scholarly journals Concept Design and Analysis of Self Sustainable Triboelectric Pacemaker

2021 ◽  
Vol 2115 (1) ◽  
pp. 012050
Author(s):  
K S Ackshaya Varshini ◽  
K.S. Maanav Charan ◽  
M B Shyam Kumar

Abstract The heart is one of the most crucial organs for the functioning of the human body. Due to aging and various other ailments like cardiomyopathy and congestive heart failure, the functioning of the heart tends to drop or stop in serious conditions. In such conditions, a bio-medical device called the cardiac pacemaker is used. The pacemaker is a small device that will be placed in the dysfunctional heart that sends electrical impulses to the heart muscles whenever the functioning decreases or ceases. But the pacemaker existing in the market has a low battery life and has to be replaced every few years which is a painful process for the people using it. Therefore, to overcome this predicament in this study we have designed and developed a self-sustaining pacemaker that can generate its electricity from the pacing of the heart itself thereby increasing its battery life generously. This pacemaker works on the principle of tribe-electricity. The model of this pacemaker is designed using SolidWorks and the electrical circuit for the same is simulated using Simulink.

Author(s):  
Sourabh Aggarwal ◽  
Vishal Gupta

Introduction: Hospital readmission after pacemaker or cardioverter/defibrillator procedure (insertion, revision, replacement or removal) has a major impact on quality of patient’s life and cost-effectiveness of care. However, the data for 30-day readmission for patients undergoing these procedures is very limited. Methods: We queried Agency of Healthcare Research and Quality sponsored Healthcare Cost and Utilization Project’s Nationwide Inpatient Sample (NIS) data using Clinical Classification Software procedure Code 48 for insertion, revision, replacement and removal of cardiac pacemaker or cardioverter/defibrillator, which included ICD 9 codes of 00.50, 00.51, 00.52, 00.53, 00.54, 00.56, 00.57, 17.51, 17.52, 37.70, 37.71, 37.72, 37.73, 37.74, 37.75, 37.76, 37.77, 37.78, 37.79, 37.80, 37.81, 37.82, 37.83, 37.85, 37.86, 37.87, 37.89, 37.94, 37.95, 37.96, 37.97 and 37.98 to extract data for admissions secondary to pacemaker or cardioverter/defibrillator procedure. NIS represents 20% of all hospital data in US. Data was extracted for years 2009-2011 and 30-day readmissions secondary to these procedures identified. Statistical analysis was done using chi-square to determine parameters associated with increased 30-day readmissions. Results: We identified 443,719 admission for pacemaker or cardioverter/defibrillator procedure during the study period with total 30-day readmission rate of 15.38%. Females (15.91%), patients aged more than 65 years (15.78%), patients under Medicaid (17.65%), having low median income for zip code (16.44%) and staying in metropolitan areas (15.71%) were more likely to have total 30-day readmissions than other groups (P<0.01). Most common cause for readmission was congestive heart failure in 14,042 patients (20.57%), followed by complication of device in 8,244 patients (12.08%), arrhythmia in 5,750 patients (8.43%). Septicemia was responsible for readmission in 2,346 patients (3.44%) Conclusions: Strategies to reduce 30-day readmissions secondary to pacemaker or cardioverter/defibrillator procedure should be focused on more susceptible population including females, aged more than 65 years, covered under Medicaid, having low median income for zip code and staying in metropolitan areas and stringent control of risk factors for congestive heart failure and arrhythmia.


2011 ◽  
Vol 23 (04) ◽  
pp. 253-260 ◽  
Author(s):  
Ren-Guey Lee ◽  
Chun-Chieh Hsiao ◽  
Chieh-Yi Kao

The purpose of this paper is to show the influence of congestive heart failure (CHF) on heart by using different entropies to apply on the group of patients with CHF and normal group. Three different entropies are used: approximate entropy (ApEn), multiscale entropy (MSE), and base-scale entropy (BsEn). We use these three entropies to measure the complexity of the heart rate variability (HRV) and also use analysis of variance (ANOVA) to analyze the result of entropies to discuss the feasibility of recognizing CHF patients by utilizing entropies. With the analysis results of different entropies, the influence of CHF on heart has also been clearly demonstrated. The results on the approximate entropy show that the normal young group has a higher approximate entropy value while the CHF group has a lower value. This can be explained as a healthy, strong heart that can change its heart rate freely to adapt the change of the environment or the needs of the human body, therefore the HRV will be more complex. From the ANOVA results of approximate entropy, it can be observed that the F value is larger than 1, but is still small. In other words, the approximate entropy can be used to distinguish the three groups, the effect is, however, not good. It is hard to recognize a CHF patient by using approximate entropy.


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