Preejith Sreeletha Premkumar

Bio: Preejith Sreeletha Premkumar is an academic researcher from Indian Institute of Technology Madras. The author has an hindex of 1, co-authored 1 publications receiving 9 citations.

A Yellow–Orange Wavelength-Based Short-Term Heart Rate Variability Measurement Scheme for Wrist-Based Wearables

Abstract: Heart rate variability (HRV) is one of the important biomarkers of physical and psychological well-being. Hence, a convenient and minimally intrusive method for HRV measurement is advantageous. Although high levels of surrogacy of short-term HRV estimates obtained from the measurements of blood volume changes to traditional electrocardiographic (ECG) measurements have been reported, no detailed account on extraction of such parameters from a wrist-based optical monitor is found in the literature. In this paper, a yellow–orange wavelength-based optical scheme is incorporated into a wearable device for HRV estimation from dorsal side of the wrist. This design is pivotal in catering to a wider span of population with varied skin tones. The developed wearable in alliance with a gateway device is capable of picking up photoplethysmography from the measurement site, allowing estimation of HRV-indices within a confidence of 5% from ECG-derived parameters. The HRV measurement ecosystem is validated under the setting of three postural loads for 20 subjects, generating 60 data sets. Study results show statistically significant positive correlation and nonsignificant bias in Bland–Altman analysis, for the HRV-indices derived from either method. In most of the extracted HRV features, the observations in supine position showed minimum deviation from the reference. Estimation of short-term HRV-indices from wrist-based photoplethysmography under stationary conditions shows promising results from the study. Electrical and biological noninterference and ease of usage of the proposed design simplify stationary and ambulatory monitoring of HRV.

Pulse rate variability in cardiovascular health: a review on its applications and relationship with heart rate variability

Abstract: Heart rate variability has been largely used for the assessment of cardiac autonomic activity, due to the direct relationship between cardiac rhythm and the activity of the sympathetic and parasympathetic nervous system. In recent years, another technique, pulse rate variability, has been used for assessing heart rate variability information from pulse wave signals, especially from photoplethysmography, a non-invasive, non-intrusive, optical technique that measures the blood volume in tissue. The relationship, however, between pulse rate variability and heart rate variability is not entirely understood, and the effects of cardiovascular changes in pulse rate variability have not been thoroughly elucidated. In this review, a comprehensive summary of the applications in which pulse rate variability has been used, with a special focus on cardiovascular health, and of the studies that have compared heart rate variability and pulse rate variability is presented. It was found that the relationship between heart rate variability and pulse rate variability is not entirely understood yet, and that pulse rate variability might be influenced not only due to technical aspects but also by physiological factors that might affect the measurements obtained from pulse-to-pulse time series extracted from pulse waves. Hence, pulse rate variability must not be considered as a valid surrogate of heart rate variability in all scenarios, and care must be taken when using pulse rate variability instead of heart rate variability. Specifically, the way pulse rate variability is affected by cardiovascular changes does not necessarily reflect the same information as heart rate variability, and might contain further valuable information. More research regarding the relationship between cardiovascular changes and pulse rate variability should be performed to evaluate if pulse rate variability might be useful for the assessment of not only cardiac autonomic activity but also for the analysis of mechanical and vascular autonomic responses to these changes.

A Review of Methods for Non-Invasive Heart Rate Measurement on Wrist

Abstract: When evaluating general health condition on a patient, heart rate is an essential indicator as it is directly representative of the cardiac system state. Continuous measurement methods of heart rate are required for ambulatory monitoring involved in preliminary diagnostic indicators of cardiac diseases or stroke. The growing number of recent developments in wearable devices is reflective of the increasing demand in wrist-worn activity trackers for fitness and health applications. Indeed, the wrist represents a convenient location in terms of form factor and acceptability for patients. While most commercially-available devices are based on optical methods for heart rate measurement, others methods were also developed, based on various physiological phenomena. This review is focused on heart rate measurement methods located on forearm and more specifically on the wrist. For each method, the physiological mechanism involved is described, and the associated transducers for bio-signal acquisition as well as practical developments and prototypes are presented. Methods are discussed on their advantages, limitations and their suitability for an ambulatory use. More specifically, the superposition of motion artefacts over the signal of interest is one of the largest drawbacks for these methods, when used out of laboratory conditions. As such, artefact reduction techniques proposed in the literature are also presented and discussed.

Assessing drivers' physiological responses using consumer grade devices

Abstract: The ability to measure drivers’ physiological responses is important for understanding their state and behavior under different driving conditions. Such measurements can be used in the development of novel user interfaces, driver profiling, advanced driver assistance systems, etc. In this paper, we present a user study in which we performed an evaluation of two commercially available wearable devices for assessment of drivers’ physiological signals. Empatica’s E4 wristband measures blood volume pulse (BVP), inter-beat interval (IBI), galvanic skin response (GSR), temperature, and acceleration. Bittium’s Faros 360 is an electrocardiographic (ECG) device that can record up to 3-channel ECG signals. The aim of this study was to explore the use of such devices in a dynamic driving environment and their ability to differentiate between different levels of driving demand. Twenty-two participants (eight female, 14 male) aged between 18 and 45 years old participated in the study. The experiment compared three phases: Baseline (no driving), easy driving scenario, and demanding driving scenario. Mean and median heart rate variability (HRV), standard deviation of R–R intervals (SDNN), HRV variables for shorter time frames (standard deviation of the average R–R intervals over a shorter period—SDANN and mean value of the standard deviations calculated over a shorter period—SDNN index), HRV variables based on successive differences (root mean square of successive differences—RMSSD and percentage of successive differences, greater than 50 ms—pNN50), skin temperature, and GSR were observed in each phase. The results showed that motion artefacts due to driving affect the GSR recordings, which may limit the use of wrist-based wearable devices in a driving environment. In this case, due to the limitations of the photoplethysmography (PPG) sensor, E4 only showed differences between non-driving and driving phases but could not differentiate between different levels of driving demand. On the other hand, the results obtained from the ECG signals from Faros 360 showed statistically significant differences also between the two levels of driving demand.

Wearable Devices and Diagnostic Apps: Beyond the Borders of Traditional Medicine, But What About Their Accuracy and Reliability?

Abstract: Nowadays people are willing to self-monitor their health status, and when they do not feel well, they tend to ask Dr. Google for a diagnosis (over a third of adults go online to analyze or look for information about a health condition [1]). People trust technology, often more than physicians; smartphone and Artificial Intelligence (AI) technologies are undoubtedly making innovative monitoring and diagnostic devices rapidly progress, so much that it seems that the future of medicine is in smartphones, where apps may run and to which devices can be connected, hence supporting mobile health (m-Health) [2]. In addition to smartwatches and wrist-worn devices that are surely the most common wearable devices [3], [4], there are also connected wearable clothes [5], socks [6], rings, or glasses-type wearables [7].

Heart Rate Variability Analysis With Wearable Devices: Influence of Artifact Correction Method on Classification Accuracy for Emotion Recognition

Abstract: Heart Rate Variability (HRV) analysis is widely explored in several application fields, such as emotion recognition. Photoplethysmographic (PPG) signals are often considered for this analysis because of their large use in wearable devices. However, quality of these signals (in terms of added disturbances) could be not always optimal, since they are susceptible to many factors, e.g. motion artifacts, ambient light, pressure of contact, skin color and conditions. Therefore, methods for artifacts correction play a pivotal role and consequently influence the results. This paper aims at proposing a new data artifacts correction method to improve the classification performance in emotion recognition, considering PPG signals during audio stimulation, and a Support Vector Machine (SVM) classifier. Results show that the proposed method provides a better classification in stimuli detection (66.67%) with respect to data pre-processing performed with a standard tool (Kubios, 48.81%); however, for further improvement, other signals could be considered in combination with PPG, such as the electrodermal activity (EDA).