ECG signal feature extraction and classification based on R peaks detection in the phase space

Malgina, Olga; Milenković, Jana; Plesnik, Emil; Zajc, Matej; Tasič, J.F.

doi:10.1109/ieeegcc.2011.5752545

Cited by 12 publications

(2 citation statements)

References 5 publications

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“…Feature Generation: Once a portrait is generated, the next step is to extract appropriate features from it. Based on the work in [3,4], we extract a total of eight features.…”

Section: Ecg Morphology Alteration De-tectionmentioning

confidence: 99%

Detecting malicious morphological alterations of ECG signals in body sensor networks

Cai

Venkatasubramanian

2015

Proceedings of the 14th International Conference on Information Processing in Sensor Networks

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Body Sensor Network (BSN) -a network of body-worn wireless health monitoring sensors -have a tremendous potential to remove the space and time restrictions on health management. Given the importance of the data BSNs collect for improved health outcomes, securing the data from unauthorized tampering is essential. A compromised (or externally influenced) sensor in a BSN may generate erroneous patient data leading to, among other things, wrong diagnosis and treatment. In this paper, we present a novel approach to address the problem of detecting maliciously induced morphological alterations in the ECG signal (i.e., inducing changes to its shape). Our approach works by correlating the ECG signals with synchronously measured arterial blood pressure (ABP) signal measured using a distinct (and un-compromised) sensor. Initial analysis of our system demonstrates promising results, with 99.75% accuracy in detecting ECG signal morphological alterations for healthy patients with normal sinus rhythms.

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“…Feature Generation: Once a portrait is generated, the next step is to extract appropriate features from it. Based on the work in [3,4], we extract a total of eight features.…”

Section: Ecg Morphology Alteration De-tectionmentioning

confidence: 99%

Detecting malicious morphological alterations of ECG signals in body sensor networks

Cai

Venkatasubramanian

2015

Proceedings of the 14th International Conference on Information Processing in Sensor Networks

View full text Add to dashboard Cite

show abstract

“…Over the last decade, many machine learning approaches have been applied to classify ECG signals. Machine Learning algorithms such as Support Vector Machines (SVM) [3,4] and K-Nearest Neighbors (KNN) [4] have been used for the classification. One of the studies proposed the use of a deep feed-forward neural network [5] for classification which involved the extraction of six types of features from the ECG signal.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning based Arrhythmia Classification with an ECG Acquisition System

Badrinath*,

Navada,

Narahari

et al. 2019

IJITEE

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One of the issues that the human body faces is arrhythmia, a condition where the human heartbeat is either irregular, too slow or too fast. One of the ways to diagnose arrhythmia is by using ECG signals, the best diagnostic tool for detection of arrhythmia. This paper describes a deep learning approach to check whether signs of arrhythmia, in a given input signal, are present or not. A batch normalized CNN is used to classify the ECG signals based on the different types of arrhythmia. The model has achieved 96.39% training accuracy and 97% testing accuracy. The ECG signals are classified into five classes namely: Normal beats, Premature Ventricular Contraction (PVC) beats, Right Bundle Branch Block (RBBB) beats, Left Bundle Branch Block (LBBB) beats and Paced beats. A peak detection algorithm with six simple steps is designed to detect R-peaks from the ECG signals. A hardware device is built using Raspberry Pi to acquire ECG signals, which are then sent to the trained CNN for classification. The data-set for training is obtained from the MIT-BIH repository. Keras and Tensorflow libraries are used to design and develop the CNN and an application is designed using ’MEAN’ stack and ’Flask’ based servers.

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Electrocardiogram Diagnosis Based on SMOTE+ENN and Random Forest

Shang

Cao

et al. 2019

Lecture Notes in Computer Science

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ECG signal feature extraction and classification based on R peaks detection in the phase space

Cited by 12 publications

References 5 publications

Detecting malicious morphological alterations of ECG signals in body sensor networks

Detecting malicious morphological alterations of ECG signals in body sensor networks

Deep Learning based Arrhythmia Classification with an ECG Acquisition System

Electrocardiogram Diagnosis Based on SMOTE+ENN and Random Forest

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