Pulse transit time based respiratory rate estimation with singular spectrum analysis

A Novel Approach for Sleep Arousal Disorder Detection Based on the Interaction of Physiological Signals and Metaheuristic Learning

The vast majority of sleep disturbances are caused by various types of sleep arousal. To diagnose sleep disorders and prevent health problems such as cardiovascular disease and cognitive impairment, sleep arousals must be accurately detected. Consequently, sleep specialists must spend considerable time and effort analyzing polysomnography (PSG) recordings to determine the level of arousal during sleep. The development of an automated sleep arousal detection system based on PSG would considerably benefit clinicians. We quantify the EEG-ECG by using Lyapunov exponents, fractals, and wavelet transforms to identify sleep stages and arousal disorders. In this paper, an efficient hybrid-learning method is introduced for the first time to detect and assess arousal incidents. Modified drone squadron optimization (mDSO) algorithm is used to optimize the support vector machine (SVM) with radial basis function (RBF) kernel. EEG-ECG signals are preprocessed samples from the SHHS sleep dataset and the PhysioBank challenge 2018. In comparison to other traditional methods for identifying sleep disorders, our physiological signals correlation innovation is much better than similar approaches. Based on the proposed model, the average error rate was less than 2%-7%, respectively, for two-class and four-class issues. Additionally, the proper classification of the five sleep stages is determined to be accurate 92.3% of the time. In clinical trials of sleep disorders, the hybrid-learning model technique based on EEG-ECG signal correlation features is effective in detecting arousals.

Cuffless Blood Pressure Monitoring: Academic Insights and Perspectives Analysis

In recent decades, cuffless blood pressure monitoring technology has been a point of research in the field of health monitoring and public media. Based on the web of science database, this paper evaluated the publications in the field from 1990 to 2020 using bibliometric analysis, described the developments in recent years, and presented future research prospects in the field. Through the comparative analysis of keywords, citations, H-index, journals, research institutions, national authors and reviews, this paper identified research hotspots and future research trends in the field of cuffless blood pressure monitoring. From the results of the bibliometric analysis, innovative methods such as machine learning technologies related to pulse transmit time and pulse wave analysis have been widely applied in blood pressure monitoring. The 2091 articles related to cuffless blood pressure monitoring technology were published in 1131 journals. In the future, improving the accuracy of monitoring to meet the international medical blood pressure standards, and achieving portability and miniaturization will remain the development goals of cuffless blood pressure measurement technology. The application of flexible electronics and machine learning strategy in the field will be two major development directions to guide the practical applications of cuffless blood pressure monitoring technology.

Carotid-Femoral Pulse Transit Time Variability Predicted Mortality and Improved Risk Stratification in the Elderly

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Multi-site Pulse Transit Times, Beat-to-Beat Blood Pressure, and Isovolumic Contraction Time at Rest and Under Stressors

This study investigates the beat-to-beat relationships among Pulse Transit Times (PTTs) and Pulse Arrival Times (PATs) concomitantly measured from the heart to finger, ear and forehead vascular districts, and their correlations with continuous finger blood pressure. These aspects were explored in 22 young volunteers at rest and during cold pressure test (CPT, thermal stress), handgrip (HG, isometric exercise) and cyclo-ergometer pedalling (CYC, dynamic exercise). The starting point of the PTT measures was the opening of the aortic valve detected by the seismocardiogram. Results indicate that PTTs measured at the ear, forehead and finger districts are uncorrelated each other at rest, and during CPT and HG. The stressors produced district-dependent changes in the PTT variability. Only the dynamic exercise was able to induce significant changes with respect to rest in the PTTs mean values (-40%, -36% and -17%, respectively for PTTear, PTTfore, PTTfinger,), and synchronize their modulations. Similar trends were observed in the PATs. The isovolumic contraction time decreased during the stressors application with a minimum at CYC (-25%) reflecting an augmented heart contractility. The increase in blood pressure (BP) at CPT was greater than that at CYC (137 vs. 128 mmHg), but the correlations between beat-to-beat transit times and BP were maximal at CYC (PAT showed a higher correlation than PTT; correlations were greater for systolic than for diastolic BP). This suggests that pulse transit times do not always depend directly on the beat-to-beat BP values but, under specific conditions, on other factors and mechanisms that concomitantly also influence BP.