Identification of electrocardiogram signals using internet of things based on combinatory classification

2017 ◽  
Vol 08 (03) ◽  
pp. 1750035 ◽  
Author(s):  
Marzieh Faridi Masouleh ◽  
Mohammad Ali Afshar Kazemi ◽  
Mahmood Alborzi ◽  
Abbas Toloie Eshlaghy
Keyword(s):  
Feature Extraction ◽  
Internet Of Things ◽  
Health Systems ◽  
Information Provision ◽  
Ecg Signal ◽  
Electrocardiogram Signals ◽  
Digital Identification ◽  
Electrocardiogram Ecg ◽  
Service Customization ◽  
Taking Care

Combination of computer sciences and electronics has resulted in one of the most remarkable technologies of the recent years called internet of things, considered as a challenge in electronic health systems for taking care of patients. Internet of things presents a promising paradigm for management of digital identification in the form of service customization. The effect of internet of things on healthcare is still in its preliminary stages and requires a substantial development. Various equipment and services are developed and utilized for health systems by providing different things to establish communication and information provision to users at any conditions or places. In this paper, attempts have been made to detect electrocardiogram (ECG) signal through a wireless simple sensing network of body using internet of things operating based on classification and feature extraction.

scholarly journals Capturing Electrocardiogram Signals from Chairs by Multiple Capacitively Coupled Unipolar Electrodes

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2835 ◽  
Author(s):  
Zhongjie Hou ◽  
Jinxi Xiang ◽  
Yonggui Dong ◽  
Xiaohui Xue ◽  
Hao Xiong ◽  
...  
Keyword(s):  
Printed Circuit Board ◽  
Experimental Tests ◽  
Circuit Board ◽  
Signal Acquisition ◽  
Ecg Signal ◽  
Capacitively Coupled ◽  
Ecg Signals ◽  
Printed Circuit ◽  
Electrocardiogram Signals ◽  
Electrocardiogram Ecg

A prototype of an electrocardiogram (ECG) signal acquisition system with multiple unipolar capacitively coupled electrodes is designed and experimentally tested. Capacitively coupled electrodes made of a standard printed circuit board (PCB) are used as the sensing electrodes. Different from the conventional measurement schematics, where one single lead ECG signal is acquired from a pair of sensing electrodes, the sensing electrodes in our approaches operate in a unipolar mode, i.e., the biopotential signals picked up by each sensing electrodes are amplified and sampled separately. Four unipolar electrodes are mounted on the backrest of a regular chair and therefore four channel of signals containing ECG information are sampled and processed. It is found that the qualities of ECG signal contained in the four channel are different from each other. In order to pick up the ECG signal, an index for quality evaluation, as well as for aggregation of multiple signals, is proposed based on phase space reconstruction. Experimental tests are carried out while subjects sitting on the chair and clothed. The results indicate that the ECG signals can be reliably obtained in such a unipolar way.

scholarly journals Fast and Accurate Algorithm for ECG Authentication Using Residual Depthwise Separable Convolutional Neural Networks

2020 ◽  
Vol 10 (9) ◽  
pp. 3304 ◽  
Author(s):  
Eko Ihsanto ◽  
Kalamullah Ramli ◽  
Dodi Sudiana ◽  
Teddy Surya Gunawan
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Feature Extraction ◽  
Processing Speed ◽  
State Of The Art ◽  
Ecg Signal ◽  
Biometric Authentication ◽  
Beat Detection ◽  
Two Stages ◽  
Electrocardiogram Ecg

The electrocardiogram (ECG) is relatively easy to acquire and has been used for reliable biometric authentication. Despite growing interest in ECG authentication, there are still two main problems that need to be tackled, i.e., the accuracy and processing speed. Therefore, this paper proposed a fast and accurate ECG authentication utilizing only two stages, i.e., ECG beat detection and classification. By minimizing time-consuming ECG signal pre-processing and feature extraction, our proposed two-stage algorithm can authenticate the ECG signal around 660 μs. Hamilton’s method was used for ECG beat detection, while the Residual Depthwise Separable Convolutional Neural Network (RDSCNN) algorithm was used for classification. It was found that between six and eight ECG beats were required for authentication of different databases. Results showed that our proposed algorithm achieved 100% accuracy when evaluated with 48 patients in the MIT-BIH database and 90 people in the ECG ID database. These results showed that our proposed algorithm outperformed other state-of-the-art methods.

An IoT-Based Remote Health Monitoring System for Smart Healthcare

2020 ◽  
pp. 231-254
Author(s):  
Uday Maji ◽  
Rohan Mandal ◽  
Saurav Bhattacharya ◽  
Shalini Priya
Keyword(s):  
Internet Of Things ◽  
Monitoring System ◽  
Health Monitoring ◽  
Remote Monitoring ◽  
Ecg Signal ◽  
Smart Healthcare ◽  
Electrocardiogram Ecg ◽  
Monitoring Devices ◽  
Remote Health Monitoring System ◽  
Remote Health

Many automated health monitoring devices detect health abnormalities based on gleaned data. One of the effective approaches of monitoring a senior cardiac patient is the analysis of an Electrocardiogram (ECG) signal, as proven by various studies and applications. However, diagnosis results must be communicated to an expert. An intelligent and effective technology gaining wide popularity known as ‘internet of things' or ‘IoT' allows remote monitoring of the patient.

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 ECG Signal Classification Review

2021 ◽  
Vol 5 (1) ◽  
pp. 15
Author(s):  
Muhammad Rausan Fikri ◽  
Indah Soesanti ◽  
Hanung Adi Nugroho
Keyword(s):  
Feature Extraction ◽  
Heart Function ◽  
Circulatory System ◽  
Ecg Signal ◽  
Main Task ◽  
Classification Methods ◽  
Ecg Signals ◽  
Electrocardiogram Signals ◽  
Network Methods ◽  
Two Stages

The heart is an important part of the human body, functioning to pump blood through the circulatory system. Heartbeats generate a signal called an ECG signal. ECG signals or electrocardiogram signals are basic raw signals to identify and classify heart function based on heart rate. Its main task is to analyze each signal in the heart, whether normal or abnormal. This paper discusses some of the classification methods which most frequently used to classify ECG signals. These methods include pre-processing, feature extraction, and classification methods such as MLP, K-NN, SVM, CNN, and RNN. There were two stages of ECG classification, the feature extraction stage and the classification stage. Before ECG features were extracted, raw ECG signal data first processed in the pre-processing stage because ECG signals were not necessarily free of noise. Noise will cause a decrease in accuracy during the classification process. After features were extracted, ECG signals were then classified with the classification method. Neural Network methods such as CNN and RNN are best to use since they can give better accuracy. For further research, the machine learning method needs to be improved to get high accuracy and high precision in the ECG signals classification.

Introduction to Heart

2014 ◽  
pp. 96-112
Keyword(s):  
The Body ◽  
Ecg Signal ◽  
Electrocardiogram Signals ◽  
Signal Characteristics ◽  
Improve Measurement ◽  
Computer Based ◽  
The Common ◽  
Diagnosis Tool ◽  
Heart Disorders ◽  
Electrocardiogram Ecg

The heart is an important organ in the human body, for pumping the blood throughout the body. An electrocardiogram (ECG) is a diagnosis tool that reports the electrical operation of the heart, recorded by skin electrodes at specific locations on the body. The introduction of computer-based methods for the purpose of quantifying different ECG signal characteristics is the result of a desire to improve measurement accuracy as well as reproducibility. In this chapter, the author explains the basic definitions in heart studies and the electrocardiogram signals. In addition, the importance of interpretation and measuring the effective features in heart signals to detect the heart disorders is described. Finally, some of the common disorders of heart are briefly explained.

scholarly journals Implementasi Pengamanan Transmisi Sinyal EKG (Elektrokardiogram) secara Daring dengan Metode Anonimasi

2019 ◽  
Vol 7 (1) ◽  
pp. 85
Author(s):  
JUSAK JUSAK ◽  
BRAMASTA AGNANDA SETIAWAN ◽  
SONY SOLEHUDIN ◽  
IRA PUSPASARI
Keyword(s):  
Internet Of Things ◽  
World Health Organization ◽  
World Health ◽  
The Internet ◽  
Ecg Signal ◽  
Risk Of Death ◽  
Health Organization ◽  
Electrocardiogram Ecg ◽  
The Internet Of Things ◽  
Secure Transmission

ABSTRAKData World Health Organization (WHO) pada tahun 2014 menunjukkan bahwa di Indonesia sebanyak 37% dari seluruh penyebab kematian adalah penyakit yang berhubungan dengan jantung. Kehadiran teknologi dan pemanfaatan Internet of Things (IoT) diharapkan dapat membantu mengurangi resiko kematian akibat penyakit jantung tersebut. Pada penelitian ini, pengukuran dan pengamatan sinyal jantung melalui tele-auskultasi sinyal elektrokardiogram (EKG) dilakukan. Untuk mengamankan sinyal EKG dalam proses transmisi melalui jaringan Internet digunakan metode anonimasi sinyal berbasis algoritma Jusak-Seedahmed. Hasil pengujian menunjukkkan bahwa algoritma Jusak-Seedahmed dapat melakukan proses anonimasi dan proses rekonstruksi sinyal dengan baik. Pengujian korelasi silang antara sinyal hasil rekonstruksi dan sinyal EKG asli sebelum anonimasi menghasilkan korelasi sebesar 1 pada lag=0. Sinyal EKG hasil rekonstruksi ditampilkan dalam aplikasi mobile untuk memudahkan analisis oleh dokter.Kata kunci: elektrokardiogram, keamanan, anonimasi, IoT, FFT ABSTRACTBased on the latest data released by the World Health Organization in 2014, deaths caused by cardiovascular disease in 2012 have reached 37% of the total number of non-communicable diseases deaths in Indonesia. Therefore, it is expected that the applications of the Internet of Things (IoT) might be used to reduce the risk of death due to the heart related problems. In this research, a tele-auscultation technique for measuring and monitoring electrocardiogram (ECG) signal was built. To secure transmission of the ECG signal over the Internet, we implemented a recently proposed Jusak-Seedahmed algorithm. Our examinations showed that the algorithm performed the anonymization and reconstruction processes well. Crosscorrelation analysis showed that correlation between the reconstructed and the original ECG signal at lag=0 was 1. Furthermore, a mobile-based application had been built to display the reconstructed ECG signal for further analysis.Keywords: electrocardiogram, security, anonimization, IoT, FFT

An IoT-Based Remote Health Monitoring System for Smart Healthcare

2021 ◽  
pp. 455-473
Author(s):  
Uday Maji ◽  
Rohan Mandal ◽  
Saurav Bhattacharya ◽  
Shalini Priya
Keyword(s):  
Internet Of Things ◽  
Monitoring System ◽  
Health Monitoring ◽  
Remote Monitoring ◽  
Ecg Signal ◽  
Smart Healthcare ◽  
Electrocardiogram Ecg ◽  
Monitoring Devices ◽  
Remote Health Monitoring System ◽  
Remote Health

Many automated health monitoring devices detect health abnormalities based on gleaned data. One of the effective approaches of monitoring a senior cardiac patient is the analysis of an Electrocardiogram (ECG) signal, as proven by various studies and applications. However, diagnosis results must be communicated to an expert. An intelligent and effective technology gaining wide popularity known as ‘internet of things' or ‘IoT' allows remote monitoring of the patient.

A Fourier-Bessel Expansion-Based Method for Automated Detection of Atrial Fibrillation From Electrocardiogram Signals

2019 ◽  
pp. 248-277
Author(s):  
Ashish Sharma ◽  
Shivnarayan Patidar
Keyword(s):  
Atrial Fibrillation ◽  
Ecg Signal ◽  
Clinical Environment ◽  
Test Dataset ◽  
Ecg Signals ◽  
Rr Interval ◽  
Electrocardiogram Signals ◽  
Detection And Identification ◽  
Bagged Decision Trees ◽  
Electrocardiogram Ecg

This chapter presents a new methodology for detection and identification of cardiovascular diseases from a single-lead electrocardiogram (ECG) signal of short duration. More specifically, this method deals with the detection of the most common cardiac arrhythmia called atrial fibrillation (AF) in noisy and non-clinical environment. The method begins with appropriate pre-processing of ECG signals in order to get the RR-interval and heart rate (HR) signals from them. A set of indirect features are computed from the original and the transformed versions of RR-interval and HR signals along with a set of direct features that are obtained from ECG signals themselves. In all, 47 features are computed and subsequently they are fed to an ensemble system of bagged decision trees for classifying the ECG recordings into four different classes. The proposed method has been evaluated with 2017 PhysioNet/CinC challenge hidden test dataset (phase II subset) and the final F1 score of 0.81 is obtained.

Identification of myocardial infarction from analysis of ECG signal

2020 ◽  
Vol 24 (3) ◽  
pp. 217-226
Author(s):  
D.B.V. Jagannadham ◽  
D.V. Sai Narayana ◽  
P. Ganesh ◽  
D. Koteswar
Keyword(s):  
Myocardial Infarction ◽  
Feature Extraction ◽  
Early Stage ◽  
Heart Diseases ◽  
Ecg Signal ◽  
St Segment Elevation ◽  
T Wave ◽  
European Database ◽  
St Segment ◽  
Electrocardiogram Ecg

Many heart diseases can be identified and cured at an early stage by studying the changes in the features of electrocardiogram (ECG) signal. Myocardial Infarction (MI) is the serious cause of death worldwide. If MI can be detected early, the death rate will reduce. In this paper, an algorithm to detect MI in an ECG signal using Daubechies wavelet transform technique is developed. The ECG signal-denoising is performed by removing the corresponding wavelet coefficients at higher scale. After denoising, an important step towards identifying an arrhythmia is the feature extraction from the ECG. Feature extraction is carried out to detect the R peaks of the ECG signal. Since as R peak is having the highest amplitude, and therefore it is detected in the first round, subsequently location of other peaks are determined. Having completed the preprocessing and the feature extraction the MI is detected from the ECG based on inverted T wave logic and ST segment elevation. The algorithm was evaluated using MIT-BIH database and European database satisfactorily.

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