A Study of Significant data Classification between EDR extracted and frequency analysis of Heart Rate Variability from ECG using Conductive textile

Noh, Yeon Sik; Park, Sung Bin; Hong, Kyu Seok; Yoon, Young Joon; Yoon, Hyoung Ro

doi:10.1007/978-3-540-36841-0_1039

Cited by 4 publications

(4 citation statements)

References 4 publications

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“…In this study shows the classification using frequency analysis between Heart Instantaneous Frequency (HIF) and EDR signal from ECG, whereas that using frequency analysis between HRV and EDR signal from ECG in [3]. It is the reason that HIF signal is simpler than HRV signal.…”

Section: Introductionmentioning

confidence: 87%

A Novel Approach to Classify Significant ECG Data Based on Heart Instantaneous Frequency and ECG-derived Respiration using Conductive Textiles

Noh

Park

et al. 2007

2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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Our study focuses on classifying as significant Electrocardiogram (ECG) data from home healthcare system. Generally, spectral analysis of RR Interval (RRI) time series is used to determine periodic component of Heart Rate Variability (HRV). It is well known, moreover, that Low Frequency (LF) component is associated with blood pressure regulation, and High Frequency (HF) component is referred to respiration as Respiration Sinus Arrhythmia (RSA) in the HRV power spectra. In many cases, however, LF and HF components may be entirely superimposed on each other and, therefore, the method by division of power spectra range can not be evaluated diagnostically. We propose another approach to interpret well better than before. The method which we suggest is that it finds high correlative data using frequency analysis comparison Heart Instantaneous Frequency (HIF) based on extracting the instantaneous fundamental frequency with EDR. The reason which we use HIF is that it is simpler and more powerful against noise than HRV. First of all, we show the EDR extraction process, and then prove that HIF signal is useful or not through comparison with HRV. Finally, we classify significant signal data through comparison High Frequency (HF) component obtained frequency analysis of HIF with that of EDR.

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Section: Introductionmentioning

confidence: 87%

A Novel Approach to Classify Significant ECG Data Based on Heart Instantaneous Frequency and ECG-derived Respiration using Conductive Textiles

Noh

Park

et al. 2007

2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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“…There has been research which studied EDR using frequency analysis [16]. The most distinct peak reflects changes in beat to beat interval that oscillates at the same frequency as respiration.…”

Section: ′′mentioning

confidence: 99%

Utilising Machine Learning to Predict Myocardial Infarction by Electrocardiogram Derived Respiration

Fung,

Ghiasi

2023

J Stud Res

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Myocardial Infarction (MI) is one of the leading causes of death. Electrocardiogram (ECG) is a non-invasive tool that is commonly used as a diagnostic tool to assess cardiac conditions. A dataset consisting ECG signals of healthy individuals and MI patients was subjected to pre-processing techniques like normalization and application of a bandpass filter. R-R peak intervals from the pre-processed ECG signals are extracted to generate the respiratory signal. The features extracted from the respiratory signal are used to predict MI. The objective of this study is to evaluate the potential of ECG derived respiratory signal (EDR) in predicting MI by utilizing machine learning techniques like random forest, linear regression, Convolutional Neural Network(CNN), Multilayer perceptron(MLP). The results of the study will examine the feasibility of using EDR in predicting MI and provide insight into the most effective machine learning technique for this application. This study will contribute to the development of new and efficient prediction methods for MI patients.

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“…The research work (Noh, 2007) tries to find out significant Heart Rate Variability (HRV) signal through comparison between power spectrums of ECG-Derived Respiration (EDR) and R-R interval variability ratio. The result shows that by considering cross-correlation, which is the measure of similarity of both HRV and EDR signals as a function of a time-lag applied to one of them, significant data acquisition gain can be achieved, while disregarding the low frequency component representing the Respiration Sinus Arrhythmia to enhance the feature extraction quality before classification.…”

Section: Data Analysis and Feedbackmentioning

confidence: 99%

Wireless Body Area Sensor Networks Signal Processing and Communication Framework: Survey on Sensing, Communication Technologies, Delivery and Feedback

Al-Saud¹

2012

Journal of Computer Science

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Problem statement:The Wireless Body Area Sensor Networks (WBASNs) is a wireless network used for communication among sensor nodes operating on or inside the human body in order to monitor vital body parameters and movements. This study surveys the state-of-the-art on Wireless Body Area Networks, discussing the major components of research in this area including physiological sensing and preprocessing, WBASNs communication techniques and data fusion for gathering data from sensors. In addition, data analysis and feedback will be presented including feature extraction, detection and classification of human related phenomena. Approach: Comparative studies of the technologies and techniques used in such systems will be provided in this study, using qualitative comparisons and use case analysis to give insight on potential uses for different techniques. Results and Conclusion: Wireless Sensor Networks (WSNs) technologies are considered as one of the key of the research areas in computer science and healthcare application industries. Sensor supply chain and communication technologies used within the system and power consumption therein, depend largely on the use case and the characteristics of the application. Authors conclude that Life-saving applications and thorough studies and tests should be conducted before WBANs can be widely applied to humans, particularly to address the challenges related to robust techniques for detection and classification to increase the accuracy and hence the confidence of applying such techniques without physician intervention.

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A Study of Significant data Classification between EDR extracted and frequency analysis of Heart Rate Variability from ECG using Conductive textile

Cited by 4 publications

References 4 publications

A Novel Approach to Classify Significant ECG Data Based on Heart Instantaneous Frequency and ECG-derived Respiration using Conductive Textiles

A Novel Approach to Classify Significant ECG Data Based on Heart Instantaneous Frequency and ECG-derived Respiration using Conductive Textiles

Utilising Machine Learning to Predict Myocardial Infarction by Electrocardiogram Derived Respiration

Wireless Body Area Sensor Networks Signal Processing and Communication Framework: Survey on Sensing, Communication Technologies, Delivery and Feedback

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