scholarly journals Fractal Based Analysis of the Influence of Odorants on Heart Activity

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Hamidreza Namazi ◽  
Vladimir V. Kulish
Keyword(s):  
Heart Rate ◽  
Heart Diseases ◽  
Approximate Entropy ◽  
Ecg Signal ◽  
Heart Activity ◽  
Heart Research ◽  
Olfactory Stimuli ◽  
Important Challenge ◽  
External Stimuli ◽  
Electrocardiogram Ecg

Abstract An important challenge in heart research is to make the relation between the features of external stimuli and heart activity. Olfactory stimulation is an important type of stimulation that affects the heart activity, which is mapped on Electrocardiogram (ECG) signal. Yet, no one has discovered any relation between the structures of olfactory stimuli and the ECG signal. This study investigates the relation between the structures of heart rate and the olfactory stimulus (odorant). We show that the complexity of the heart rate is coupled with the molecular complexity of the odorant, where more structurally complex odorant causes less fractal heart rate. Also, odorant having higher entropy causes the heart rate having lower approximate entropy. The method discussed here can be applied and investigated in case of patients with heart diseases as the rehabilitation purpose.

scholarly journals An approach of adaptive notch filtering design for electrocardiogram noise cancellation

2021 ◽  
Vol 22 (3) ◽  
pp. 1303
Author(s):  
Rahmad Hidayat ◽  
Ninik Sri Lestari ◽  
Herawati Herawati ◽  
Givy Devira Ramady ◽  
Sudarmanto Sudarmanto ◽  
...  
Keyword(s):  
Notch Filter ◽  
Ecg Signal ◽  
Least Mean Square ◽  
Heart Activity ◽  
Frequency Range ◽  
Biomedical Signal ◽  
Nlms Algorithm ◽  
Digital Notch Filter ◽  
Notch Filtering ◽  
Electrocardiogram Ecg

An electrocardiogram (ECG) is a means of measuring and monitoring important signals from heart activity. One of the major biomedical signal issues such as ECG is the issue of separating the desired signal from noise or interference. Different kinds of digital filters are used to distinguish the signal components from the unwanted frequency range to the ECG signal. To address the question of noise to the ECG signal, in this paper the digital notch filter IIR 47 Hz is designed and simulated to demonstrate the elimination of 47 Hz noise to obtain an accurate ECG signal. The full architecture of the structure and coefficient of the IIR notch filter was carried out using the FDA Tool. Then the model is finished with the help of Simulink and the MATLAB script was to filter out the 47 Hz noise from the signal of ECG. For this purpose, the normalized least mean square (NLMS) algorithm was used. The results indicate that before being filtered and after being filtered it clearly shows the elimination of 47 Hz noise in the signal of the ECG. These results also show the accuracy of the design technique and provide an easy model to filter out noise in the ECG signal.

scholarly journals Smartphone based ECG Acquisition and Analysis

2021 ◽  
Vol 9 (VI) ◽  
pp. 539-543
Author(s):  
Renuka Vijay Kapse
Keyword(s):  
Cardiovascular Diseases ◽  
Cause Of Death ◽  
Health Monitoring ◽  
Early Warning ◽  
Heart Diseases ◽  
Ecg Signal ◽  
Android Application ◽  
Ecg Signals ◽  
Heart Damage ◽  
Electrocardiogram Ecg

Health monitoring and technologies related to health monitoring is an appealing area of research. The electrocardiogram (ECG) has constantly being mainstream estimation plan to evaluate and analyse cardiovascular diseases. Heart health is important for everyone. Heart needs to be monitored regularly and early warning can prevent the permanent heart damage. Also heart diseases are the leading cause of death worldwide. Hence the work presents a design of a mini wearable ECG system and it’s interfacing with the Android application. This framework is created to show and analyze the ECG signal got from the ECG wearable system. The ECG signals will be shipped off an android application via Bluetooth device. This system will automatically alert the user through SMS.

scholarly journals A Novel Heart Rate Robust Method for Short-Term Electrocardiogram Biometric Identification

2019 ◽  
Vol 9 (1) ◽  
pp. 201 ◽  
Author(s):  
Di Wang ◽  
Yujuan Si ◽  
Weiyi Yang ◽  
Gong Zhang ◽  
Tong Liu
Keyword(s):  
Heart Rate ◽  
Principal Component ◽  
Identification Accuracy ◽  
Ecg Signal ◽  
Identification Task ◽  
Short Term ◽  
Ecg Signals ◽  
Subject Identification ◽  
Novel Method ◽  
Electrocardiogram Ecg

In the past decades, the electrocardiogram (ECG) has been investigated as a promising biometric by exploiting the subtle discrepancy of ECG signals between subjects. However, the heart rate (HR) for one subject may vary because of physical activities or strong emotions, leading to the problem of ECG signal variation. This variation will significantly decrease the performance of the identification task. Particularly for short-term ECG signal without many heartbeats, the hardly measured HR makes the identification task even more challenging. This study aims to propose a novel method suitable for short-term ECG signal identification. In particular, an improved HR-free resampling strategy is proposed to minimize the influence of HR variability during heartbeat processing. For feature extraction, the Principal Component Analysis Network (PCANet) is implemented to determine the potential difference between subjects. The proposed method is evaluated using a public ECG-ID database that contains various HR data for some subjects. Experimental results show that the proposed method is robust to HR change and can achieve high subject identification accuracy (94.4%) on ECG signals with only five heartbeats. Thus, the proposed method has the potential for application to systems that use short-term ECG signals for identification (e.g., wearable devices).

HRV

2020 ◽  
pp. 236-264
Author(s):  
Kirti Rawal ◽  
Gaurav Sethi ◽  
Barjinder Singh Saini ◽  
Indu Saini
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Input Signal ◽  
Medical Tourism ◽  
Ecg Signal ◽  
Rr Intervals ◽  
Experimental Protocols ◽  
Hrv Analysis ◽  
Electrocardiogram Ecg ◽  
Electrical Interference

The most important factor involved in heart rate variability (HRV) analysis is cardiac input signal, which is achieved in the form of electrocardiogram (ECG). The ECG signal is used for identifying many electrical defects associated with the heart. In this chapter, many issues involved while ECG recording such as type of the recording instrument, various sources of noise, artifacts, and electrical interference from surroundings is presented. Most importantly, this chapter comprises the details about the experimental protocols followed while ECG recording. Also, the brief overview of medical tourism as well as various interpolation methods used for pre-processing of RR intervals are presented in this chapter.

scholarly journals Classification of Multi Heart Diseases With Android Based Monitoring System

2020 ◽  
pp. 14-22
Keyword(s):  
Feature Extraction ◽  
Heart Diseases ◽  
Confusion Matrix ◽  
Predictive Coding ◽  
Classification Performance ◽  
Support Vector ◽  
Ecg Signal ◽  
Linear Predictive Coding ◽  
Electrocardiogram Ecg

Electrocardiogram (ECG) examination via computer techniques that involve feature extraction, pre-processing and post-processing was implemented due to its significant advantages. Extracting ECG signal standard features that requires high processing operation level was the main focusing point for many studies. In this paper, up to 6 different ECG signal classes are accurately predicted in the absence of ECG feature extraction. The corner stone of the proposed technique in this paper is the Linear predictive coding (LPC) technique that regress and normalize the signal during the pre-processing phase. Prior to the feature extraction using Wavelet energy (WE), a direct Wavelet transform (DWT) is implemented that converted ECG signal to frequency domain. In addition, the dataset was divided into two parts , one for training and the other for testing purposes Which have been classified in this proposed algorithm using support vector machine (SVM). Moreover, using MIT AI2 Companion was developed by MIT Center for Mobile Learning, the classification result was shared to the patient mobile phone that can call the ambulance and send the location in case of serious emergency. Finally, the confusion matrix values are used to measure the proposed classification performance. For 6 different ECG classes, an accuracy ration of about 98.15% was recorded. This ratio became 100% for 3 ECG signal classes and decreases to 97.95% by increasing ECG signal to 7 classes.

scholarly journals Design of a Wearable 12-Lead Noncontact Electrocardiogram Monitoring System

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1509 ◽  
Author(s):  
Chien-Chin Hsu ◽  
Bor-Shing Lin ◽  
Ke-Yi He ◽  
Bor-Shyh Lin
Keyword(s):  
Myocardial Ischemia ◽  
Monitoring System ◽  
Mechanical Design ◽  
Heart Diseases ◽  
Signal Quality ◽  
Ecg Signal ◽  
Ventricular Premature Contraction ◽  
Dry Electrodes ◽  
Electrocardiogram Monitoring ◽  
Electrocardiogram Ecg

A standard 12-lead electrocardiogram (ECG) is an important tool in the diagnosis of heart diseases. Here, Ag/AgCl electrodes with conductive gels are usually used in a 12-lead ECG system to access biopotentials. However, using Ag/AgCl electrodes with conductive gels might be inconvenient in a prehospital setting. In previous studies, several dry electrodes have been developed to improve this issue. However, these dry electrodes have contact with the skin directly, and they might be still unsuitable for patients with wounds. In this study, a wearable 12-lead electrocardiogram monitoring system was proposed to improve the above issue. Here, novel noncontact electrodes were also designed to access biopotentials without contact with the skin directly. Moreover, by using the mechanical design, this system allows the user to easily wear and take off the device and to adjust the locations of the noncontact electrodes. The experimental results showed that the proposed system could exactly provide a good ECG signal quality even while walking and could detect the ECG features of the patients with myocardial ischemia, installation pacemaker, and ventricular premature contraction.

scholarly journals Analysis of the Influence of Complexity and Entropy of Odorant on Fractal Dynamics and Entropy of EEG Signal

2016 ◽  
Vol 2016 ◽  
pp. 1-5 ◽  
Author(s):  
Hamidreza Namazi ◽  
Amin Akrami ◽  
Sina Nazeri ◽  
Vladimir V. Kulish
Keyword(s):  
Brain Research ◽  
Approximate Entropy ◽  
Olfactory Stimulus ◽  
Brain Diseases ◽  
Eeg Signal ◽  
Olfactory Stimuli ◽  
Important Challenge ◽  
Molecular Complexity ◽  
Electroencephalogram Eeg

An important challenge in brain research is to make out the relation between the features of olfactory stimuli and the electroencephalogram (EEG) signal. Yet, no one has discovered any relation between the structures of olfactory stimuli and the EEG signal. This study investigates the relation between the structures of EEG signal and the olfactory stimulus (odorant). We show that the complexity of the EEG signal is coupled with the molecular complexity of the odorant, where more structurally complex odorant causes less fractal EEG signal. Also, odorant having higher entropy causes the EEG signal to have lower approximate entropy. The method discussed here can be applied and investigated in case of patients with brain diseases as the rehabilitation purpose.

scholarly journals Noncontact ECG Monitoring by Capacitive Coupling of Textiles in a Chair

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Po-Cheng Su ◽  
Ya-Hsin Hsueh ◽  
Ming-Ta Ke ◽  
Jyun-Jhe Chen ◽  
Ping-Chen Lai
Keyword(s):  
Measurement System ◽  
Capacitive Coupling ◽  
Ecg Signal ◽  
Ecg Monitoring ◽  
Heart Activity ◽  
Textile Electrodes ◽  
Conductive Textile ◽  
Electrocardiogram Ecg ◽  
T Waves

Some patients are uncomfortable with being wired to a device to have their heart activity measured. Accordingly, this study adopts a noncontact electrocardiogram (ECG) measurement system using coupled capacitance in a conductive textile. The textiles can be placed on a chair and are able to record some of the patient’s heart data. Height and distance between the conductive textile electrodes were influential when trying to obtain an optimal ECG signal. A soft and highly conductive textile was used as the electrode, and clothing was regarded as capacitance insulation. The conductive textile and body were treated as the two electrode plates. This study found that placing the two conductive textiles at the same height provided better data than different heights. The system also enabled identifying the P, Q, R, S, and T waves of the ECG signal and eliminated unnecessary noise successfully.

scholarly journals Heart rate detection properties of dry-electrode ECG compared to conventional 3-lead gel-electrode ECG in newborns

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Hanne Pike ◽  
Joar Eilevstjønn ◽  
Peder Bjorland ◽  
Jørgen Linde ◽  
Hege Ersdal ◽  
...  
Keyword(s):  
Heart Rate ◽  
Birth Weight ◽  
Full Range ◽  
Mean Difference ◽  
Ecg Signal ◽  
Dry Electrode ◽  
Data Points ◽  
Study Population ◽  
The Mean ◽  
Electrocardiogram Ecg

Abstract Objective To compare the accuracy of heart rate detection properties of a novel, wireless, dry-electrode electrocardiogram (ECG) device, NeoBeat®, to that of a conventional 3-lead gel-electrode ECG monitor (PropaqM®) in newborns. Results The study population had a mean gestational age of 39 weeks and 2 days (1.5 weeks) and birth weight 3528 g (668 g). There were 950 heart rate notations from each device, but heart rate was absent from the reference monitor in 14 of these data points, leaving 936 data pairs to compare. The mean (SD) difference when comparing NeoBeat to the reference monitor was -0.25 (9.91) beats per minute (bpm) (p = 0.44). There was a deviation of more than 10 bpm in 7.4% of the data pairs, which primarily (78%) was attributed to ECG signal disturbance, and secondly (22%) due to algorithm differences between the devices. Excluding these outliers, the correlation was equally consistent (r2 = 0.96) in the full range of heart rate captured measurements with a mean difference of − 0.16 (3.09) bpm. The mean difference was less than 1 bpm regardless of whether outliers were included or not.

scholarly journals Obesity, Nutrition and Heart Rate Variability

2021 ◽  
Vol 22 (8) ◽  
pp. 4215
Author(s):  
Anna Strüven ◽  
Christina Holzapfel ◽  
Christopher Stremmel ◽  
Stefan Brunner
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Weight Reduction ◽  
Dietary Intervention ◽  
Special Focus ◽  
Heart Rate Monitoring ◽  
Self Monitoring ◽  
Additional Tool ◽  
External Stimuli ◽  
Electrocardiogram Ecg

Heart rate variability (HRV) represents the activity and balance of the autonomic nervous system and its capability to react to internal and external stimuli. As a measure of general body homeostasis, HRV is linked to lifestyle factors and it is associated with morbidity and mortality. It is easily accessible by heart rate monitoring and gains interest in the era of smart watches and self-monitoring. In this review, we summarize effects of weight loss, training, and nutrition on HRV with a special focus on obesity. Besides weight reduction, effects of physical activity and dietary intervention can be monitored by parameters of HRV, including its time and frequency domain components. In the future, monitoring of HRV should be included in any weight reduction program as it provides an additional tool to analyze the effect of body weight on general health and homeostasis. HRV parameters could, for example, be monitored easily by implementation of an electrocardiogram (ECG) every two to four weeks during weight reduction period. Indices presumibly showing beneficial changes could be a reduction in heart rate and the number of premature ventricular complexes as well as an increase in standard deviation of normal-to-normal beat intervals (SDNN), just to name some.

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