The main aim of this study is to evaluate the cardiac function of healthy young adults using ED to analyze different cardiac signals. This ECG model is therefore effective in evaluating cardiac function with the consumption of ED.
Fp(i) = Gaussian function for P wave FQ(t) = Gaussian function for Q wave FR(t) = Gaussian function for R wave F5(t) = Gaussian function for S wave F(t) = Gaussian function for T wave . FECGBD(t) = Function to generate ECG signal before ED is FECG, AD(t) = Function to generate ECG signal after ED is.
State of Art
Energy drinks (EDs) are a group of beverages that consumers use to further increase energy, promote alertness, maintain alertness, and improve cognitive abilities and mood [7]. Determining changes in cardiac activity due to energy drink (ED) consumption.
Contributions
To assess the impact of energy drink consumption on electrocardiography (ECG) and photoplethysmography (PPG). To propose a mathematical model for the generation of ECG taking into account the effects of energy drink consumption.
Potential Applications of this Research
Organization of the Thesis
Energy Beverages
- Energy Drink Brands
- Ingredients
A list of the best-selling energy drink brands over the past three years is given in Table 2.1. A common ingredient in most energy drinks is caffeine (often in the form of guarana or yerba mate).
Effects of Caffeine
- Physiological and Psychological Effects
Other stimulants such as ginseng are often added to energy drinks and can enhance the effects of caffeine [43] and ingredients such as guarana themselves contain caffeine. In the United States, energy drinks have been associated with reports of nausea, abnormal heart rhythms, and emergency room visits [45].
Health Effects of Sugar
- Health Effects
- Side Effects
The consumer basically takes energy drinks to produce a short-term boost of energy or to improve physical and mental performance. The number of people receiving emergency treatment for consuming energy drinks has increased in the US.
What are Cardiac Signals?
- Electrocardiogram
- Photo Plethysmogram
- Heart Rate or Pulse Rate
- What are Cardiac Function Parameters?
The baseline of the electrocardiogram (flat horizontal segments) is measured as the part of the tracing that follows the T wave and precedes the next P wave and the segment between the P wave and the following QRS complex (PR segment). The PR interval is measured from the beginning of the P wave to the beginning of the QRS complex.
Spectrum Analysis of Cardiac Signals - Fourier Transform
- Frequency Spectrum
- Spectral Density
- Energy Spectral Density
- Power Spectral Density
The Fourier transform of a function produces a frequency spectrum which contains all the information of the original signal, but in a different form. Because of reversibility, the Fourier transform is called a representation of the function in terms of frequency rather than time; thus it is a frequency domain representation. The frequency spectrum can be generated via a Fourier transform of the signal, and the resulting values are usually presented as amplitude and phase, both plotted against frequency [75].
Since the integral on the right-hand side is the signal energy, the integral x(a)I2 can be interpreted as a density function describing the energy per unit frequency contained in the signal at frequency w. The above definition of spectral energy density is most appropriate for transients, i.e., pulse-like signals, for which Fourier transforms of signals exist. In analyzing the frequency content of the signal x(t), one may wish to calculate the ordinary Fourier transform x(w); however, for many signals of interest this Fourier transform does not exist.
Mathematical Modeling of Cardiac Signals
- Proposed Approach
- Error Evaluation Parameters
- Modified Proposed Model
- Modeling ECG having Energy Drinks
The authors in [91] proposed a model using the Gaussian function, but they did not represent the QRS wave individually, and it is not able to fit the real ECG to a significant level. They used double differentiation of the Gaussian function, which is time-consuming and requires a complex mathematical operation. Another mathematical model was proposed using the single differentiation of the Gaussian function [92] but matching the maximum amplitude of the Q and S waves was not sufficient.
In this study, a peak amplitude-based Gaussian function (Gaussian peak function) is used to model complete ECG as well as P. In the case of P, R and I waves, the peak lies approximately in the middle of the wave which can be easily modeled using Gaussian function. But for Q and S wave, the peak point does not lie in the middle of the corresponding wave.
Subjects Specifications
Necessary Tools Specifications
Mean ± standard deviation (SD) values for age, weight, height and Body Mass Index (BMI) of the subjects are given in Table 4.2. Conditioning of acquisition amplifiers (Biopac, USA). from the skin 8 Amplifier LDF LDF IOOC Recording of blood perfusion and. Biopac, USA) amplifiers that condition LDF signals from the skin 9 Biopac Interface, USA Reliable Interface MP36 Link.
Experimental Setup
- Hardware Setup
- Database - Subject Preparation
- End-Connection Setup
- Software and Calibration Setup
For ECG recordings, three electrodes (EL503) were placed on the subject's body as shown in Figure 4.2. That is, an electrode on the medial surface of the right leg, just above the ankle bone; other electrode on the medial surface of the left leg, just above the ankle bone;. The skin surface is abraded at the electrode placement points to a diameter of about 2".
For optimal electrode adhesion, the electrodes were placed on the subject's skin at least 5 minutes before starting the calibration procedure. Each of the fixation connectors at the end of the electrode cable was attached to a specific electrode. The window of the pulse sensor (SS2LA) was cleaned and the transducer wrapped tightly around the tip of the subject's index finger on the left hand.
Cardiac Signals Recording
- Cardiac Signals at Normal Condition
- Cardiac Signals at Energized Condition
A minimum of 5 minutes was allowed for acclimatization before measurements were taken on the subject's body. Before consuming energy drinks (normal condition), ECG and pulse measurements were taken over a time period of approximately 10 minutes. ECG and pulse recordings for a subject in a normal state with a short duration are shown in Figure 4.6 and Figure 4.7, respectively.
After taking energy drinks, ECG and pulse recordings were performed with a period of approximately 95 minutes. Continuous ECG and pulse recordings were not performed due to the time limitation of recording using Biopac Student Lab (BSL) software. The recordings of ECG and pulse for a subject in activated state are shown in Figure 4.8 and Figure 4.9 respectively.
Influence of ED on Cardiac Signals
- Influence on PPG
- Influence on ECC & PPG Parameters
The average changes in the peak amplitude of different waves of the ECG signal over time due to the consumption of ED are shown in Table 4.4. In addition, a significant decrease in the average peak amplitude of the T wave is also noted as a result of excitation. The percentage changes in the average peak amplitude of different waves of the ECG signal over time are shown in Table 4.5.
An effective decrease in maximum amplitude to peak pulse and heart rate due to ED is also observed. The changes in percentage of the mean R-peak amplitude of the ECG over time are shown in Figure 4.26. Percentage changes in peak pulse amplitude (PPG) and heart rate (HR) over time are shown in Figure 4.27 and Figure 4.28, respectively.
63 Figure 4.34: Percentage changes in amplitude of FFT with time for ECG
Evaluation of Cardiac Signals Modeling
- Evaluation of Proposed Model
- EvaLuation of Modified Proposed Model
- Evaluation with Consuming ED
Peak amplitude-based model parameters have been noted after fitting the model ECG with real time-varying ECG. To fit our modified model ECG with real ECG, we have varied the constants or variable parameters of our modified proposed model. For modified proposed model, error analysis for a typical subject at a particular time is noted in Table 4.13.
Peak amplitude-based model parameters were noted after fitting the modified model ECG with real time-varying ECG. By changing the value of M1 (i E Q S) within the predetermined range, we evaluated the percentage error at both before and after ED as shown in Table 4.16 and Table 4.17. The mean percentage error (PNRMSE) at both before and after ED with varying M (i E Q, S) is shown in Table 4.18 and Table 4.19.
What is LDF?
Previous Works on LDF
Working Principle of LDF Module
LDF Recording
LDF recordings before and after ED for a typical subject are shown in Figure 5.2 and Figure 5.3, respectively. Before having ED, the maximum, minimum and average flow are 1315 BPU, 585 BPU and 972 BPU respectively. After ED, the maximum, minimum and average flow are 1487 BPU, 888 BPU and 1210 BPU respectively.
We can see that, due to the ED, the maximum, minimum and average flows are increasing, but the decrease in peak peak flow is more significant.
Spectrum Analysis of LDF
FFT analysis of the LDF signal before and after ED for a typical subject is shown in Figure 5.4 and Figure 5.5, respectively. Before ED occurs, the peak magnitude of FFT within cardiac activity is 17.37 BPU, which occurs at 1.04 Hz. We can see that, due to ED, the peak value of FFT within the cardiac activity decreases.
The PSD analysis of the LDF signal before and after ED for a typical subject is shown in Figure 5.6 and Figure 5.7, respectively. We can see that, due to ED, the maximum PSD power within the cardiac activity is decreasing. Changes in the FFT and PSD parameters of the LDF signal due to ED for a typical subject are listed in Table 5.2.
Validation of Previous Analysis
Conclusions
In this study, ECG signal modeling was performed using the proposed model, and the peak amplitude-based model parameters were recorded after fitting the model ECG with the real ECG. This model is better for modeling P, R and T waves, but it is not suitable for modeling Q and S waves due to the larger error. The performance of the modified proposed model for generating the ECG signal is better than the proposed model due to the small error in P .
The complete ECG signal modeling using modified proposed model in both before and after consumption of ED results in good fit as well as low error with real ECG signal. This model is essential to evaluate cardiac functions by comparing model parameters with real parameters with intake of ED.
Future Works
Ahmad, "Determining the effect of energy drink consumption using laser Doppler flowmetry," in Proc. Tetteh, “Acute effects of energy drink consumption on intraocular pressure and blood pressure,” Clinical Optometry , vol. Marczinski, “Acute effects of a glucose energy drink on behavioral control,” Experimental and Clinical Psychopharmacology , vol.
Wescott, “The Effects of Red Bull Energy Drink on Human Performance and Mood,” Amino Acids, vol. Audenaert, "The effects of energy drinks on cognitive performance", Tj/ds'chrj/i for psychiatric, vol. Rogers: “The effects of caffeine on performance and mood depend on the degree of caffeine abstinence.”
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