Blinded Analysis of an Exercise ECG Database Using High Frequency QRS Analysis

Omer, Noam; Granot, Yair; Kähönen, Mika; Lehtinen, Rami; Nieminen, Tuomo; Nikus, Kjell; Lehtimäki, Terho; Viik, Jari; Abboud, Shimon

doi:10.22489/cinc.2017.010-254

Cited by 3 publications

(2 citation statements)

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“…The ANN is an adaptive system with exciting features such as the ability to adapt, learn, and summarize; because ANN's parallel processing, self-organizing, fault-tolerant, and adaptive capabilities make it capable of solving many complex problems, ANN is also very accurate in the classification and prediction of outputs [35]. The neural network (NN) consists of the number of layers; the initial layer has an association as of the system input, and the end layer gives the output of the network [36].…”

Section: Annmentioning

confidence: 99%

A Review Study for Electrocardiogram Signal Classification

Abdulla

Al-Ani

2020

UHD J SCI TECH

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An electrocardiogram (ECG) signal is a recording of the electrical activity generated by the heart. The analysis of the ECG signal has been interested in more than a decade to build a model to make automatic ECG classification. The main goal of this work is to study and review an overview of utilizing the classification methods that have been recently used such as Artificial Neural Network, Convolution Neural Network (CNN), discrete wavelet transform, Support Vector Machine (SVM), and K-Nearest Neighbor. Efficient comparisons are shown in the result in terms of classification methods, features extraction technique, dataset, contribution, and some other aspects. The result also shows that the CNN has been most widely used for ECG classification as it can obtain a higher success rate than the rest of the classification approaches.

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Section: Annmentioning

confidence: 99%

A Review Study for Electrocardiogram Signal Classification

Abdulla

Al-Ani

2020

UHD J SCI TECH

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“…Figure 2 presents the proposed block diagram which is used to heartbeats monitoring based on the optimized Convolutional Neural Network (CNN), as well as the proposed model, is known as Heartbeats Classification Model (HCM). For training and testing of proposed Heartbeats Classification Model (HCM), the Standard MIT-BIH arrhythmia database is utilized with hybrid Convolutional Neural Network (CNN) architecture [9]. Here, R-peaks, as well as R-R intervals,are used as ECG features and in Heartbeats Classification Model (HCM); feature extraction plays a significant responsibility to categorize masses of cardiac diseases (Fig.…”

Section: Introductionmentioning

confidence: 99%

Intellectual heartbeats classification model for diagnosis of heart disease from ECG signal using hybrid convolutional neural network with GOA

Tyagi

Mehra

2021

SN Appl. Sci.

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Automatic heart disease detection from human heartbeats is a challenging and intellectual assignment in signal processing because periodically monitoring of the heart beat arrhythmia for patient is an essential task to reduce the death rate due to cardiovascular disease (CVD). In this paper, the focus of research is to design hybrid Convolutional Neural Network (CNN) architecture by making use of Grasshopper Optimization Algorithm (GOA) to classify different types of heart diseases from the ECG signal or human heartbeats. Convolutional Neural Network (CNN) as an artificial intelligence approach is widely used in computer vision-based medical data analysis. However, the traditional CNN cannot be used for classification of heart diseases from the ECG signal because lots of noise or irrelevant data is mixed with signal. So this study utilizes the pre-processing and selection of feature for proper heart diseases classification, where Discrete Wavelet Transform (DWT) is used for the noise reduction as well as segmentation of ECG signal and Grasshopper Optimization Algorithm (GOA) is used for selection of R-peaks features from the extracted feature sets in terms of R-peaks and R-R intervals that help to attain better classification accuracy. For training as well as testing of projected Heartbeats Classification Model (HCM), the Standard MIT-BIH arrhythmia database is utilized with hybrid Convolutional Neural Network (CNN) architecture. The assortment of proper R-peaks and R-R intervals is a major factor and because of the deficiency of apposite pre-processing phases like noise removal, signal decomposition, smoothing and filtering, the uniqueness of extracted feature is less. The experimental outcomes show that the planned HCM is effective for detecting irregular human heartbeats via R-peaks and R-R intervals. When the proposed Heartbeats Classification Model (HCM) was verified on the database, model achieved higher efficiency than other state-of-the-art techniques for 16 heartbeat disease categories and the average classification accuracy is 99.58% with fast and robust responses where the correctly classified heartbeats are 86,005 and misclassified beats is only 108 with 0.42% error rate.

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