Influence of Type 1 Diabetes on the Symbolic Analysis and Complexity of Heart Rate Variability in Young Adults

BACKGROUND: The current method to evaluate major depressive disorder (MDD) relies on subjective clinical interviews and self-questionnaires. OBJECTIVE: Autonomic imbalance in MDD patients is characterized using entropy measures of heart rate variability (HRV). A machine learning approach for screening depression based on the entropy is demonstrated. METHODS: The participants experience five experimental phases: baseline (BASE), stress task (MAT), stress task recovery (REC1), relaxation task (RLX), and relaxation task recovery (REC2). The four entropy indices, approximate entropy, sample entropy, fuzzy entropy, and Shannon entropy, are extracted for each phase, and a total of 20 features are used. A support vector machine classifier and recursive feature elimination are employed for classification. RESULTS: The entropy features are lower in the MDD group; however, the disease does not have a significant effect. Experimental tasks significantly affect the features. The entropy did not recover during REC1. The differences in the entropy features between the two groups increased after MAT and showed the largest gap in REC2. We achieved 70% accuracy, 64% sensitivity, and 76% specificity with three optimal features during RLX and REC2. CONCLUSION: Monitoring of HRV complexity changes when a subject experiences autonomic arousal and recovery can potentially facilitate objective depression recognition.

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“…In the present study,

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were set to 3 and 6, respectively, as they are commonly used for clinical HRV data [37, 59]. …”

Section: Methodsmentioning

confidence: 99%

Entropy analysis of heart rate variability and its application to recognize major depressive disorder: A pilot study

Byun

Kim

Jang

et al. 2019

THC

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“…Nonlinear features related to the complexity, dynamics, and chaotic components of HRV have also shown to be altered in persons with T1D. These include HRV randomness [ 44 ], symbolic indices [ 45 ], Katz fractal dimension [ 46 ], and geometric indices, such as the Poincare plot [ 47 ]. The effect of T1D on HRV was also demonstrated during exercise [ 48 ].…”

Section: Resultsmentioning

confidence: 99%

The Potential of Current Noninvasive Wearable Technology for the Monitoring of Physiological Signals in the Management of Type 1 Diabetes: Literature Survey

Daskalaki¹,

Parkinson²,

Brew‐Sam³

et al. 2022

J Med Internet Res

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Background Monitoring glucose and other parameters in persons with type 1 diabetes (T1D) can enhance acute glycemic management and the diagnosis of long-term complications of the disease. For most persons living with T1D, the determination of insulin delivery is based on a single measured parameter—glucose. To date, wearable sensors exist that enable the seamless, noninvasive, and low-cost monitoring of multiple physiological parameters. Objective The objective of this literature survey is to explore whether some of the physiological parameters that can be monitored with noninvasive, wearable sensors may be used to enhance T1D management. Methods A list of physiological parameters, which can be monitored by using wearable sensors available in 2020, was compiled by a thorough review of the devices available in the market. A literature survey was performed using search terms related to T1D combined with the identified physiological parameters. The selected publications were restricted to human studies, which had at least their abstracts available. The PubMed and Scopus databases were interrogated. In total, 77 articles were retained and analyzed based on the following two axes: the reported relations between these parameters and T1D, which were found by comparing persons with T1D and healthy control participants, and the potential areas for T1D enhancement via the further analysis of the found relationships in studies working within T1D cohorts. Results On the basis of our search methodology, 626 articles were returned, and after applying our exclusion criteria, 77 (12.3%) articles were retained. Physiological parameters with potential for monitoring by using noninvasive wearable devices in persons with T1D included those related to cardiac autonomic function, cardiorespiratory control balance and fitness, sudomotor function, and skin temperature. Cardiac autonomic function measures, particularly the indices of heart rate and heart rate variability, have been shown to be valuable in diagnosing and monitoring cardiac autonomic neuropathy and, potentially, predicting and detecting hypoglycemia. All identified physiological parameters were shown to be associated with some aspects of diabetes complications, such as retinopathy, neuropathy, and nephropathy, as well as macrovascular disease, with capacity for early risk prediction. However, although they can be monitored by available wearable sensors, most studies have yet to adopt them, as opposed to using more conventional devices. Conclusions Wearable sensors have the potential to augment T1D sensing with additional, informative biomarkers, which can be monitored noninvasively, seamlessly, and continuously. However, significant challenges associated with measurement accuracy, removal of noise and motion artifacts, and smart decision-making exist. Consequently, research should focus on harvesting the information hidden in the complex data generated by wearable sensors and on developing models and smart decision strategies to optimize the incorporation of these novel inputs into T1D interventions.

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Advanced cardiovascular physiology in an individual with type 1 diabetes after 10-year ketogenic diet

Watso,

Robinson,

Singar

et al. 2024

American Journal of Physiology-Cell Physiology

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Adults with type 1 diabetes (T1D) have an elevated risk for cardiovascular disease (CVD) compared with the general population. HbA1c is the primary modifiable risk factor for CVD in T1D. Fewer than 1% of patients achieve euglycemia (<5.7%HbA1c). Ketogenic diets (KD; ≤50g carbohydrate/day) may improve glycemia and downstream vascular dysfunction in T1D by reducing HbA1c and insulin load. However, there are concerns regarding the long-term CVD risk from a KD. Therefore, we compared data collected in a 60-day window in an adult with T1D on exogenous insulin who consumed a KD for 10 years versus normative values in those with T1D (T1D norms). The participant achieved euglycemia with an HbA1c of 5.5%, mean glucose of 98[5]mg/dL(median[IQR]), and 90[11]%time-in-range 70-180mg/dL (T1D norms: 1st percentile for all); and low insulin requirements of 0.38±0.03IU/kg/day (T1D norms: 8th percentile). Seated systolic blood pressure (SBP) was 113mmHg (T1D norms: 18th percentile) while ambulatory awake SBP was 132±15mmHg (T1D target: <130mmHg), blood triglycerides were 69mg/dL (T1D norms: 34th percentile), low-density lipoprotein was 129mg/dL (T1D norms: 60th percentile), heart rate was 56bpm (T1D norms: >1SD below the mean), carotid-femoral pulse wave velocity was 7.17m/s (T1D norms: lowest quartile of risk), flow-mediated dilation was 12.8% (T1D norms: >1SD above mean), and cardiac vagal baroreflex gain was 23.5ms/mmHg (T1D norms: >1SD above mean). Finally, there was no indication of left ventricular diastolic dysfunction from echocardiography. Overall, these data demonstrate below-average CVD risk relative to T1D norms despite concerns regarding the long-term impact of a KD on CVD risk.

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Influence of Type 1 Diabetes on the Symbolic Analysis and Complexity of Heart Rate Variability in Young Adults

Cited by 3 publications

References 22 publications

Entropy analysis of heart rate variability and its application to recognize major depressive disorder: A pilot study

Entropy analysis of heart rate variability and its application to recognize major depressive disorder: A pilot study

The Potential of Current Noninvasive Wearable Technology for the Monitoring of Physiological Signals in the Management of Type 1 Diabetes: Literature Survey

Advanced cardiovascular physiology in an individual with type 1 diabetes after 10-year ketogenic diet

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