Filtering the ECG Signal towards Heart Attack Detection using Motion Artifact Removal Technique

The Internet of Things (IoT) is inter communication of embedded devices using networking technologies. The IoT will be one of the important trends in future; can affect the networking, business and communication. In this paper, proposing a remote sensing parameter of the human body which consists of pulse and temperature. The parameters that are used for sensing and monitoring will send the data through wireless sensors. Adding a web based observing helps to keep track of the regular status of patient. The sensing data will be continuously collected in a database and will be used to inform patient to any unseen problems to undergo possible diagnosis. Experimental results prove the proposed system is user friendly, reliable, economical. IoT typically expected to propose the advanced high bandwidth connectivity of embedded devices, systems and services which goes beyond machine –to – machine (M2M) context. The advanced connectivity of devices aide in automation is possible in nearly all field. Everyone today is so busy in their lives; even they forget to take care of their health. By keeping all these things in minds, technology really proves to be an asset for an individual. With the advancement in technology, lots of smart or medical sensors came into existence that continuously analyzes individual patient activity and automatically predicts a heart attack before the patient feels sick.

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Motion Artifact Removal and Feature Extraction from PPG Signals Using Efficient Signal Processing Algorithms

2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI) ◽

10.1109/icacci.2018.8554599 ◽

2018 ◽

Cited By ~ 1

Author(s):

Anjana Luke ◽

Shereena Shaji ◽

K. A. Unnikrishna Menon

Keyword(s):

Signal Processing ◽

Feature Extraction ◽

Motion Artifact ◽

Artifact Removal ◽

Signal Processing Algorithms ◽

Processing Algorithms

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Accurate Detection of ECG Signals in ECG Monitoring Systems by Eliminating the Motion Artifacts and Improving the Signal Quality Using SSG Filter with DBE

Journal of Circuits System and Computers ◽

10.1142/s0218126620500243 ◽

2019 ◽

Vol 29 (02) ◽

pp. 2050024

Author(s):

Mahesh B. Dembrani ◽

K. B. Khanchandani ◽

Anita Zurani

Keyword(s):

Signal Analysis ◽

Motion Artifact ◽

Motion Artifacts ◽

Signal Quality ◽

Ecg Signal ◽

Qrs Complex ◽

Ecg Signals ◽

Cardiac Disorders ◽

Digital Processor ◽

Ica Algorithm

The automatic recognition of QRS complexes in an Electrocardiography (ECG) signal is a critical step in any programmed ECG signal investigation, particularly when the ECG signal taken from the pregnant women additionally contains the signal of the fetus and some motion artifact signals. Separation of ECG signals of mother and fetus and investigation of the cardiac disorders of the mother are demanding tasks, since only one single device is utilized and it gets a blend of different heart beats. In order to resolve such problems we propose a design of new reconfigurable Subtractive Savitzky–Golay (SSG) filter with Digital Processor Back-end (DBE) in this paper. The separation of signals is done using Independent Component Analysis (ICA) algorithm and then the motion artifacts are removed from the extracted mother’s signal. The combinational use of SSG filter and DBE enhances the signal quality and helps in detecting the QRS complex from the ECG signal particularly the R peak accurately. The experimental results of ECG signal analysis show the importance of our proposed method.

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End-to-End Deep Learning Fusion of Fingerprint and Electrocardiogram Signals for Presentation Attack Detection

Sensors ◽

10.3390/s20072085 ◽

2020 ◽

Vol 20 (7) ◽

pp. 2085 ◽

Cited By ~ 1

Author(s):

Rami M. Jomaa ◽

Hassan Mathkour ◽

Yakoub Bazi ◽

Md Saiful Islam

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Attack Detection ◽

Ecg Signal ◽

Ecg Signals ◽

Biometric Systems ◽

Fingerprint Biometrics ◽

End To End ◽

The Impact

Although fingerprint-based systems are the commonly used biometric systems, they suffer from a critical vulnerability to a presentation attack (PA). Therefore, several approaches based on a fingerprint biometrics have been developed to increase the robustness against a PA. We propose an alternative approach based on the combination of fingerprint and electrocardiogram (ECG) signals. An ECG signal has advantageous characteristics that prevent the replication. Combining a fingerprint with an ECG signal is a potentially interesting solution to reduce the impact of PAs in biometric systems. We also propose a novel end-to-end deep learning-based fusion neural architecture between a fingerprint and an ECG signal to improve PA detection in fingerprint biometrics. Our model uses state-of-the-art EfficientNets for generating a fingerprint feature representation. For the ECG, we investigate three different architectures based on fully-connected layers (FC), a 1D-convolutional neural network (1D-CNN), and a 2D-convolutional neural network (2D-CNN). The 2D-CNN converts the ECG signals into an image and uses inverted Mobilenet-v2 layers for feature generation. We evaluated the method on a multimodal dataset, that is, a customized fusion of the LivDet 2015 fingerprint dataset and ECG data from real subjects. Experimental results reveal that this architecture yields a better average classification accuracy compared to a single fingerprint modality.

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