ECG feature detection using randomly compressed samples for stable HRV analysis over low rate links

Gao, Ju; Teng, Diyan; Ertin, Emre

doi:10.1109/bsn.2016.7516253

Cited by 3 publications

(1 citation statement)

References 9 publications

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“…For older people, who are suffered from cardio disease , real time monitoring can provide the doctor with very important information about heart activity, the most significant means of heart activity status is electrocardiogram (ECG)signal, The typical ECG signal is shown in figure (1), it is constructed from P-wave, which represents the upper champers activity of the heart, QRS represents the most important champers of the signal and it is used for analysis the status of the heart, also it is used to determine the other characteristics of the signal, the other part is T wave , which is show the activity of lower champers, So any problem in the heart can be reflected on the waveform of these part especially QRS complex, which is detected by shorten, widening or lengthening QRS complex. [1,2,3,4] Figure (1) ECG waveform.…”

Section: Introductionmentioning

confidence: 99%

Remote ECG signal monitoring and classification based on Arduino with AD8232 sensor

Hamad¹,

Jasim²

2021

UTJES

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Real time monitoring with IOT is developed in the industry of health care , this can enable the doctors and specialist to diagnosis the patient status in quick, smart and efficient methods. Although, there is a lot of research and studies are designed methods for observing the ECG signal remotely, there are no proposed methods for classifying these signals with monitoring, and therefore , to design complete health care system, classification techniques should be used to classify the extracted signal. In this paper , We have proposed ECG monitoring and classification system. The proposed system is extracted ECG signal based on AD8232 sensor with the ardunino nodeMcu, analog to digital converter and its communication is used to convert the signal to more precision , then the extracted signal is transmitted to cloud to be used at anywhere by using cloud, the signal is pre-processed to remove the noise and QRS complex is detected to determine the other characteristics of the signal such as heart rate, also to determine one cycle of ECG signal, later the signal is classified by using proposed convolution neural network model to detect the signal status. The extracted ECG signal is transmitted in real time to cloud (Ubidots cloud is used) through ESP8266 over to the cloud using WiFi based on MQTT publishing method. The experimental results are performed on different signals and the different stage of de-noising and QRS detection are applied and different pooling layers are used in the proposed CNN model. The results show that the proposed classification model is achieved accuracy (94.94%) with ( 94.56%), (94.56% ) and ( 5.06) for sensitivity, specificity and error rate (ERR) respectively

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Section: Introductionmentioning

confidence: 99%

Remote ECG signal monitoring and classification based on Arduino with AD8232 sensor

Hamad¹,

Jasim²

2021

UTJES

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A Probabilistic Approach for Heart Rate Variability Analysis Using Explicit Duration Hidden Markov Models

Gao

Teng

Ertin

2018

2018 IEEE Statistical Signal Processing Workshop (SSP)

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Raspberry PI Based ECG Data Acquisition System by Using Block Chain

Navaraja¹,

Deepika²,

Birundha³

et al. 2021

IJARSCT

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There have been revolutionary developments in the healthcare industry with the advancement of technology over the past years. Technology has widespread health records that may have been digitized into electronic health records. Internet of Things, Cloud Computing, Block chain technology, lab-on-chip, non-invasive and minimally invasive surgeries and so on has simplified several dreadful diseases. The research as well as healthcare industry has been greatly impacted by these new technologies. In such case accessibility of health data from one provider to another at the right time remains a major challenge, especially when patients are in a critical condition having access to fragmented health records from multiple sources into a single chain. The proposed system aims to exchange health information on a block chain platform to build a smart e-health system. In this system, block chain is a clinical data repository that provides patients a complete, distributed ledger record containing records of all the events and seamless access to their electronic health records through healthcare providers. As an important feature, this system provides high security and integrity through cryptographic hash functions.

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ECG feature detection using randomly compressed samples for stable HRV analysis over low rate links

Cited by 3 publications

References 9 publications

Remote ECG signal monitoring and classification based on Arduino with AD8232 sensor

Remote ECG signal monitoring and classification based on Arduino with AD8232 sensor

A Probabilistic Approach for Heart Rate Variability Analysis Using Explicit Duration Hidden Markov Models

Raspberry PI Based ECG Data Acquisition System by Using Block Chain

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