Discrimination of vascular aging using the arterial pulse spectrum and machine-learning analysis

Unveiling the Potential: Remote Monitoring and Telemedicine in Shaping the Future of Heart Failure Management

Heart failure (HF) remains a significant burden on global healthcare systems, necessitating innovative approaches for its management. This manuscript critically evaluates the role of remote monitoring and telemedicine in revolutionizing HF care delivery. Drawing upon a synthesis of current literature and clinical practices, it delineates the pivotal benefits, challenges, and personalized strategies associated with these technologies in HF management. The analysis highlights the potential of remote monitoring and telemedicine in facilitating timely interventions, enhancing patient engagement, and optimizing treatment adherence, thereby ameliorating clinical outcomes. However, technical intricacies, regulatory frameworks, and socioeconomic factors pose formidable hurdles to widespread adoption. The manuscript emphasizes the imperative of tailored interventions, leveraging advancements in artificial intelligence and machine learning, to address individual patient needs effectively. Looking forward, sustained innovation, interdisciplinary collaboration, and strategic investment are advocated to realize the transformative potential of remote monitoring and telemedicine in HF management, thereby advancing patient-centric care paradigms and optimizing healthcare resource allocation.

Artificial Intelligence and Heart-Brain Connections: A Narrative Review on Algorithms Utilization in Clinical Practice

Cardiovascular and neurological diseases are a major cause of mortality and morbidity worldwide. Such diseases require careful monitoring to effectively manage their progression. Artificial intelligence (AI) offers valuable tools for this purpose through its ability to analyse data and identify predictive patterns. This review evaluated the application of AI in cardiac and neurological diseases for their clinical impact on the general population. We reviewed studies on the application of AI in the neurological and cardiological fields. Our search was performed on the PubMed, Web of Science, Embase and Cochrane library databases. Of the initial 5862 studies, 23 studies met the inclusion criteria. The studies showed that the most commonly used algorithms in these clinical fields are Random Forest and Artificial Neural Network, followed by logistic regression and Support-Vector Machines. In addition, an ECG-AI algorithm based on convolutional neural networks has been developed and has been widely used in several studies for the detection of atrial fibrillation with good accuracy. AI has great potential to support physicians in interpretation, diagnosis, risk assessment and disease management.

Assessing the severity of AstraZeneca COVID-19 vaccine-related side effects through pulse spectrum analysis

AstraZeneca (AZ) vaccine is one of the most common vaccines against COVID-19 used globally. However, adverse reactions post-vaccination have been reported, including severe symptoms and cases of sudden death within several hours. Therefore, this study aimed to establish a database of spectral characteristics of blood pressure waveforms (BPWs) for the AZ vaccine and analyze reactions after vaccine administration using objective physiological signal and symptom analyses for identifying potential differences between heavy and slight groups defined in the study. In total, 24 participants were enrolled in the case-control study. BPW measurements were acquired pre- and post-vaccination. A questionnaire survey on side effects was conducted 5 days after vaccination. The related spectral characteristics of heavy and slight groups were acquired after Fourier transform analysis. Four types of harmonic indexes from BPW signals, including amplitude proportion (Cn), coefficient of variation of Cn (CVn), phase angle (Pn), and standard deviation of Pn (Pn_SD), were derived. The characteristics of harmonic indexes of arterial BPW for the AZ vaccine were in C6 (P = .011), CV2 (P = .027), P5 (P = .009), and P2_SD (P = .027) on the radial pulse. C5 (P = .037), C8 (P = .007), C9 (P = .037), CV5 (P = .015), CV8 (P = .005), and CV9 (P = .028) were significantly different at posttest between heavy and slight groups. In both pretest or posttest, C8 was almost significantly different between slight and heavy groups. More parameters changed significantly post-vaccination, with more severe side effects. Most average values of posttest/pretest of CVn and Pn_SD in the slight group exceeded 100%. All average values of posttest/pretest of CVn and Pn_SD in the heavy group were smaller than 100%. This approach may enable prediction of the risk of reactions post-vaccination to determine suitability of the AZ vaccine and evaluation of side effect severity in vaccinated individuals using pulse analysis to ensure relevant precautions are taken.

Using Arterial Pulse and Laser Doppler Analyses to Discriminate between the Cardiovascular Effects of Different Running Levels

BACKGROUND AND AIMS

Running can induce advantageous cardiovascular effects such as improved arterial stiffness and blood-supply perfusion. However, the differences between the vascular and blood-flow perfusion conditions under different levels of endurance-running performance remains unclear. The present study aimed to assess the vascular and blood-flow perfusion conditions among 3 groups (44 male volunteers) according to the time taken to run 3 km: Level 1, Level 2, and Level 3.

METHODS

The radial blood pressure waveform (BPW), finger photoplethygraphy (PPG), and skin-surface laser-Doppler flowmetry (LDF) signals of the subjects were measured. Frequency-domain analysis was applied to BPW and PPG signals; time- and frequency-domain analyses were applied to LDF signals.

RESULTS

Pulse waveform and LDF indices differed significantly among the three groups. These could be used to evaluate the advantageous cardiovascular effects provided by long-term endurance-running training, such as vessel relaxation (pulse waveform indices), improvement in blood supply perfusion (LDF indices), and changes in cardiovascular regulation activities (pulse and LDF variability indices). Using the relative changes in pulse-effect indices, we achieved almost perfect discrimination between Level 3 and Level 2 (AUC = 0.878). Furthermore, the present pulse waveform analysis could also be used to discriminate between the Level-1 and Level-2 groups.

CONCLUSIONS

The present findings contribute to the development of a noninvasive, easy-to-use, and objective evaluation technique for the cardiovascular benefits of prolonged endurance-running training.

Discrimination between possible sarcopenia and metabolic syndrome using the arterial pulse spectrum and machine-learning analysis

Sarcopenia is defined as decreased skeletal muscle mass and function, and is an important cause of frailty in the elderly, also being associated with vascular lesions and poor microcirculation. The present study aimed to combine noninvasive pulse measurements, frequency-domain analysis, and machine learning (ML) analysis (1) to determine the effects on the pulse waveform induced by sarcopenia and (2) to develop discriminating models for patients with possible sarcopenia. Radial blood pressure waveform (BPW) signals were measured noninvasively for 1 min in 133 subjects who visited Tri-Service General Hospital for geriatric health checkups. They were assigned to a robust group and a possible-sarcopenia group that combined dynapenia, presarcopenia, and sarcopenia. Two classification methods were used: ML analysis and a self-developed scoring system that used 40 harmonic pulse indices as features: amplitude proportions and their coefficients of variation, and phase angles and their standard deviations. Significant differences were found in several spectral indices of the BPW between possible-sarcopenia and robust subjects. Threefold cross-validation results indicated excellent discrimination performance, with AUC equaling 0.77 when using LDA and 0.83 when using our scoring system. The present noninvasive and easy-to-use measurement and analysis method for detecting sarcopenia-induced changes in the arterial pulse transmission condition could aid the discrimination of possible sarcopenia.

Significance of different offending vessels and development of a potential screening tool for trigeminal neuralgia

OBJECTIVES

This study was performed amongst trigeminal neuralgia (TN) patients with neurovascular contact (NVC) to 1) investigate the association of the demographic and radiologic factors/variables with TN occurrence, and 2) develop a screening tool for TN/TN-affected nerves based on the factors/variables associated with it.

METHODS

Eighty-five TN patients were recruited, and 121 trigeminal nerves with NVC were derived from them. Based on MRI sequences, including balanced turbo field echo and enhanced T1 high-resolution isotropic volume excitation, radiologic factors/variables for each nerve, from the offending vessel to the presence of nerve displacement, were identified by a neuroradiologist and a neurosurgeon. Demographic and clinical data were obtained from clinical notes. Logistic regression was performed to assess the association of the factors/variables with TN occurrence (i.e., affected vs. unaffected nerves).

RESULTS

Three factors/variables were significantly (p < 0.05) associated with TN occurrence amongst patients with NVC: nerve laterality, vertebral artery (VA) involvement, and the presence of nerve displacement. The nerves with VA involvement, those on the right side, and those with nerve displacement exhibited a significantly higher likelihood/odd of being affected by TN, compared to those without VA involvement, those on the left side, and those without nerve displacement, respectively. Based on these factors/variables, a screening tool/nomogram with acceptable accuracy was established (C-statistic/AUC = 0.80).

CONCLUSIONS

This study revealed an association of the three radiologic factors/variables with TN occurrence. A screening tool for TN/TN-affected nerves was established based on them. The findings may lay a foundation for an improvement of the diagnosis and clinical management of TN.

KEY POINTS

• VA involvement and nerve displacement could be identified using MRI, and are significantly associated with TN occurrence. • A potential objective screening tool/nomogram for TN/TN-affected nerves could be established based on the three radiologic factors/variables: VA involvement, the presence of nerve displacement, and nerve laterality. • The screening accuracy of the tool/nomogram is acceptable as the C-statistic is 0.80.

The temporal dedifferentiation of global brain signal fluctuations during human brain ageing

The variation of brain functions as healthy ageing has been discussed widely using resting-state brain imaging. Previous conclusions may be misinterpreted without considering the effects of global signal (GS) on local brain activities. Up to now, the variation of GS with ageing has not been estimated. To fill this gap, we defined the GS as the mean signal of all voxels in the gray matter and systematically investigated correlations between age and indices of GS fluctuations. What's more, these tests were replicated with data after hemodynamic response function (HRF) de-convolution and data without noise regression as well as head motion data to verify effects of non-neural information on age. The results indicated that GS fluctuations varied as ageing in three ways. First, GS fluctuations were reduced with age. Second, the GS power transferred from lower frequencies to higher frequencies with age. Third, the GS power was more evenly distributed across frequencies in ageing brain. These trends were partly influenced by HRF and physiological noise, indicating that the age effects of GS fluctuations are associated with a variety of physiological activities. These results may indicate the temporal dedifferentiation hypothesis of brain ageing from the global perspective.

Discrimination of the Cognitive Function of Community Subjects Using the Arterial Pulse Spectrum and Machine-Learning Analysis

Early identification of cognitive impairment would allow affected patients to receive care at earlier stage. Changes in the arterial stiffness have been identified as a prominent pathological feature of dementia. This study aimed to verify if applying machine-learning analysis to spectral indices of the arterial pulse waveform can be used to discriminate different cognitive conditions of community subjects. 3-min Radial arterial blood pressure waveform (BPW) signals were measured noninvasively in 123 subjects. Eight machine-learning algorithms were used to evaluate the following 4 pulse indices for 10 harmonics (total 40 BPW spectral indices): amplitude proportion and its coefficient of variation; phase angle and its standard deviation. Significant differences were noted in the spectral pulse indices between Alzheimer’s-disease patients and control subjects. Using them as training data (AUC = 70.32% by threefold cross-validation), a significant correlation (R² = 0.36) was found between the prediction probability of the test data (comprising community subjects at two sites) and the Mini-Mental-State-Examination score. This finding illustrates possible physiological connection between arterial pulse transmission and cognitive function. The present findings from pulse-wave and machine-learning analyses may be useful for discriminating cognitive condition, and hence in the development of a user-friendly, noninvasive, and rapid method for the early screening of dementia.

Age-Related Changes in the Characteristics of the Elderly Females Using the Signal Features of an Earlobe Photoplethysmogram

Non-invasive measurement of physiological parameters and indicators, specifically among the elderly, is of utmost importance for personal health monitoring. In this study, we focused on photoplethysmography (PPG), and developed a regression model that calculates variables from the second (SDPPG) and third (TDPPG) derivatives of the PPG pulse that can observe the inflection point of the pulse wave measured by a wearable PPG device. The PPG pulse at the earlobe was measured for 3 min in 84 elderly Korean women (age: 71.19 ± 6.97 years old). Based on the PPG-based cardiovascular function, we derived additional variables from TDPPG, in addition to the aging variable to predict the age. The Aging Index (AI) from SDPPG and Sum of TDPPG variables were calculated in the second and third differential forms of PPG. The variables that significantly correlated with age were c/a, Tac, AI of SDPPG, sum of TDPPG, and correlation coefficient ‘r’ of the model. In multiple linear regression analysis, the r value of the model was 0.308, and that using deep learning on the model was 0.839. Moreover, the possibility of improving the accuracy of the model using supervised deep learning techniques, rather than the addition of datasets, was confirmed.