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Research Article
Open Access Peer-reviewed

Comparing Heart Rate Variability with Polar H10 Sensor and Pulse Rate Variability with LYFAS: A Novel Study

Subhagata Chattopadhyay , Rupam Das
Journal of Biomedical Engineering and Technology. 2021, 9(1), 1-9. DOI: 10.12691/jbet-9-1-1
Received October 17, 2021; Revised November 20, 2021; Accepted December 01, 2021

Abstract

Background: Heart Rate Variability (HRV) surrogates for Cardiac Autonomic Modulation (CAM), while Pulse Rate variability (PRV) reflects Cardiovascular Autonomic Modulation (CvAM). HRV and PRV therefore, are not exactly interchangeable terms. However, the paper proposes that PRV and HRV can be correlated. Aim: To compare the correlation between two different instruments – (I) gold-standard Polar H10 HRV sensor that works on the principle of CAM and Kubios software for HRV estimation, together called ‘PK’ and (II) Lyfas smartphone application called ‘L’, which estimates PRV that reflects the state of CvAM. Methods: Parameters, such as (i) HR, (ii) RMSSD, (iii) pNN50, (iv) SDNN, and (v) LF/HF are captured from a total of 567 healthy Indian adults (312 males and 255 females) using PK and L simultaneously. End HR data (120 sec) are then compared instrument and gender-wise statistically by computing the i) RMSE, ii) Regressions (L on PK), iii) a Two-sample t-test, and (iv) classification accuracy of L when compared to PK. Finally, the Sensitivity/recall (R), Specificity (S), Precision (P), Accuracy (A), F score (F), and Youden’s index (Y) are computed for L. Results: L shows encouraging averages for males and females respectively ‘R’ (84%, 70%), ‘S’ (95%, 87%), ‘A’ (82%, 79%), ‘P’ (87%, 69%),’F’ (1.4836, 1.3706) and ‘Y’ (79%, 81%) for HR, SDNN, RMSSD, and pNN50 respectively. Conclusions: Although ‘L’ has 1/33th times less resolution compared to PK, its technical efficacy, speed, user-friendliness, low-cost-benefit, high ‘Y’ values, and ubiquitousness pose a great advantage in its deployment in the clinical setup as a biomarker tool over PK.

1. Introduction

The heart is not a metronome and hence it does not beat at a uniform rhythm and amplitude 1. Heart Rate (HR) is the number of times the heart contracts/beats per minute (bpm), which varies between 60 (below which is bradycardia) to 100 (above which is tachycardia) in adults 2. Heart Rate Variability (HRV) is the degree of fluctuation in the length of the intervals between two consecutive heartbeats 3. The intervals are named RR or NN intervals (interval between two R wave peaks), measured in milliseconds (ms), and reflect the regularity of heartbeats 4. More the regularity, lower will be the variability in heartbeats and vice-versa.

Cardiovascular muscles are supplied with the Autonomic Nervous Systems (ANS), which has two major components: the sympathetic (SNS) and the parasympathetic nervous system (PNS) 5. Heart Rate is controlled by both for balancing its functions. It may be increased by SNS (mediated by the release of epinephrine and norepinephrine) lowering the HRV and decreased by PNS activation (mediated by releasing acetylcholine from the vagal nerve) elevating the HRV 5.

Regulation of HRV by ANS is reflective of the state of respiration, exercises, hormonal reaction, metabolism, cognitive processes, stress and recovery, Blood Pressure (BP), gas exchange, gut motility, vascular tone i.e., the size of the diameter of the blood vessels that regulate BP, and possibly regulating the tonicity of facial muscles 6. Besides the afore-mentioned non-emotional tasks of ANS, there have also been attempts to characterize the ANS changes associated with emotions 7. Hence, HRV can provide insights into various physiological and psychological control mechanisms.

A healthy heart’s functioning is complex and non-linear, as mentioned above. The dynamic interplay between ANS and HRV under uncertain and continuously changing environments is possible because of this non-linearity, thereby providing enough flexibility to cope up with that change. This depicts the relevance of the natural complexity of Cardiac Autonomic Modulation (CAM) 8. For instance, during physical activities, we are often asked to focus on exhaling. The body is under SNS activation during exercises marked by high HR and low HRV. Exhalation compensates for the increased HR by playing an opposite force of slowing down the HR (marked by high HRV), and thereby mediating the complementary interplay between the two components of ANS, and further providing endurance to finish off the repetitions.

Pulse Rate Variability (PRV) surrogates for the HRV 9. However, it is not a direct measure, rather a good biomarker, as several factors influence the HRV-correlate scores 10. It measures the Cardiovascular Autonomic Modulation (CvAM), as it captures the HRV-correlates from the peripheral capillary blood vessels using photoplethysmography sensors. The state of stiffness or contractile state of the arterioles leading to loss of peripheral vascular tone is crucial for the correct measurement of the HRV-correlates 11. The stiffness, in turn, can be caused by the ANS modulation through sympathetic overdrive owing to loss of baroreflex function, which is again influenced by various factors, the most important of which is the inherent physiological stress, inflammation, and several metabolic disorders.

It is important not to compartmentalize SNS and PNS as ‘energizing’ and ‘resting’ systems respectively in all the parts of ANS-targeted activities as PNS too plays a role in activating many organs that are critically involved in emotions such as salivation, crying, hydrochloric acid secretion during hunger phase, erection, and gut motility among others 7. This feature helps to derive the gender-specific difference in the ANS activity. Women generally have a more parasympathetic component reflected in higher values of High Frequency (HF) waves and Root Mean Square of the Difference between two consecutive normal heartbeats (RMSSD); while men have a higher sympathetic component as evident by higher values of Low Frequency (LF) waves, Very Low Frequency (VLF) waves, and the Standard Deviation of the NN interval (SDNN) 12. However, a loss or increase in the complexity of these dynamics may represent a deviation from this natural state of ANS functioning giving rise to pathological states in the mind and body, which is reflected by the HRV-correlates 13.

Mapping of HRV under stress responses has well-established the activation of the hypothalamic-pituitary-adrenal axis along with the sympathetic activation and deactivation of the parasympathetic system, which has also been linked to increased levels of cortisol linked to autonomic stress and hence, impacting the overall brain function. The above conditions are reflected in higher LF values and hence, higher LF/HF ratio 14. This is psychologically and physiologically reflective in frustration, anxiety, anger, rage, aggression, attention deficit, further linked to Chronic Post Traumatic Stress Disorder (CPTSD), Attention Deficit Hyperactive Disorder (ADHD), dementia, poor skeletal muscle strength, sleeplessness, and vasoconstriction.

Besides, arterial stiffness, it is also relevant to understand the progression of metabolic multisystem disease like diabetes that influences the HRV. Popularly known as a metabolic syndrome characterized by obesity and insulin resistance, it is often positively associated with depression and hypertension, and in turn, activates the SNS (marked by higher LF/HF value) 15 or metabolic consequences are visible as an effect of higher sympathetic activity, which is further related to longterm inflammation 16.

While the SNS can suppress PNS activity, it can also increase PNS reactivity 17. Parasympathetic rebound may occur following high levels of stress, resulting in increased night-time gastric activity 18 and bronchoconstriction giving rise to the symptoms of bronchial asthma 19.

Psychophysiological coherence is characterized by autonomic balance. HRV variables such as LF, HF, SDNN, RMSSD helps in understanding the PNS and SNS quantitatively with their correlation with character traits. A non-aggressive assertiveness requires a creative approach that is more integrative and adaptive, characterized by more parasympathetic regulation and autonomic balance rather than defensive (i.e., aggression or avoidance) or submissive (i.e., agreement or forgiveness) approaches that are characterized by either greater sympathetic and/or parasympathetic activity 20.

A higher HRV score is warranted with higher cognitive performance e.g. reading, memory, and learning among others 21, which in turn are facilitated and are cumulative results of emotions-induced learning/memory, according to the evolutionary model of emotions 7. The physiological responses like facial expression, muscular tonus, voice, ANS activities, and endocrine activities to produce an optimum response to an event are organized by emotions, which in itself depicts how emotions impose order (and not chaos) and coherence across disparate bio-behavioral systems and not leading to any maladaptive consequences 7. Emotional moments have a stronger influence on memory by enhancing the amygdala and hippocampus activation than non-emotional moments. When an event is organized and synchronized across the ANS, it produces a coherent and specific memory that is pertinent to the physiological conditions at that timestamp 22.

Coherence and chronology in memory is associated with identity formation which further feeds to reasoning, feelings, and cognition processing in the brain that directly impacts one’s behavior with the external world/event. Depressive symptoms, CPTSD, and behavioral problems are found to be negatively related to memory coherence and autobiographical chronology. Additionally, memory coherence is negatively associated with depressive symptoms, behavioral problems, and CPTSD 23.

Thus, HRV and its correlates are the versatile digital biomarkers of evaluating the mind-body homeostasis at a given timestamp. PRV surrogates for HRV as well as arterial health and therefore more versatile. This the study aims to examine the technical efficacy of Lyfas, a smartphone-based non-invasive and ubiquitous biomedical application 24, (a) in capturing the HRV correlates using the optical sensor in the phone camera and phone’s LED as the light source using reflectant Arterial Photoplethysmography (APPG) and then comparing with the findings of the gold standard Polar H10 HRV sensor which captures the HR and rhythm 25, (b) the software of Lyfas that uses heuristics-based analytics, compared with that of the gold standard Kubios software, which calculates the HRV-correlates 26 such as the HR, RMSSD, pNN50, SDNN, and LF/HF, respectively, as well as classifying the health-state, and (c) comparison of the instruments for the convenience factors.

2. Material and Methods

This section describes the reliability study of Lyfas as an HRV capturing tool when compared to Polar H10 HRV sensor and Kubios software. It is structured as follows:

2.1. Lyfas, Polar H10 HRV Sensor, and Kubios Software

A. Lyfas (L): It is a biomedical application, developed at Aculli Labs Pvt. Ltd. in India 27. It applies the optical sensor within the smartphone camera, running on the Android operating systems of version 7 or the higher, and the LED light of the camera. It captures the capillary blood flow of the index finger’s microvasculature using the principle of reflectance arterial photoplethysmography (APPG) 28 and the solutes (blood biochemistry) including the cellular components flowing through the blood using the method of photochromatography (PCG) 28 when the finger is placed on the main rear camera of the phone. From the APPG studies, Lyfas can detect Pulse Rate Variability (PRV) that surrogates for the HRV and its correlates. Therefore, Lyfas measures Cardiovascular Autonomic Modulation (CvAM) and is sensitive to the adaptability (vasoconstriction and vasodilatation) of the peripheral vasculature to maintain the homeostasis of the body. Lyfas uses its proprietary formulae and heuristics to provide comprehensive mind-body analytics that can be used for screening and prognosis of any physiological state of clinical interest. Figure 1 shows one sample Lyfas report.

B. Polar H10 HRV sensor (P): It is an HRV sensor that comes with a wearable chest belt and has a gold standard of high precision and accuracy. It can be connected to multiple types of training devices via Bluetooth and ANT+ 29. The sensor comes with a soft adjustable sensor for wearing it in the chest with the sensor touching the skin of the chest to capture the HR in real-time.

C. Kubios software (K): Kubios is a scientifically validated gold-standard software for HRV analysis and hence has widely been used in biomedical research across the globe 30. Although it is device-independent, it is recommended to use another gold-standard ECG device or Polar H10 HRV sensor for accurate collection of the HR data 31. Kubios software captures the CAM, whereas Lyfas captures CvAM. Figure 2 shows a snapshot of Kubios HRV report.

In this study to compare the reliability of Lyfas (L), the authors have used the combination of Polar H10 HRV sensor & Kubios software (PK) in the subjects. It is important to note that PK is the Instrument-1 while L is the Instrument-2, compared in this work.

2.2. Research Ethics

• The study protocol was approved by the Vagas Institutional Ethics Committee review board (No. ECR/1181/Inst/KA 2019, dated 30-01-2020).

Signed informed consents of all participants’ have been taken according to the declaration of Helsinki by the research team prior test.

2.3. Recruitment of the Subjects and Conducting the Tests

During 6 months (1st February 2021 to 18th July 2021), a total of 567 healthy adults comprising of 312 males (mean age 35.6 years, median age 35 years, mean BMI 23.44, median BMI 23.12; mean Blod Pressure (BP) 125.67/83.12 mm Hg, median BP 125/82 mm Hg; mean Pulse Rate (PR) 87 bpm, median PR 86.5 bpm) and 255 females (mean age 38.8 years, median age 38 years, mean BMI 23.74, median BMI 23.62; mean Blod Pressure (BP) 122.22/80.92 mm Hg, median BP 122/80 mm Hg; mean Pulse Rate (PR) 85.7 bpm, median PR 85.5 bpm) without any known comorbidity was recruited as the subjects. Figure 3 shows how the tests are carried.

2.4. The Study

The study aims to compare two methods – (a) HRV, captured by Polar H10 sensor (compatibility: iPhone 4S and later and OS version 4.3 or later or Android devices with Bluetooth 4.0; Battery: CR2025; Battery life: 400 hrs BLE and 5KHz transmission active) plus Kubios HRV software 3.5.0 and (b) PRV, captured by Lyfas (version: 9.1 running on Android version 11 RKQ1.200826.002; primary camera: Quad 64MP, f/1.9, 26mm wide, 1/1.72”, 0.8 µm, PDAF) simultaneously using the following steps. Data has been captured simultaneously at the 120th second, while HR is calculated twice – at the 60th sec and then at 120th sec for each subject using each method. The time taken to complete one test end-to-end is noted for each subject and the mean and median time has been noted. It is important to note that the end heart rate and the HRV correlate at 120 sec at Lyfas and Polar H10 sensor has been considered for comparison 32. Figure 2 shows how the tests have been performed.

HRV analysis using Polar H10 sensor plus Kubios HRV software

• Step-1: capturing the HR data by Polar H10 sensor attached to the flexible chest belt

• Step-2: transferring the ECG data (lead II) of Polar H10 sensor to HRV XT Pro software using low energy Bluetooth (BLE)

• Step-3: raw data obtained from HRV XT Pro software is downloaded using USB to a PC, preloaded with Kubios software

• Step-4: data wrangling is done with Kubios software manually

• Step-5: report generation and HRV data visualization.

HRV analysis Lyfas

• Step-1: capturing the PRV data (surrogates for the HRV data) by the optical sensor in the smartphone camera and LED light from the index finger capillary

• Step-2: computing the HRV correlates using its in-built software having formulae and heuristics

• Step-3: analyze the HRV data

• Step-4: report generation and HRV data visualization.

HR & HRV correlates in this study

HR (bpm): Number of heartbeats per minute. The normal range in adults is 56-102 bpm 33.

SDNN (ms): It is the standard deviation of the NN interval and predicts the health status based on 24-hours monitoring. SDNN <50 ms is classified as unhealthy, 50-100 ms as compromised health, and >100 as healthy state 34.

RMSSD (ms): Root Mean Square of successive differences between two consecutive HR. For adults. The normal range in adults is 18-74. Fewer values indicate health issues 33. It is the most important HRV-correlate in real life because it reflects the vagus-mediate PNS activity which can be evident by beat-by-beat changes 32.

pNN50 (%): It is a pair of R-R intervals that differs more than 50 milliseconds 35. The normal range in adults is 2-68. Below 2% reflects health issues 33.

LF/HF: It is a ratio of Low and High-Frequency waves. The normal range of HF in adults is 0.15 - 0.40 while 0.04 - 0.15 for LF. Hence, the ratio is around 1.5-2.0. Values more than 2 are associated with health issues 36. It is important to note that HF values surrogate for the PNS activities, while SNS activity is reflected by the LF. It is worth noting that HF and LF both are influenced by breathing and therefore, RMSSD poses to be the best indicator for PNS activity in the body 32. LF/HF comes under the frequency domain, while pNN50, SDNN, and RMSSD fall under the time domain and it is important to note that the normal ranges that are mentioned above could be different from one instrument to another instrument.

2.5. Data Analysis to Measure the Reliability of Lyfas in Comparison to Polar H10 HRV Sensor and Kubios Software

Data is analyzed using Python 3.8 having Spyder editor version 5.1 on a Windows 10 Pro PC with Intel®CoreTM i5-3360M CPU @ 2.8 GHz. Following standard statistical analyses have been conducted:

a) Descriptive statistics: Mean, Median, and Standard deviation of each of the HR and HRV-correlates have been measured gender-wise and shown in Table 1.

b) Regression: At first, HR and HRV data obtained by L (independent variable) are regressed on the data obtained simultaneously by PK (dependent variable), and R2 values (coefficient of determination) are computed (see equation 1) and then

(1)

Where, SSR and TSS denote Sum of the squares of the residual (explained variance) and the Total sum of the square (total variance), respectively. Values close to 1.0 are considered to be a good fit for the model.

b) Gender-wise Root Mean Square Error (RMSE) is shown in equation 2 of each of the HRV correlates, which are captured by the L and with PK are compared for ‘n’ number of observations (i varies from 1 to n).

(2)

The good RMSE lies between 0.2 to 0.5, which indicates that the model can relatively predict the data accurately 1.

c) A Two-sample t-test (t) 2: It compares the means of two observation values as a pair with a Confidence Interval (CI) kept as 95%, which means that there is 95% confidence that there are true differences in the means and reject the null hypothesis. It can be viewed in equation 3. According to ‘t’ the null hypothesis is that there is no difference between the actual means of the observations; however, this study hypothesizes that there must be differences among the means.

(3)

All attributes of equation 3 are already described in equation 2. It is important to note that the ‘t’ results have two components – (i) test statistic (higher values goes in favor of rejection of the null hypothesis) and (ii) p-value, if <0.05 (CI 95%) indicates that there is a statistically significant difference in the mean values.

d) Classification of HRV-correlates: Accepted values of all attributes, i.e., HR and HRV-correlates are mentioned in Table 2. HR and HRV-correlate values, obtained from L and PK are carefully assessed for each subject. Any value that falls under the category of ‘Acceptable’ is treated as ‘No concern class’, while the values beyond the acceptable range are termed as ‘Concerning class’, which means there could be an associated health risk in these cases. In this step, the authors have examined whether there is any discrepancy in comprehending the classes by L when compared to the ‘class’ obtained from PK.

Sensitivity or Recall (R), Specificity (S), Precision (P), Accuracy (A), (F) F-score, and Youden’s index (Y) are computed using equations 4 to 9, respectively. It is important to mention here that ‘Y’ refers to an industry-standard statistical validation of the efficacy of a diagnostic device or tool in a real-world clinical setup, where any value more than or equal to 50% indicates that the tool or device is qualified to be used for diagnostic purposes 3. Results are shown below and discussed.

(4)
(5)
(6)
(7)
(8)
(9)

In the above equations 4-9, TP, FP, TN, and FN refer to True Positive, False Positive, True Negative, and False Negative, respectively. Table 4 shows how good L can predict the class through the HR and HRV-correlate values it has captured when compared to the values captured by PK.

3. Results and Discussions

In this section results obtained from the above experiments have been shown and described.

a) Descriptive statistics of HR and HRV-correlates are shown in Table 2a (males) and 2b (females).

From the tables, the following observations can be made:

i) Males and female data do not show many dissimilarities in its distribution in all the parameters obtained by L and PK

ii) The differences between the mean and median in HR, SDNN, and LF/HF are much less, compared to RMSSD and pNN50.

However, mere numeric comparison between the values obtained by L and PK has got no significance, because of the differences in their acceptable ranges as the former measures the PRV while the latter captures HRV. Hence, the authors examined whether there is any classification error (refer to Table 4a and 4b for males and females, respectively) by L when compared to the values obtained from PK and this is more meaningful physiologically.

b) Figure 4. Shows the results of Regression results, according to the gender, and HRV biomarkers.

c) RMSE values can also be found in Figure 4.

Abbreviations: L refers to Lyfas; PK denotes H10 HRV sensor plus Kubios software in Figure 4.

Inference: HR calculated by L can be determined by PK with the high coefficient of determinant values and very less RMSE for both the genders; while for the HRV correlates, the R2 values are quite less and RMSE values are high. It is therefore evident that HRV correlates vary significantly in L and PK, which is expected as these are two different instruments having two different modalities of working. A standard Polar H10 HR sensor uses ECG-based analysis while Lyfas uses photoplethysmography (PPG) and captures the Pulse Rate Variability (PRV) that is nothing but the reflection of the CvAM that surrogates the HRV and its correlates. It is worth noting that the PRV and HRV values may converge in the case of linearity of CAM, which is a physiologically impossible condition due to the vascular factor that plays a catalytic role in the measurement, such as the stiffness in the capillary arterioles. However, the encouraging finding is that L can be used for monitoring vitals, such as the HR efficiently as it is closely matched with the HR obtained by PK.

d) Two-sample t-test (t): To validate the findings of regressions, a Two-sample t-test has been conducted. Table 3 represents the results of the Two-sample t-tests as follows. Table 3a is in the case of males and Table 3b is in the case of females:

Inference: It is evident from the values that L and PK means are significantly different (positive t-statistic and very low p-values), corroborating the above-mentioned regression results.

e) Classification: In the final experiment, the classification efficacy of L has been tested on PK. Table 4 shows the results. In HR, ‘positive’ means tachycardia (HR > 100 bpm) or bradycardia (HR < 60 bpm), and ‘negative’ means within the normal limits. For the remaining parameters, cut-off values shown in Table 1 have been considered. Values beyond the range values are considered as ‘positive’ or abnormal, else ‘negative’ or normal.

From Table 4, it can be evident that in males HR, SDNN, and RMSSD can be well-explained by L when compared to PK (which has 33.33 times higher resolution than L, which has 30 Hz) with 97%, 93%, and 94% Precision (P), respectively with the average P of 87%; 90%, 79%, and 80% Accuracies (A), respectively with the average A of 82%; and a very high Youden’s indices (Y), which are 96%, 91%, and 90%, respectively, with the average Y of 79%; HR, SDNN, and RMSSD captured by L are found synchronized with that captured by PK, which validates the efficacy of L for the NN cycles similar to PK. Although compared to HR, SDNN, and RMSSD, the Precision, and Accuracy of pNN50 is a little lower (83% and 89%, respectively), the ‘Y’ value of 61% is still much encouraging for diagnostic purposes 41. LF/HF reflects the state of the body’s autonomic homeostasis, where the HF component refers to the parasympathetic drive and is the most important parameter of such homeostasis. The drawback of LF/HF is that the HF component is influenced by the types and rate of respiration, whereas RMSSD, which also is a measure of parasympathetic drive remains unaltered with respiration and therefore can be a better marker. In this study, RMSSD values obtained by L are synchronizing with PK already, therefore the lower P and A values of LF/HF although the Y value is 57%, which is over 50%, can be ignored. Female participants show a similar picture as seen in males with an average ‘P’ of 69%, ‘A’ of 79%, and the “Y’ of 81%, respectively. This result establishes the fact that ‘L’ is qualified to be a clinical monitoring instrument based on high ‘Y’ values in the participants. ‘Y’ values higher than 50% determine the industry standard of any clinical instrument that can be used for diagnostic purposes 41. The authors suggest that ‘L’ provides the psychophysiological snapshot and thus may assist the clinicians in understanding the detailed pathophysiology of any condition and arriving at a holistic differential diagnosis.

4. Conclusions

The paper compares the efficacies of one new medical instrument L (PRV-based optical biomarker tool) with a gold-standard instrument PK (HRV-based electrical biofeedback tool) in terms of capturing the HR and HRV-correlates. The study observes that L synchronizes with PK in almost all correlates although the resolutions and working principles vary. L has 30 Hz resolution and captures PRV that surrogates for the HRV and hence, it captures the CvAM, where vascular states (constriction vs. dilatation) play a catalytic role. PK on the other hand has 1000 Hz resolution and captures HRV and hence it is a measure of CAM, where the measures are vascular state independent. High ‘Y’ values found in this study validate that L can be used as a bedside diagnostic device as it also gives a snapshot of the inherent stress (marked by sympathetic overdrive) of the body at the time of test, whereas PK is a better instrument to assess the physiological response under exercise or other externally induced stress. Therefore, the paper concludes that Lyfas gives the ‘physiological biomarker’ snapshot that helps medical doctors in making the differential diagnosis of a given condition while PK gives a ‘biofeedback’ snapshot of the body. A few more advantages of L over PK according to the convenience factors are:

i) Fast: A complete test in L takes 2-3 minutes depending on the smartphone’s processor speed and the internet speed; while PK takes about 15 minutes to complete one test.

ii) Ubiquitous: L can be installed on the smartphone through the link to the application posted to the participant, while PK requires the supply of the physical device, i.e., the belt with sensor and the Kubios software loaded in a PC or a mobile device.

iii) User-friendly: L is just a gentle index finger touch technique on the main rear camera with automatic multilingual voice-assisted prompting to the user step-by-step, whereas PK requires wearing the sensor belt on the chest to capture the HR.

iv) Low-cost: L is currently economical to the users, while the Polar belt comes with INR 11,000/-, which is pretty costly for many, especially in the developing nations.

Hence, L can be used as a biofeedback instrument alongside its advantage of being a medical device that can provide a comprehensive snapshot of the mind-body homeostasis of the test-takers at that timestamp and thus adding more physiological insight to the condition ubiquitously 45. Therefore, the paper proposes that Lyfas can be used for screening, diagnosis of any ongoing pathology, and assessment of prognosis due to its’,

a) bouquet of proprietary holistic technical approaches in the application, i.e., (i) signal processing in capturing the optical biomarkers, (ii) analytics and visualization of these biomarkers, and (iii) medical heuristics in physiological decision-making on the organ-specific mind-body homeostasis. The authors are currently working on the ML-based approach in steering the decision into predictive analytics, and AI algorithms for prescriptive analytics. , and

b) the low-cost advantage for the developing nations.

Acknowledgments

TIDE 2.0 Program, DERBI Foundation, The Ministry of Electronics and Information Technologies, Govt. of Karnataka, India. Certificate number: IN-KA45186921153022T, Dated: 13th July 2021.

Shalini Gaur for literature review and Yogesh Kumar Mangal for data analytics and visualization.

Statement of Competing Interest

The authors state that there is no conflict of interest with anyone in person or with any organization regarding this study.

References

[1]  F. Shaffer, R. McCraty and C. L. Zerr, “A healthy heart is not a metronome: an integrative review of the heart's anatomy and heart rate variability,” Frontiers in Psychology, vol. 5, p. 1040, 2014.
In article      View Article  PubMed
 
[2]  “Pulse & Heart Rate,” [Online]. Available: https://my.clevelandclinic.org/health/diagnostics/17402-pulse--heart-rate.
In article      
 
[3]  R. McCraty and F. Shaffer, “Heart rate variability: new perspectives on physiological mechanisms, assessment of self-regulatory capacity, and health risk,” Glob Adv Health Med , vol. 4, no. 1, pp. 46-61, 2015.
In article      View Article  PubMed
 
[4]  E. Morgan, “All About HRV Part 2: Interbeat Intervals and Time Domain Stats,” 24 September 2017. [Online]. Available: https://support.mindwaretech.com/2017/09/all-about-hrv-part-2-interbeat-intervals-and-time-domain-stats/. [Accessed 17 October 2021].
In article      
 
[5]  J. A. Waxenbaum, V. Reddy and M. Varacallo, Anatomy, Autonomic Nervous System., vol. 2, StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; , 2021.
In article      
 
[6]  R. N. Gevirtz, P. M. Lehrer and M. S. Schwartz, Biofeedback: A Practitioner’s Guide, 4th ed ed., A. F. Schwartz MS, Ed., New York: The Guilford Press, 2016, p. 196-213.
In article      
 
[7]  R. W. Levenson, “The Autonomic Nervous System and Emotion,” Sage Journals, 2014.
In article      View Article
 
[8]  F. Beckers, B. Verheyden and A. E. Auburt, “Aging and nonlinear heart rate control in a healthy population,” Am J Physiol Heart Circ Physiol, vol. 290, no. 6, pp. H2560-H2570, 2006.
In article      View Article  PubMed
 
[9]  S. W. Weinschenk, R. D. Beise and J. Lorenz, “Heart rate variability (HRV) in deep breathing tests and 5-min short-term recordings: agreement of ear photoplethysmography with ECG measurements, in 343 subjects,” European Jpurnal of Applied Physiology, vol. 116, no. 8, p. 1527-35, 2016.
In article      View Article  PubMed
 
[10]  E. Yuda, M. Shibata, Y. Ogata, N. Ueda, T. Yambe, M. Yoshizawa and J. Hayano, “Pulse rate variability: a new biomarker, not a surrogate for heart rate variability,” Journal of Physiological Anthropology, vol. 39, no. 21, 2020.
In article      View Article  PubMed
 
[11]  M. Nardone, J. S. Floras and P. J. Millar, “Sympathetic neural modulation of arterial stiffness in humans,” American Journal of Physiology, 2020.
In article      View Article  PubMed
 
[12]  I. Antekmi, S. d. P. Rogerio, A. R. Shinzato, C. A. Peres, A. J. Mansur and e. al, “Influence of age, gender, body mass index, and functional capacity on heart rate variability in a cohort of subjects without heart disease,” The American Journal of Cardiology, vol. 93, no. 3, pp. 381-5, 2004.
In article      View Article  PubMed
 
[13]  N. K. Vaillancourt DE, “Changing complexity in human behavior and physiology through aging and disease.,” Neurobiology of Aging, vol. 23, no. 1, pp. 1-11, 2002.
In article      View Article
 
[14]  D. Lucini, G. Norbiato, M. Clerici and M. Pagani, “Hemodynamic and Autonomic Adjustments to Real Life Stress Conditions in Humans,” Journal of the American Heart Association, vol. 39, no. 1, pp. 184-188, 2002.
In article      View Article  PubMed
 
[15]  H. G. N. Assoumou, V. Pichot, V. Dauphinot, S. Celle, P. Gosse, M. Kossovsky and e. al, “Metabolic Syndrome and Short-Term and Long-Term Heart Rate Variability in Elderly Free of Clinical Cardiovascular Disease: The PROOF Study,” Rejuvenation research, vol. 13, no. 6, pp. 653-63, 2010.
In article      View Article  PubMed
 
[16]  A. Sajadieh, O. W. Nielsen, V. Rasmussen and e. al, “Increased heart rate and reduced heart-rate variability are associated with subclinical inflammation in middle-aged and elderly subjects with no apparent heart disease,” European Heart Journal, vol. 25, no. 5, pp. 363-370, 2004.
In article      View Article  PubMed
 
[17]  E. Gellhorn, Autonomic Imbalance and the Hypthalamus: Implications for Physiology, Medicine, Psychology, and Neuropsychiatry, NED ed., University of Minnesota Press, 1957.
In article      
 
[18]  T. Nada, M. Nomura, A. Iga, R. Kawaguchi, Y. Ochi and K. Saito, “Autonomic nervous function in patients with peptic ulcer studied by spectral analysis of heart rate variability,” J Med, vol. 32, pp. 333-47, 2001.
In article      
 
[19]  R. D. Ballard, “Sleep, Respiratory Physiology, and Nocturnal asthma,” Chronobiology International, vol. 16, no. 5, pp. 565-580, 2009.
In article      View Article  PubMed
 
[20]  A. H. Zohar, C. R. Cloninger and R. McCraty, “Personality and Heart Rate Variability: Exploring Pathways from Personality to Cardiac Coherence and Health,” Open Journal of Social Sciences, vol. 1, no. 6, pp. 32-39, 2013.
In article      View Article
 
[21]  C. L. Schaich, D. Malaver, H. Chen, H. A. Shaltout, A. Z. A. Hazzouri, D. M. Herrington and T. M. Hughes, “Association of Heart Rate Variability With Cognitive Performance: The Multi-Ethnic Study of Atherosclerosis,” Journal of the American Heart Association, vol. 9, no. 7, p. e013827, 2020.
In article      View Article  PubMed
 
[22]  C. M. Tyng, H. U. Amin, M. N. M. Saad and A. S. Malik, “The Influences of Emotion on Learning and Memory,” Front. Psychol., vol. 8, no. 1454, 2017.
In article      View Article  PubMed
 
[23]  E. Vanderveren, P. Bijttebier and D. Hermans, “The Importance of Memory Specificity and Memory Coherence for the Self: Linking Two Characteristics of Autobiographical Memory,” Front. Psychol., vol. 8, no. 2250, 2017.
In article      View Article  PubMed
 
[24]  H. S. Deepa and R. Das, “EVALUATION OF NON-INVASIVE SMARTPHONE BASED DIGITAL BIOMARKER TOOL LYFAS IN DETECTING SLEEP DEFICIENCY AND ITS EFFECTS: A RETROSPECTIVE OBSERVATIONAL STUDY,” Indian Journal of Applied Research, vol. 11, no. 1, pp. 46-47, 2021.
In article      View Article
 
[25]  [Online]. Available: https://www.polar.com/blog/heart-rate-variability-and-orthostatic-test-lets-talk-polar/. [Accessed 09 October 2021].
In article      
 
[26]  [Online]. Available: https://www.kubios.com/scientific-research/. [Accessed 09 October 2021].
In article      
 
[27]  [Online]. Available: https://in.linkedin.com/company/acculi-labs. [Accessed 09 October 2021].
In article      
 
[28]  S. Cheriyedath, “Photoplethysmography (PPG),” 27 Feb 2019. [Online]. Available: https://www.news-medical.net/health/Photoplethysmography-(PPG).aspx. [Accessed 29 Sept 2021].
In article      
 
[29]  [Online]. Available: https://www.polar.com/en/products/accessories/H10_heart_rate_sensor. [Accessed 9 October 2021].
In article      
 
[30]  J. A. Lipponen and M. P. Tarvainen, “A robust algorithm for HRV time series artefact correction using novel beat classification.,” Journal of Medical Engineering & Technology, vol. 43, no. 3, pp. 173-181, 2019.
In article      View Article  PubMed
 
[31]  [Online]. Available: https://www.kubios.com/scientific-research/. [Accessed 09 October 2021].
In article      
 
[32]  M. Altini, “https://medium.com/,” 7 February 2020. [Online]. Available: https://medium.com/@altini_marco/the-ultimate-guide-to-heart-rate-variability-hrv-part-1-70a0a392fff4. [Accessed 15 October 2021].
In article      
 
[33]  K. Umetani, D. H. Singer, R. McCraty and M. Atkinson, “Twenty-Four Hour Time Domain Heart Rate Variability and Heart Rate: Relations to Age and Gender Over Nine Decades,” Journal of the American College of Cardiology, vol. 31, no. 3, pp. 593-601, 1998.
In article      View Article
 
[34]  F. Shaffer and J. P. Ginsberg, “An Overview of Heart Rate Variability Metrics and Norms,” Frontiers in Public Health, vol. 28, no. 5, p. 258, 2017.
In article      View Article  PubMed
 
[35]  J. L. Francis, A. A. Weinstein, D. S. Krantz, M. C. Haigney, P. K. Stein, P. H. Stone, J. S. Gottdiener and W. J. Kop, “Association between Symptoms of Depression and Anxiety with Heart Rate Variability in Patients with Implantable Cardioverter Defibrillators,” Psychosomatic Medicine, vol. 71, no. 8, p. 821-827., 2009.
In article      View Article  PubMed
 
[36]  M. Yılmaz, H. Kayançiçek and Y. Çekici, “Heart rate variability: Highlights from hidden signals,” Journal of Integrative Cardiology, vol. 4, no. 5, pp. 1-8, 2018.
In article      View Article
 
[37]  A. Ritter and R. Muñoz-Carpena, “Performance evaluation of hydrological models: Statistical significance for reducing subjectivity in goodness-of-fit assessments,” Journal of Hydrology, vol. 480, p. 33-45, 2013.
In article      View Article
 
[38]  A. Ross and V. L. Willson, “Paired Samples T-Test.,” in Basic and Advanced Statistical Tests, Rotterdam, SensePublishers, 2017.
In article      View Article
 
[39]  B. Griffiths, 8 October 2020. [Online]. Available: https://www.polar.com/blog/heart-rate-101/#:~:text=The%20average%20healthy%20adult%20will,RHR%20of%20around%2040%20BPM.. [Accessed 15 October 2021].
In article      
 
[40]  M. M. Corrales, B. d. l. C. Torres, A. G. Esquivel, M. A. G. Salazar and J. N. Orellana, “Normal values of heart rate variability at rest in a young, healthy and active Mexican population,” Scientific Research, vol. 4, no. 7, pp. 1-9, 2012.
In article      
 
[41]  E. Heidel, “Diagnosis,” scalesstatistics.com, 2021. [Online]. Available: https://www.scalestatistics.com/youden-index.html. [Accessed 02 Oct 2021].
In article      
 
[42]  D. G. Altman and J. M. Bland, “Measurement in medicine: the analysis of method comparison studies,” Statistician, vol. 32, p. 307-17, 1983.
In article      View Article
 
[43]  D. Giavarina, “Understanding Bland Altman analysis,” Biochem Med (Zagreb), vol. 25, no. 2, p. 141-151., 2015.
In article      View Article  PubMed
 
[44]  G. J. Clement, “https://towardsdatascience.com/why-how-to-use-the-bland-altman-plot-for-a-b-testing-python-code-78712d28c362,” https://towardsdatascience.com, [Online]. Available: https://towardsdatascience.com/why-how-to-use-the-bland-altman-plot-for-a-b-testing-python-code-78712d28c362. [Accessed 14 October 2021].
In article      
 
[45]  Das, R.; Chatopadhyay S. “Towards Cardiac Risk Monitoring of Duchene Muscular Dystrophy using Lyfas”. Journal of Nanetechnology in Diagnosis and Treatment (2021), 7:25-32.
In article      
 

Published with license by Science and Education Publishing, Copyright © 2021 Subhagata Chattopadhyay and Rupam Das

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Normal Style
Subhagata Chattopadhyay, Rupam Das. Comparing Heart Rate Variability with Polar H10 Sensor and Pulse Rate Variability with LYFAS: A Novel Study. Journal of Biomedical Engineering and Technology. Vol. 9, No. 1, 2021, pp 1-9. http://pubs.sciepub.com/jbet/9/1/1
MLA Style
Chattopadhyay, Subhagata, and Rupam Das. "Comparing Heart Rate Variability with Polar H10 Sensor and Pulse Rate Variability with LYFAS: A Novel Study." Journal of Biomedical Engineering and Technology 9.1 (2021): 1-9.
APA Style
Chattopadhyay, S. , & Das, R. (2021). Comparing Heart Rate Variability with Polar H10 Sensor and Pulse Rate Variability with LYFAS: A Novel Study. Journal of Biomedical Engineering and Technology, 9(1), 1-9.
Chicago Style
Chattopadhyay, Subhagata, and Rupam Das. "Comparing Heart Rate Variability with Polar H10 Sensor and Pulse Rate Variability with LYFAS: A Novel Study." Journal of Biomedical Engineering and Technology 9, no. 1 (2021): 1-9.
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  • Table 2a. Descriptive statistics of HR and HRV-correlates calculated by the L and PK, respectively in males (N = 312)
  • Table 2b. Descriptive statistics of HR and HRV-correlates calculated by the L and PK, respectively in females (N = 255)
[1]  F. Shaffer, R. McCraty and C. L. Zerr, “A healthy heart is not a metronome: an integrative review of the heart's anatomy and heart rate variability,” Frontiers in Psychology, vol. 5, p. 1040, 2014.
In article      View Article  PubMed
 
[2]  “Pulse & Heart Rate,” [Online]. Available: https://my.clevelandclinic.org/health/diagnostics/17402-pulse--heart-rate.
In article      
 
[3]  R. McCraty and F. Shaffer, “Heart rate variability: new perspectives on physiological mechanisms, assessment of self-regulatory capacity, and health risk,” Glob Adv Health Med , vol. 4, no. 1, pp. 46-61, 2015.
In article      View Article  PubMed
 
[4]  E. Morgan, “All About HRV Part 2: Interbeat Intervals and Time Domain Stats,” 24 September 2017. [Online]. Available: https://support.mindwaretech.com/2017/09/all-about-hrv-part-2-interbeat-intervals-and-time-domain-stats/. [Accessed 17 October 2021].
In article      
 
[5]  J. A. Waxenbaum, V. Reddy and M. Varacallo, Anatomy, Autonomic Nervous System., vol. 2, StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; , 2021.
In article      
 
[6]  R. N. Gevirtz, P. M. Lehrer and M. S. Schwartz, Biofeedback: A Practitioner’s Guide, 4th ed ed., A. F. Schwartz MS, Ed., New York: The Guilford Press, 2016, p. 196-213.
In article      
 
[7]  R. W. Levenson, “The Autonomic Nervous System and Emotion,” Sage Journals, 2014.
In article      View Article
 
[8]  F. Beckers, B. Verheyden and A. E. Auburt, “Aging and nonlinear heart rate control in a healthy population,” Am J Physiol Heart Circ Physiol, vol. 290, no. 6, pp. H2560-H2570, 2006.
In article      View Article  PubMed
 
[9]  S. W. Weinschenk, R. D. Beise and J. Lorenz, “Heart rate variability (HRV) in deep breathing tests and 5-min short-term recordings: agreement of ear photoplethysmography with ECG measurements, in 343 subjects,” European Jpurnal of Applied Physiology, vol. 116, no. 8, p. 1527-35, 2016.
In article      View Article  PubMed
 
[10]  E. Yuda, M. Shibata, Y. Ogata, N. Ueda, T. Yambe, M. Yoshizawa and J. Hayano, “Pulse rate variability: a new biomarker, not a surrogate for heart rate variability,” Journal of Physiological Anthropology, vol. 39, no. 21, 2020.
In article      View Article  PubMed
 
[11]  M. Nardone, J. S. Floras and P. J. Millar, “Sympathetic neural modulation of arterial stiffness in humans,” American Journal of Physiology, 2020.
In article      View Article  PubMed
 
[12]  I. Antekmi, S. d. P. Rogerio, A. R. Shinzato, C. A. Peres, A. J. Mansur and e. al, “Influence of age, gender, body mass index, and functional capacity on heart rate variability in a cohort of subjects without heart disease,” The American Journal of Cardiology, vol. 93, no. 3, pp. 381-5, 2004.
In article      View Article  PubMed
 
[13]  N. K. Vaillancourt DE, “Changing complexity in human behavior and physiology through aging and disease.,” Neurobiology of Aging, vol. 23, no. 1, pp. 1-11, 2002.
In article      View Article
 
[14]  D. Lucini, G. Norbiato, M. Clerici and M. Pagani, “Hemodynamic and Autonomic Adjustments to Real Life Stress Conditions in Humans,” Journal of the American Heart Association, vol. 39, no. 1, pp. 184-188, 2002.
In article      View Article  PubMed
 
[15]  H. G. N. Assoumou, V. Pichot, V. Dauphinot, S. Celle, P. Gosse, M. Kossovsky and e. al, “Metabolic Syndrome and Short-Term and Long-Term Heart Rate Variability in Elderly Free of Clinical Cardiovascular Disease: The PROOF Study,” Rejuvenation research, vol. 13, no. 6, pp. 653-63, 2010.
In article      View Article  PubMed
 
[16]  A. Sajadieh, O. W. Nielsen, V. Rasmussen and e. al, “Increased heart rate and reduced heart-rate variability are associated with subclinical inflammation in middle-aged and elderly subjects with no apparent heart disease,” European Heart Journal, vol. 25, no. 5, pp. 363-370, 2004.
In article      View Article  PubMed
 
[17]  E. Gellhorn, Autonomic Imbalance and the Hypthalamus: Implications for Physiology, Medicine, Psychology, and Neuropsychiatry, NED ed., University of Minnesota Press, 1957.
In article      
 
[18]  T. Nada, M. Nomura, A. Iga, R. Kawaguchi, Y. Ochi and K. Saito, “Autonomic nervous function in patients with peptic ulcer studied by spectral analysis of heart rate variability,” J Med, vol. 32, pp. 333-47, 2001.
In article      
 
[19]  R. D. Ballard, “Sleep, Respiratory Physiology, and Nocturnal asthma,” Chronobiology International, vol. 16, no. 5, pp. 565-580, 2009.
In article      View Article  PubMed
 
[20]  A. H. Zohar, C. R. Cloninger and R. McCraty, “Personality and Heart Rate Variability: Exploring Pathways from Personality to Cardiac Coherence and Health,” Open Journal of Social Sciences, vol. 1, no. 6, pp. 32-39, 2013.
In article      View Article
 
[21]  C. L. Schaich, D. Malaver, H. Chen, H. A. Shaltout, A. Z. A. Hazzouri, D. M. Herrington and T. M. Hughes, “Association of Heart Rate Variability With Cognitive Performance: The Multi-Ethnic Study of Atherosclerosis,” Journal of the American Heart Association, vol. 9, no. 7, p. e013827, 2020.
In article      View Article  PubMed
 
[22]  C. M. Tyng, H. U. Amin, M. N. M. Saad and A. S. Malik, “The Influences of Emotion on Learning and Memory,” Front. Psychol., vol. 8, no. 1454, 2017.
In article      View Article  PubMed
 
[23]  E. Vanderveren, P. Bijttebier and D. Hermans, “The Importance of Memory Specificity and Memory Coherence for the Self: Linking Two Characteristics of Autobiographical Memory,” Front. Psychol., vol. 8, no. 2250, 2017.
In article      View Article  PubMed
 
[24]  H. S. Deepa and R. Das, “EVALUATION OF NON-INVASIVE SMARTPHONE BASED DIGITAL BIOMARKER TOOL LYFAS IN DETECTING SLEEP DEFICIENCY AND ITS EFFECTS: A RETROSPECTIVE OBSERVATIONAL STUDY,” Indian Journal of Applied Research, vol. 11, no. 1, pp. 46-47, 2021.
In article      View Article
 
[25]  [Online]. Available: https://www.polar.com/blog/heart-rate-variability-and-orthostatic-test-lets-talk-polar/. [Accessed 09 October 2021].
In article      
 
[26]  [Online]. Available: https://www.kubios.com/scientific-research/. [Accessed 09 October 2021].
In article      
 
[27]  [Online]. Available: https://in.linkedin.com/company/acculi-labs. [Accessed 09 October 2021].
In article      
 
[28]  S. Cheriyedath, “Photoplethysmography (PPG),” 27 Feb 2019. [Online]. Available: https://www.news-medical.net/health/Photoplethysmography-(PPG).aspx. [Accessed 29 Sept 2021].
In article      
 
[29]  [Online]. Available: https://www.polar.com/en/products/accessories/H10_heart_rate_sensor. [Accessed 9 October 2021].
In article      
 
[30]  J. A. Lipponen and M. P. Tarvainen, “A robust algorithm for HRV time series artefact correction using novel beat classification.,” Journal of Medical Engineering & Technology, vol. 43, no. 3, pp. 173-181, 2019.
In article      View Article  PubMed
 
[31]  [Online]. Available: https://www.kubios.com/scientific-research/. [Accessed 09 October 2021].
In article      
 
[32]  M. Altini, “https://medium.com/,” 7 February 2020. [Online]. Available: https://medium.com/@altini_marco/the-ultimate-guide-to-heart-rate-variability-hrv-part-1-70a0a392fff4. [Accessed 15 October 2021].
In article      
 
[33]  K. Umetani, D. H. Singer, R. McCraty and M. Atkinson, “Twenty-Four Hour Time Domain Heart Rate Variability and Heart Rate: Relations to Age and Gender Over Nine Decades,” Journal of the American College of Cardiology, vol. 31, no. 3, pp. 593-601, 1998.
In article      View Article
 
[34]  F. Shaffer and J. P. Ginsberg, “An Overview of Heart Rate Variability Metrics and Norms,” Frontiers in Public Health, vol. 28, no. 5, p. 258, 2017.
In article      View Article  PubMed
 
[35]  J. L. Francis, A. A. Weinstein, D. S. Krantz, M. C. Haigney, P. K. Stein, P. H. Stone, J. S. Gottdiener and W. J. Kop, “Association between Symptoms of Depression and Anxiety with Heart Rate Variability in Patients with Implantable Cardioverter Defibrillators,” Psychosomatic Medicine, vol. 71, no. 8, p. 821-827., 2009.
In article      View Article  PubMed
 
[36]  M. Yılmaz, H. Kayançiçek and Y. Çekici, “Heart rate variability: Highlights from hidden signals,” Journal of Integrative Cardiology, vol. 4, no. 5, pp. 1-8, 2018.
In article      View Article
 
[37]  A. Ritter and R. Muñoz-Carpena, “Performance evaluation of hydrological models: Statistical significance for reducing subjectivity in goodness-of-fit assessments,” Journal of Hydrology, vol. 480, p. 33-45, 2013.
In article      View Article
 
[38]  A. Ross and V. L. Willson, “Paired Samples T-Test.,” in Basic and Advanced Statistical Tests, Rotterdam, SensePublishers, 2017.
In article      View Article
 
[39]  B. Griffiths, 8 October 2020. [Online]. Available: https://www.polar.com/blog/heart-rate-101/#:~:text=The%20average%20healthy%20adult%20will,RHR%20of%20around%2040%20BPM.. [Accessed 15 October 2021].
In article      
 
[40]  M. M. Corrales, B. d. l. C. Torres, A. G. Esquivel, M. A. G. Salazar and J. N. Orellana, “Normal values of heart rate variability at rest in a young, healthy and active Mexican population,” Scientific Research, vol. 4, no. 7, pp. 1-9, 2012.
In article      
 
[41]  E. Heidel, “Diagnosis,” scalesstatistics.com, 2021. [Online]. Available: https://www.scalestatistics.com/youden-index.html. [Accessed 02 Oct 2021].
In article      
 
[42]  D. G. Altman and J. M. Bland, “Measurement in medicine: the analysis of method comparison studies,” Statistician, vol. 32, p. 307-17, 1983.
In article      View Article
 
[43]  D. Giavarina, “Understanding Bland Altman analysis,” Biochem Med (Zagreb), vol. 25, no. 2, p. 141-151., 2015.
In article      View Article  PubMed
 
[44]  G. J. Clement, “https://towardsdatascience.com/why-how-to-use-the-bland-altman-plot-for-a-b-testing-python-code-78712d28c362,” https://towardsdatascience.com, [Online]. Available: https://towardsdatascience.com/why-how-to-use-the-bland-altman-plot-for-a-b-testing-python-code-78712d28c362. [Accessed 14 October 2021].
In article      
 
[45]  Das, R.; Chatopadhyay S. “Towards Cardiac Risk Monitoring of Duchene Muscular Dystrophy using Lyfas”. Journal of Nanetechnology in Diagnosis and Treatment (2021), 7:25-32.
In article