scholarly journals Wearable Measurement of ECG Signals Based on Smart Clothing

2020 ◽  
Vol 2020 ◽  
pp. 1-9 ◽  
Author(s):  
Ming Li ◽  
Wei Xiong ◽  
Yongjian Li
Keyword(s):  
Human Body ◽  
The Body ◽  
Differential Signal ◽  
Ecg Signals ◽  
Smart Clothing ◽  
Textile Electrodes ◽  
Volume Conductor Model ◽  
Body Surface Potential ◽  
Potential Mapping ◽  
Electrocardiogram Ecg

Smart clothing that can measure electrocardiogram (ECG) signals and monitor the health status of people meets the needs of our increasingly aging society. However, the conventional measurement of ECG signals is complicated and its electrodes can cause irritation to the skin, which makes the conventional measurement method unsuitable for applications in smart clothing. In this paper, a novel wearable measurement of ECG signals is proposed. There are only three ECG textile electrodes knitted into the fabric of smart clothing. The acquired ECG signals can be transmitted to a smartphone via Bluetooth, and they can also be sent out to a PC terminal by a smartphone via WiFi or Internet. To get more significant ECG signals, the ECG differential signal between two electrodes is calculated based on a spherical volume conductor model, and the best positions on the surface of a human body for two textile electrodes to measure ECG signals are simulated by using the body-surface potential mapping (BSPM) data. The results show that position 12 in the lower right and position 11 in the upper left of the human body are the best for the two electrodes to measure ECG signals, and the presented wearable measurement can obtain good performance when one person is under the conditions of sleeping and jogging.

ECG Processing

2011 ◽  
pp. 137-160
Author(s):  
Lenka Lhotská ◽  
Václav Chudácek ◽  
Michal Huptych
Keyword(s):  
Feature Extraction ◽  
Fuzzy Rule ◽  
Future Research ◽  
Discrete Wavelet ◽  
Channel Measurements ◽  
Ecg Signals ◽  
Body Surface Potential ◽  
Potential Mapping ◽  
Electrocardiogram Ecg

This chapter describes methods for preprocessing, analysis, feature extraction, visualization, and classification of electrocardiogram (ECG) signals. First we introduce preprocessing methods, mainly based on the discrete wavelet transform. Then classification methods such as fuzzy rule based decision trees and neural networks are presented. Two examples - visualization and feature extraction from Body Surface Potential Mapping (BSPM) signals and classification of Holter ECGs – illustrate how these methods are used. Visualization is presented in the form of BSPM maps created from multi-channel measurements on the patient’s thorax. Classification involves distinguishing between Holter recordings from premature ventricular complexes and normal ECG beats. Classification results are discussed. Finally the future research opportunities are proposed.

The Role of Neural Networks in Computerized Classification of the Electrocardiogram

2011 ◽  
pp. 60-80
Author(s):  
Chris D. Nugent ◽  
Dewar D. Finlay ◽  
Mark P. Donnelly ◽  
Norman D. Black
Keyword(s):  
Neural Networks ◽  
Research Effort ◽  
Body Surface Potential ◽  
Potential Mapping ◽  
Rule Set ◽  
Chaotic Nature ◽  
Definition Of ◽  
Cardiac Diagnosis ◽  
Electrocardiogram Ecg

Electrical forces generated by the heart are transmitted to the skin through the body’s tissues. These forces can be recorded on the body’s surface and are represented as an electrocardiogram (ECG). The ECG can be used to detect many cardiac abnormalities. Traditionally, ECG classification algorithms have used rule based techniques in an effort to model the thought and reasoning process of the human expert. However, the definition of an ultimate rule set for cardiac diagnosis has remained somewhat elusive, and much research effort has been directed at data driven techniques. Neural networks have emerged as a strong contender as the highly non-linear and chaotic nature of the ECG represents a well-suited application for this technique. This study presents an overview of the application of neural networks in the field of ECG classification, and, in addition, some preliminary results of adaptations of conventional neural classifiers are presented. From this work, it is possible to highlight issues that will affect the acceptance of this technique and, in addition, identify challenges faced for the future. The challenges can be found in the intelligent processing of larger amounts of ECG information which may be generated from recording techniques such as body surface potential mapping.

scholarly journals Data Driven Computer Simulation to Analyse an ECG Limb Lead System Used in Connected Health Environments

2016 ◽  
Vol 55 (03) ◽  
pp. 258-265 ◽  
Author(s):  
Dewar Finlay ◽  
Daniel Guldenring ◽  
Cathal Breen ◽  
Raymond Bond
Keyword(s):  
Computer Simulation ◽  
Lead System ◽  
Ecg Signals ◽  
Connected Health ◽  
Body Surface Potential Maps ◽  
Body Surface Potential ◽  
The Difference ◽  
Electrocardiogram Ecg ◽  
Monitoring Devices ◽  
Computerised Analysis

SummaryBackground: Recently under the Connected Health initiative, researchers and small-medium engineering companies have developed Electrocardiogram (ECG) monitoring devices that incorporate non-standard limb electrode positions, which we have named the Central Einthoven (CE) configuration.Objectives: The main objective of this study is to compare ECG signals recorded from the CE configuration with those recorded from the recommended Mason-Likar (ML) configuration.Methods: This study involved extracting two different sets of ECG limb leads from each patient to compare the difference in the signals. This was done using computer simulation that is driven by body surface potential maps. This simulator was developed to facilitate this experiment but it can also be used to test similar hypotheses. This study included, (a) 176 ECGs derived using the ML electrode positions and (b) the 176 corresponding ECGs derived using the CE electrode positions. The signals from these ECGs were compared using root mean square error (RMSE), Pearson product-moment correlation coefficient (r) and similarity coefficient (SC). We also investigated whether the CE configuration influences the calculated mean cardiac axis. The top 10 cases where the ECGs were significantly different between the two configurations were visually compared by an ECG interpreter.Results: We found that the leads aVL, III and aVF are most affected when using the CE configuration. The absolute mean difference between the QRS axes from both configurations was 28° (SD = 37°). In addition, we found that in 82% of the QRS axes calculated from the CE configuration was more rightward in comparison to the QRS axes derived from the ML configuration. Also, we found that there is an 18% chance that a misleading axis will be located in the inferior right quadrant when using the CE approach. Thus, the CE configuration can emulate right axis deviation. The clinician visually identified 6 out of 10 cases where the CE based ECG yielded clinical differences that could result in false positives.Conclusions: The CE configuration will not yield the same diagnostic accuracy for diagnosing pathologies that rely on current amplitude criteria. Conversely, rhythm lead II was not significantly affected, which supports the use of the CE approach for assessing cardiac rhythm only. Any computerised analysis of the CE based ECG will need to take these findings into consideration.

scholarly journals A Software for Preprocessing Experimental BSPM Signals for a CRT Study

Proceedings ◽  
2019 ◽  
Vol 31 (1) ◽  
pp. 69
Author(s):  
Huptych ◽  
Hrachovina ◽  
Lhotská
Keyword(s):  
Body Surface ◽  
The Body ◽  
Cardiac Resynchronization ◽  
Heart Activity ◽  
Body Surface Potential Mapping ◽  
Body Surface Potential ◽  
Characteristic Points ◽  
Potential Mapping ◽  
The Difference ◽  
Surface Potential Mapping

In this text, we describe the developed system for Body Surface Potential Mapping (BSPM) signals preprocessing and basic processing. The BSPM is based on multichannel ECG measurement with up to hundreds of electrodes in a specific grid on the body surface. The project is focused on the signals of patients after cardiac resynchronization therapy (CRT). These patients are indicated for CRT because of heart failure, and it is necessary to realize the difference in electrical and mechanical heart activity of such patients. The presented software is designed according to the specific conditions of the issue, with respect to minimization of the morphology distortion during filtering and specificity during signal delineation (finding of ECG characteristic points).

scholarly journals Detection and Classification of ECG Signal through Machine learning

2019 ◽  
Vol 8 (10) ◽  
pp. 3221-3227
Keyword(s):  
The Body ◽  
Wave Form ◽  
Ecg Signal ◽  
Software Application ◽  
Ecg Signals ◽  
Temporal Features ◽  
Associate Anomaly ◽  
The Individual ◽  
Electrocardiogram Ecg

The electrical activity which might be acquired by inserting the probes on the body exterior that is originated within the individual muscle cells of the heart and is summed to indicate an indication wave form referred to as the EKG (ECG). Cardiac Arrhythmia is an associate anomaly within the heart which may be diagnosed with the usage of signals generated by Electrocardiogram (ECG). For the classification of ECG signals a software application model was developed and has been investigated with the usage of the MIT-BIH database. The version is based on some existing algorithms from literature, entails the extraction of a few temporal features of an ECG signal and simulating it with a trained FFNN. The software version may be employed for the detection of coronary heart illnesses in patients. The neural network’s structure and weights are optimized using Particle Swarm Optimization (PSO). The FFNN trained with set of rules by PSO increase its accuracy. The overall accuracy and sensitivity of the algorithm is about 93.687 % and 92%.

scholarly journals Sleep Apnea Identification using HRV Features of ECG Signals

2018 ◽  
Vol 8 (5) ◽  
pp. 3940 ◽  
Author(s):  
Billy Sulistyo ◽  
Nico Surantha ◽  
Sani M. Isa
Keyword(s):  
Sleep Apnea ◽  
The Body ◽  
Classification Model ◽  
Classification Methods ◽  
Early Prevention ◽  
Ecg Signals ◽  
Long Period ◽  
Subject Specific ◽  
Specific Scheme ◽  
Electrocardiogram Ecg

Sleep apnea is a common sleep disorder that interferes with the breathing of a person. During sleep, people can stop breathing for a moment that causes the body lack of oxygen that lasts for several seconds to minutes even until the range of hours. If it happens for a long period, it can result in more serious diseases, e.g. high blood pressure, heart failure, stroke, diabetes, etc. Sleep apnea can be prevented by identifying the indication of sleep apnea itself from ECG, EEG, or other signals to perform early prevention. The purpose of this study is to build a classification model to identify sleep disorders from the Heart Rate Variability (HRV) features that can be obtained with Electrocardiogram (ECG) signals. In this study, HRV features were processed using several classification methods, i.e. ANN, KNN, N-Bayes and SVM linear Methods. The classification is performed using subject-specific scheme and subject-independent scheme. The simulation results show that the SVM method achieves higher accuracy other than three other methods in identifying sleep apnea. While, time domain features shows the most dominant performance among the HRV features.

Correction of Posture Deviations in the Sagittal Plane Using the Pilates Method

2019 ◽  
pp. 3-13
Author(s):  
Alexandru Cîtea ◽  
George-Sebastian Iacob
Keyword(s):  
Low Back Pain ◽  
Back Pain ◽  
Postural Control ◽  
Human Body ◽  
Sagittal Plane ◽  
The Body ◽  
Lower Limbs ◽  
Low Back ◽  
Pilates Method ◽  
The Relationship

Posture is commonly perceived as the relationship between the segments of the human body upright. Certain parts of the body such as the cephalic extremity, neck, torso, upper and lower limbs are involved in the final posture of the body. Musculoskeletal instabilities and reduced postural control lead to the installation of nonstructural posture deviations in all 3 anatomical planes. When we talk about the sagittal plane, it was concluded that there are 4 main types of posture deviation: hyperlordotic posture, kyphotic posture, rectitude and "sway-back" posture.Pilates method has become in the last decade a much more popular formof exercise used in rehabilitation. The Pilates method is frequently prescribed to people with low back pain due to their orientation on the stabilizing muscles of the pelvis. Pilates exercise is thus theorized to help reactivate the muscles and, by doingso, increases lumbar support, reduces pain, and improves body alignment.

Representation of a Human Body: A Comparison Study between Balinese and Javanese Traditional House

Humaniora ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 83-90
Author(s):  
Anak Agung Ayu Wulandari ◽  
Ade Ariyani Sari Fajarwati
Keyword(s):  
Comparative Study ◽  
Human Body ◽  
Qualitative Method ◽  
Descriptive Analysis ◽  
The Body ◽  
Comparison Study ◽  
Architectural Drawing ◽  
Study Approach ◽  
The Comparative Study ◽  
Head Area

The research would look further at the representation of the human body in both Balinese and Javanese traditional houses and compared the function and meaning of each part. To achieve the research aim, which was to evaluate and compare the representation of the human body in Javanese and Balinese traditional houses, a qualitative method through literature and descriptive analysis study was conducted. A comparative study approach would be used with an in-depth comparative study. It would revealed not only the similarities but also the differences between both subjects. The research shows that both traditional houses represent the human body in their way. From the architectural drawing top to bottom, both houses show the same structure that is identical to the human body; head at the top, followed by the body, and feet at the bottom. However, the comparative study shows that each area represents a different meaning. The circulation of the house is also different, while the Balinese house is started with feet and continued to body and head area. Simultaneously, the Javanese house is started with the head, then continued to body, and feet area.

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