Electrodermal activity and heart rate variability for detection of peripheral abnormalities in type 2 diabetes: A review

Modern medicine exhibits an upward trend towards non-invasive methods for early detection of disease and long-term monitoring of patients' health. Diabetes mellitus and its complications are a promising area for implementation of new medical diagnostic devices. One of the most serious complications of diabetes is diabetic foot ulcer. The main causes responsible for diabetic foot ulcer are ischemia caused by peripheral artery disease and diabetic neuropathy caused by polyol pathway-induced oxidative stress. Autonomic neuropathy impairs function of sweat glands, which can be measured by electrodermal activity. On the other hand, autonomic neuropathy leads to changes in heart rate variability, which is used to assess autonomic regulation of the sinoatrial node. Both methods are enough sensitive to detect pathological changes caused by autonomic neuropathy and are promising screening methods for early diagnosis of diabetic neuropathy, which could prevent the onset of diabetic ulcer.

The Evaluation of Remote Monitoring Technology Across Participants With Different Skin Tones

BACKGROUND

Many research studies seek to improve vital sign monitoring to enhance the conditions under which doctors and caregivers track patients' health. Non-invasive and contactless monitoring has emerged as an optimal solution for this problem, with telemedicine, self-monitoring, and well-being tools being the next generation of technology in the biomedical field. However, there is worldwide concern about the general purpose and bias toward a certain demographic group of these techniques. In particular, skin tone and the accuracy of monitoring dark skin tone groups have been key questions among researchers, with the lack of results and studies contributing to this uncertainty.

METHODS

This paper proposes a benchmark for remote monitoring solutions against a medical device across different skin tone people. Around 330 videos from 90 patients were analyzed, and heart rate (HR) and heart rate variability (HRV) were compared across different subgroups. The Fitzpatrick scale (1-6) was used to classify participants into three skin tone groups: 1 and 2, 3 and 4, and 5 and 6.

RESULTS

The results showed that our proposed methodology could estimate heart rate with a mean absolute error of 3 bpm across all samples and subgroups. Moreover, for heart rate variability (HRV) metrics, we achieved the following results: in terms of mobility assistive equipment (MAE), the HRV-inter-beat interval (IBI) was 10 ms, the HRV-standard deviation of normal to normal heartbeats (SDNN) was 14 ms, and the HRV-root mean square of successive differences (RMSSD) between normal heartbeats was 22 ms. No significant performance decrease was found for any skin tone group, and there was no error trend toward a certain group.

CONCLUSIONS

The study showed that our methodology meets acceptable agreement levels for the proposed metrics. Furthermore, the experiments showed that skin tone did not impact the results, which remained within the same range across all groups. Moreover, it enables the end users to understand their general well-being and improve their overall health.

Evaluation of Atrial Fibrillation Detection in Short-Term Photoplethysmography (PPG) Signals Using Artificial Intelligence

Background Atrial fibrillation (AFIB) is a common atrial arrhythmia that affects millions of people worldwide. However, most of the time, AFIB is paroxysmal and can pass unnoticed in medical exams; therefore, regular screening is required. This paper proposes machine learning (ML) methods to detect AFIB from short-term electrocardiogram (ECG) and photoplethysmography (PPG) signals. Aim Several experiments were conducted across five different databases, with three of them containing ECG signals and the other two consisting of only PPG signals. Experiments were conducted to investigate the hypothesis that an ML model trained to predict AFIB from ECG segments could be used to predict AFIB from PPG segments. Materials and methods A random forest (RF) ML algorithm achieved the best accuracy and achieved a 90% accuracy rate on the University of Mississippi Medical Center (UMMC) dataset (216 samples) and a 97% accuracy rate on the Medical Information Mart for Intensive Care (MIMIC)-III datasets (2,134 samples). Results A total of 269,842 signal segments were analyzed across all datasets (212,266 were of normal sinus rhythm (NSR) and 57,576 corresponded to AFIB segments). Conclusions The ability to detect AFIB with significant accuracy using ML algorithms from PPG signals, which can be acquired via non-invasive contact or contactless, is a promising step forward toward the goal of achieving large-scale screening for AFIB.

A Systematic Review of Heart Rate Variability as a Measure of Stress in Medical Professionals

Understanding the physiological effects of responding to crises is a critical component in understanding how to manage and prepare medical professionals to be crisis responders. Heart rate variability (HRV) is the variation in rate between a succession of R-R intervals. This variation is not only affected by physiological processes such as respiration or metabolic rate but is also directly controlled by the autonomic nervous system. As such, heart rate variability has been proposed as a noninvasive tool to measure the physiological stress response. The aim of this systematic review is to consolidate heart rate variability literature in the context of medical emergencies to determine if heart rate variability changes predictably from baseline when responding to medical crises. This may demonstrate utility as an objective, noninvasive measure of stress response. A systematic literature review of six databases yielded 413 articles, 17 of which met our inclusion criteria of being written in English, measuring HRV in healthcare providers, and measuring HRV in real or simulated medical resuscitations or procedures. Articles were then analyzed using the Grades of Recommendation, Assessment, Development, and Evaluation (GRADE) scoring system. Out of the 17 articles reviewed, 11 demonstrated statistically significant results showing heart rate variability responding in a predictable manner to stress. Three articles utilized a medical simulation as the stressor, six used medical procedures, and eight used medical emergencies encountered during clinical work. Overall, a predictable trend in heart rate variability metrics of standard deviation from the mean value of normal-to-normal (N-N) intervals (SDNN), root mean square of the successive differences (RMSSD), mean number of times per time interval in which the change in successive normal sinus (N-N) intervals exceeds 50 ms (PNN50), low frequency % (LF%), and low-frequency-to-high-frequency ratio (LF/HF) was observed when responding to stress. This systematic literature review showed that heart rate variability among healthcare providers responding to stressful scenarios follows a predictable pattern of change and expands our understanding of the physiology of stress in healthcare providers. This review supports the use of HRV to monitor stress during high-fidelity simulation to ensure that appropriate physiological arousal is achieved during the training of medical personnel.

Empirical Mode Decomposition as a Novel Approach to Study Heart Rate Variability in Congestive Heart Failure Assessment

Congestive heart failure (CHF) is a cardiovascular disease related to autonomic nervous system (ANS) dysfunction and fragmented patterns. There is a growing demand for assessing CHF accurately. In this work, 24-h RR interval signals (the time elapsed between two successive R waves of the QRS signal on the electrocardiogram) of 98 subjects (54 healthy and 44 CHF subjects) were analyzed. Empirical mode decomposition (EMD) was chosen to decompose RR interval signals into four intrinsic mode functions (IMFs). Then transfer entropy (TE) was employed to study the information transaction among four IMFs. Compared with the normal group, significant decrease in TE (*→1; information transferring from other IMFs to IMF1, p < 0.001) and TE (3→*; information transferring from IMF3 to other IMFs, p < 0.05) was observed. Moreover, the combination of TE (*→1), TE (3→*) and LF/HF reached the highest CHF screening accuracy (85.7%) in IBM SPSS Statistics discriminant analysis, while LF/HF only achieved 79.6%. This novel method and indices could serve as a new way to assessing CHF and studying the interaction of the physiological phenomena. Simulation examples and transfer entropy applications are provided to demonstrate the effectiveness of the proposed EMD decomposition method in assessing CHF.

An efficient method of addressing ectopic beats: new insight into data preprocessing of heart rate variability analysis

Heart rate variability (HRV) analysis is affected by ectopic beats. An efficient method was proposed to deal with the ectopic beats. The method was based on trend correlation of the heart timing signal. Predictor of R-R interval (RRI) value at ectopic beat time was constructed by the weight calculation and the slope estimation of preceding normal RRI. The type of ectopic beat was detected and replaced by the predictor of RRI. The performance of the simulated signal after ectopic correction was tested by the standard value using power spectrum density (PSD) estimation, whereas the results of clinical data with ectopic beats were compared with the adjacent ectopic-free data. The result showed the frequency indexes after ectopy corrected had less error than other methods with the test of simulated signal and clinical data. It indicated our method could improve the PSD estimation in HRV analysis. The method had advantages of high accuracy and real time properties to recover the sinus node modulation.

Assessment of autonomic control and respiratory sinus arrhythmia using point process models of human heart beat dynamics

Tracking the autonomic control and respiratory sinus arrhythmia (RSA) from electrocardiogram and respiratory measurements is an important problem in cardiovascular control. We propose a point process adaptive filter algorithm based on an inverse Gaussian model to track heart beat intervals that incorporates respiratory measurements as a covariate and provides an analytic form for computing a dynamic estimate of RSA gain. We use Kolmogorov-Smirnov tests and autocorrelation function analyses to assess model goodness-of-fit. We illustrate the properties of the new dynamic estimate of RSA in the analysis of simulated heart beat data and actual heart beat data recorded from subjects in a four-state postural study of heart beat dynamics: control, sympathetic blockade, parasympathetic blockade, and combined sympathetic and parasympathetic blockade. In addition to giving an accurate description of the heart beat data, our adaptive filter algorithm confirms established findings pointing at a vagally mediated RSA and provides a new dynamic RSA estimate that can be used to track cardiovascular control between and within a broad range of postural, pharmacological, and age conditions. Our paradigm suggests a possible framework for designing a device for ambulatory monitoring and assessment of autonomic control in both laboratory research and clinical practice.