study highlights the importance of the UPR pathway in the pathology of ischemic stroke and provides a foundation for future studies in this field.

Movement recognition via channel-activation-wise sEMG attention

CONTEXT

Surface electromyography (sEMG) signals contain rich information recorded from muscle movements and therefore reflect the user's intention. sEMG has seen dominant applications in rehabilitation, clinical diagnosis as well as human engineering, etc. However, current feature extraction methods for sEMG signals have been seriously limited by their stochasticity, transiency, and non-stationarity.

OBJECTIVE

Our objective is to combat the difficulties induced by the aforementioned downsides of sEMG and thereby extract representative features for various downstream movement recognition.

METHOD

We propose a novel 3-axis view of sEMG features composed of temporal, spatial, and channel-wise summary. We leverage the state-of-the-art architecture Transformer to enforce efficient parallel search and to get rid of limitations imposed by previous work in gesture classification. The transformer model is designed on top of an attention-based module, which allows for the extraction of global contextual relevance among channels and the use of this relevance for sEMG recognition.

RESULTS

We compared the proposed method against existing methods on two Ninapro datasets consisting of data from both healthy people and amputees. Experimental results show the proposed method attains the state-of-the-art (SOTA) accuracy on both datasets. We further show that the proposed method enjoys strong generalization ability: a new SOTA is achieved by pretraining the model on a different dataset followed by fine-tuning it on the target dataset.

A training pipeline of an arrhythmia classifier for atrial fibrillation detection using Photoplethysmography signal

Photoplethysmography (PPG) signal is potentially suitable in atrial fibrillation (AF) detection for its convenience in use and similarity in physiological origin to electrocardiogram (ECG). There are a few preceding studies that have shown the possibility of using the peak-to-peak interval of the PPG signal (PPIp) in AF detection. However, as a generalized model, the accuracy of an AF detector should be pursued on the one hand; on the other hand, its generalizability should be paid attention to in view of the individual differences in PPG manifestation of even the same arrhythmia and the existence of sub-types. Moreover, a binary classifier for atrial fibrillation and normal sinus rhythm is not convincing enough for the similarity between AF and ectopic beats. In this study, we project the atrial fibrillation detection as a multiple-class classification and try to propose a training pipeline that is advantageous both to the accuracy and generalizability of the classifier by designing and determining the configurable options of the pipeline, in terms of input format, deep learning model (with hyperparameter optimization), and scheme of transfer learning. With a rigorous comparison of the possible combinations of the configurable components in the pipeline, we confirmed that first-order difference of heartbeat sequence as the input format, a 2-layer CNN-1-layer Transformer hybrid^R model as the learning model and the whole model fine-tuning as the implementing scheme of transfer learning is the best combination for the pipeline (F1 value: 0.80, overall accuracy: 0.87)^R.

Sleep Staging Framework with Physiologically Harmonized Sub-Networks

Sleep screening is an important tool for both healthcare and neuroscientific research. Automatic sleep scoring is an alternative to the time-consuming gold-standard manual scoring procedure. Recently there have seen promising results on automatic stage scoring by extracting spatio-temporal features via deep neural networks from electroencephalogram (EEG). However, such methods fail to consistently yield good performance due to a missing piece in data representation: the medical criterion of the sleep scoring task on top of EEG features. We argue that capturing stage-specific features that satisfy the criterion of sleep medicine is non-trivial for automatic sleep scoring. This paper considers two criteria: Transient stage marker and Overall profile of EEG features, then we propose a physiologically meaningful framework for sleep stage scoring via mixed deep neural networks. The framework consists of two sub-networks: feature extraction networks, constructed in consideration of the physiological characteristics of sleep, and an attention-based scoring decision network. Moreover, we quantize the framework for potential use under an IoT setting. For proof-of-concept, the performance of the proposed framework is demonstrated by introducing multiple sleep datasets with the largest comprising 42,560 h recorded from 5,793 subjects. From the experiment results, the proposed method achieves a competitive stage scoring performance, especially for Wake, N2, and N3, with higher F1 scores of 0.92, 0.86, and 0.88, respectively. Moreover, the feasibility analysis of framework quantization provides a potential for future implementation in the edge computing field and clinical settings.

Golden Standard or Obsolete Method? Review of ECG Applications in Clinical and Experimental Context

Cardiovascular system and its functions under both physiological and pathophysiological conditions have been studied for centuries. One of the most important steps in the cardiovascular research was the possibility to record cardiac electrical activity. Since then, numerous modifications and improvements have been introduced; however, an electrocardiogram still represents a golden standard in this field. This paper overviews possibilities of ECG recordings in research and clinical practice, deals with advantages and disadvantages of various approaches, and summarizes possibilities of advanced data analysis. Special emphasis is given to state-of-the-art deep learning techniques intensely expanded in a wide range of clinical applications and offering promising prospects in experimental branches. Since, according to the World Health Organization, cardiovascular diseases are the main cause of death worldwide, studying electrical activity of the heart is still of high importance for both experimental and clinical cardiology.

Application of photoplethysmography signals for healthcare systems: An in-depth review

BACKGROUND AND OBJECTIVES

Photoplethysmography (PPG) is a device that measures the amount of light absorbed by the blood vessel, blood, and tissues, which can, in turn, translate into various measurements such as the variation in blood flow volume, heart rate variability, blood pressure, etc. Hence, PPG signals can produce a wide variety of biological information that can be useful for the detection and diagnosis of various health problems. In this review, we are interested in the possible health disorders that can be detected using PPG signals.

METHODS

We applied PRISMA guidelines to systematically search various journal databases and identified 43 PPG studies that fit the criteria of this review.

RESULTS

Twenty-five health issues were identified from these studies that were classified into six categories: cardiac, blood pressure, sleep health, mental health, diabetes, and miscellaneous. Various routes were employed in these PPG studies to perform the diagnosis: machine learning, deep learning, and statistical routes. The studies were reviewed and summarized.

CONCLUSIONS

We identified limitations such as poor standardization of sampling frequencies and lack of publicly available PPG databases. We urge that future work should consider creating more publicly available databases so that a wide spectrum of health problems can be covered. We also want to promote the use of PPG signals as a potential precision medicine tool in both ambulatory and hospital settings.

Machine learning techniques for arrhythmic risk stratification: a review of the literature

Ventricular arrhythmias (VAs) and sudden cardiac death (SCD) are significant adverse events that affect the morbidity and mortality of both the general population and patients with predisposing cardiovascular risk factors. Currently, conventional disease-specific scores are used for risk stratification purposes. However, these risk scores have several limitations, including variations among validation cohorts, the inclusion of a limited number of predictors while omitting important variables, as well as hidden relationships between predictors. Machine learning (ML) techniques are based on algorithms that describe intervariable relationships. Recent studies have implemented ML techniques to construct models for the prediction of fatal VAs. However, the application of ML study findings is limited by the absence of established frameworks for its implementation, in addition to clinicians’ unfamiliarity with ML techniques. This review, therefore, aims to provide an accessible and easy-to-understand summary of the existing evidence about the use of ML techniques in the prediction of VAs. Our findings suggest that ML algorithms improve arrhythmic prediction performance in different clinical settings. However, it should be emphasized that prospective studies comparing ML algorithms to conventional risk models are needed while a regulatory framework is required prior to their implementation in clinical practice.

Global hybrid multi-scale convolutional network for accurate and robust detection of atrial fibrillation using single-lead ECG recordings

BACKGROUND AND OBJECTIVE

Atrial fibrillation (AF) is the most common persistent cardiac arrhythmia in clinical practice, and its accurate screening is of great significance to avoid cardiovascular diseases (CVDs). Electrocardiogram (ECG) is considered to be the most commonly used technique for detecting AF abnormalities. However, previous ECG-based deep learning algorithms did not take into account the complementary nature of inter-layer information, which may lead to insufficient AF screening. This study reports the first attempt to use hybrid multi-scale information in a global space for accurate and robust AF detection.

METHODS

We propose a novel deep learning classification method, namely, global hybrid multi-scale convolutional neural network (i.e., GH-MS-CNN), to implement binary classification for AF detection. Unlike previous deep learning methods in AF detection, an ingenious hybrid multi-scale convolution (HMSC) module, for the advantage of automatically aggregating different types of complementary inter-layer multi-scale features in the global space, is introduced into all dense blocks of the GH-MS-CNN model to implement sufficient feature extraction, and achieve much better overall classification performance.

RESULTS

The proposed GH-MS-CNN method has been fully validated on the CPSC 2018 database and tested on the independent PhysioNet 2017 database. The experimental results show that the global and hybrid multi-scale information has tremendous advantages over local and single-type multi-scale information in AF screening. Furthermore, the proposed GH-MS-CNN method outperforms the state-of-the-art methods and achieves the best classification performance with an accuracy of 0.9984, a precision of 0.9989, a sensitivity of 0.9965, a specificity of 0.9998 and an F1 score of 0.9954. In addition, the proposed method has achieved comparable and considerable generalization capability on the PhysioNet 2017 database.

CONCLUSIONS

The proposed GH-MS-CNN method has promising capabilities and great advantages in accurate and robust AF detection. It is assumed that this research has made significant improvements in AF screening and has great potential for long-term monitoring of wearable devices.