Design of Robust Heart Abnormality Detection System based on Wavelet Denoising Algorithm

Abstract The technology that continues to be developed by many researchers today is an automatic heart attack detection system based on an Electrocardiogram (ECG) signal. Several other studies have been carried out to build an Internet of Things (IoT) based heart abnormality detection system. Based on the analysis of related studies that have been carried out previously, several researchers have developed an ECG signal-based heart abnormality detection system using clean ECG signal data. While the reality of the concept of an IoT-based detection system, the process of recording ECG signal data on the sensor, the process of sending data to the server, and the process of retrieving data from the server are vulnerable to noise exposure. ECG signal containing noise will greatly affect the accuracy of system detection. This paper proposes the development of a noise-resistant heart condition detection system using a wavelet denoising algorithm. The process of denoising ECG signals using the Wavelet algorithm is generally able to improve the accuracy of detecting noisy ECG signals. The most significant increase in accuracy is seen in the low SNR value. The Daubechies 4 (db4) denoising algorithm is the best-performing algorithm. The ECG signal classification method uses the Artificial Neural Network (ANN) algorithm. Detection system hardware is also designed in this research using the concept based on the Internet of Things.

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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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Machine Learning-Based IoT-Botnet Attack Detection with Sequential Architecture

Sensors ◽

10.3390/s20164372 ◽

2020 ◽

Vol 20 (16) ◽

pp. 4372 ◽

Cited By ~ 1

Author(s):

Yan Naung Soe ◽

Yaokai Feng ◽

Paulus Insap Santosa ◽

Rudy Hartanto ◽

Kouichi Sakurai

Keyword(s):

Machine Learning ◽

High Performance ◽

Detection System ◽

Rapid Development ◽

Attack Detection ◽

Cyber Attacks ◽

Detection Systems ◽

Iot Devices ◽

Artificial Neural Network Ann ◽

Feature Selection Approach

With the rapid development and popularization of Internet of Things (IoT) devices, an increasing number of cyber-attacks are targeting such devices. It was said that most of the attacks in IoT environments are botnet-based attacks. Many security weaknesses still exist on the IoT devices because most of them have not enough memory and computational resource for robust security mechanisms. Moreover, many existing rule-based detection systems can be circumvented by attackers. In this study, we proposed a machine learning (ML)-based botnet attack detection framework with sequential detection architecture. An efficient feature selection approach is adopted to implement a lightweight detection system with a high performance. The overall detection performance achieves around 99% for the botnet attack detection using three different ML algorithms, including artificial neural network (ANN), J48 decision tree, and Naïve Bayes. The experiment result indicates that the proposed architecture can effectively detect botnet-based attacks, and also can be extended with corresponding sub-engines for new kinds of attacks.

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An Optimized FPGA Based System Design for the Arrhythmia Detection using ECG

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.j9420.119119 ◽

2019 ◽

Vol 9 (1) ◽

pp. 2808-2818

Keyword(s):

System Design ◽

Detection System ◽

Ecg Signal ◽

Noise Elimination ◽

Is Success ◽

Arrhythmia Detection ◽

Ecg Signals ◽

Electrocardiogram Signals ◽

Reconstructed Signal ◽

Signal Peak

Electrocardiogram signals are highly susceptible to interferences caused due to various kinds of noises including artefacts’, disruptions in power lines attained from the human interferences and device disturbances. These noise signals tend to lower the quality of signals that result in crucial environment for detecting and diagnosing different types of arrhythmia. In order to avoid this issue, multiple filtering techniques are being incorporated out of all Gaussian filters with Haar DWT portray better outcomes in noise elimination and smoothening of signal. The process of ECG signal filtering allows performing the testing and validation of in the actual world emulation. Enhancement in PSNR ratio is observed by using the ECG signal filters along the reconstructed signal. For a given input ECG signal, the levels of the signal peak decide if the patient is suffering from arrhythmia or not. If peak is low, patient is detected with the arrhythmia disease, if high patient is normal. The results can be observed in simulation. FPGA prototyping of the design is carried out along the hardware debugging in chip scope pro tool. The design is realized using Verilog coding with the technique of morphological filtering. For the purpose of debugging the hardware device used is Artix-7. The FPGA methodology is success full in a position to detect arrhythmia. The framework based on FPGA is structured and executed in the paper which can detect a type of arrhythmia which indicates Atrio Ventricular block along with all the noises removed. The simulation results are obtained by taking ECG signals from MIT-BIH arrhythmia database. The proposed FPGA based system design is proven to be optimized as it showed very less utilization of resources when compared to previous arrhythmia detection system designs.

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Classification of Heart Rate Time Series Using Machine Learning Algorithms

Advances in Machine Learning & Artificial Intelligence ◽

10.33140/amlai.02.01.09 ◽

2021 ◽

Vol 2 (1) ◽

Keyword(s):

Detection System ◽

Machine Learning Algorithms ◽

Support Vector ◽

Ecg Signal ◽

Ecg Signals ◽

Heart Patients ◽

Extraction Step ◽

Ecg Arrhythmia ◽

Electrocardiogram Ecg ◽

The Given

An important diagnostic method for diagnosing abnormalities in the human heart is the electrocardiogram (ECG). A large number of heart patients increase the assignment of physicians. To reduce their assignment, an automatic computer detection system is needed. In this study, a computer system for classifying ECG signals is presented. The MIT-BIH, ECG arrhythmia database is used for analysis. After the ECG signal is noisy in the preprocessing stage, the data feature is extracted. In the feature extraction step, the decision tree is used and the support vector machine (SVM) is constructed to classify the ECG signal into two categories. It is normal or abnormal. The results show that the system classifies the given ECG signal with 90% sensitivity.

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ANNIDS: Artificial Neural Network based Intrusion Detection System for Internet of Things

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.k1875.0981119 ◽

2019 ◽

Vol 8 (11) ◽

pp. 2583-2588 ◽

Cited By ~ 1

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Internet Of Things ◽

Intrusion Detection ◽

Intrusion Detection System ◽

Detection System ◽

Machine Learning Techniques ◽

Detection Systems ◽

Artificial Neural ◽

Artificial Neural Network Ann

Internet of Things (IoT) makes everything in the real world to get connected. The resource constrained characteristics and the different types of technology and protocols tend to the IoT be more vulnerable than the conventional networks. Intrusion Detection System (IDS) is a tool which monitors analyzes and detects the abnormalities in the network activities. Machine Learning techniques are implemented with the Intrusion detection systems to enhance the performance of IDS. Various studies on IoT reveals that Artificial Neural Network (ANN) provides better accuracy and detection rate than other approaches. In this paper, an Artificial Neural Network based IDS (ANNIDS) technique based on Multilayer Perceptron (MLP) is proposed to detect the attacks initiated by the Destination Oriented Direct Acyclic Graph Information Solicitation (DIS) attack and Version attack in IoT environment. Contiki O.S/Cooja Simulator 3.0 is used for the IoT simulation.

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Design of Intrusion Detection System for Wormhole Attack Detection in Internet of Things

Advances in Intelligent Systems and Computing - Advanced Computing and Intelligent Engineering ◽

10.1007/978-981-15-1081-6_44 ◽

2020 ◽

pp. 513-523

Author(s):

Snehal Deshmukh-Bhosale ◽

S. S. Sonavane

Keyword(s):

Internet Of Things ◽

Intrusion Detection ◽

Intrusion Detection System ◽

Detection System ◽

Attack Detection ◽

Wormhole Attack

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False Data Detection for Fog and Internet of Things Networks

Sensors ◽

10.3390/s19194235 ◽

2019 ◽

Vol 19 (19) ◽

pp. 4235 ◽

Cited By ~ 3

Author(s):

Romano Fantacci ◽

Francesca Nizzi ◽

Tommaso Pecorella ◽

Laura Pierucci ◽

Manuel Roveri

Keyword(s):

Internet Of Things ◽

Detection System ◽

Fog Computing ◽

Data Gathering ◽

Attack Detection ◽

The Internet ◽

Data Detection ◽

Time Data ◽

Attack Tree ◽

Standard Data

The Internet of Things (IoT) context brings new security issues due to billions of smart end-devices both interconnected in wireless networks and connected to the Internet by using different technologies. In this paper, we propose an attack-detection method, named Data Intrusion Detection System (DataIDS), based on real-time data analysis. As end devices are mainly resource constrained, Fog Computing (FC) is introduced to implement the DataIDS. FC increases storage, computation capabilities, and processing capabilities, allowing it to detect promptly an attack with respect to security solutions on the Cloud. This paper also considers an attack tree to model threats and vulnerabilities of Fog/IoT scenarios with heterogeneous devices and suggests countermeasure costs. We verify the performance of the proposed DataIDS, implementing a testbed with several devices that measure different physical quantities and by using standard data-gathering protocols.

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