Uniform Sampling of ECG Waveform of MIT-BIH Normal Sinus Rhythm Database at Desired Intervals

Sharma, J. N.; Kumar, Vikash; Ayub, Shahanaz; Saini, Jitender

doi:10.5120/7845-0912

Cited by 5 publications

(5 citation statements)

References 4 publications

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“…It transfers the data from the dendrites to the synapses by passing them through threshold functions. There are three types of threshold functions commonly used in ANN models [ 15 ]: Hard limiter function Threshold function Sigmoid function. …”

Section: Methodsmentioning

confidence: 99%

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Detection of Heart Arrhythmia on Electrocardiogram using Artificial Neural Networks

Badr¹,

Al-Otaibi

Alturki

et al. 2022

Computational Intelligence and Neuroscience

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The electrocardiogram, also known as an electrocardiogram (ECG), is considered to be one of the most significant sources of data regarding the structure and function of the heart. In order to obtain an electrocardiogram, the contractions and relaxations of the heart are first captured in the proper recording medium. Due to the fact that irregularities in the functioning of the heart are reflected in the ECG indications, it is possible to use these indications to diagnose cardiac issues. Arrhythmia is the medical term for the abnormalities that might occur in the regular functioning of the heart (rhythm disorder). Environmental and genetic variables can both play a role in the development of arrhythmias. Arrhythmias are reflected on the ECG sign, which depicts the same region regardless of where in the heart they occur; thus, they may be seen in ECG signals. This is how arrhythmias can be detected. Due to the time limits of this study, the ECG signals of individuals who were healthy, as well as those who suffered from arrhythmias were divided into 10-minute segments. The arithmetic mean approach is one of the fundamental statistical factors. It is used to construct the feature vectors of each received wave and interval, and these vectors offer information regarding arrhythmias in accordance with the agreed-upon temporal restrictions. In order to identify the heart arrhythmias, the obtained feature vectors are fed into a classifier that is based on a multilayer perceptron neural network. In conclusion, ROC analysis and contrast matrix are utilised in order to evaluate the overall correct classification result produced by the ECG-based classifier. Because of this, it has been demonstrated that the method that was recommended has high classification accuracy when attempting to diagnose arrhythmia based on ECG indications. This research makes use of a variety of diagnostic terminologies, including ECG signal, multilayer perceptron neural network, signal processing, disease diagnosis, and arrhythmia diagnosis.

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Section: Methodsmentioning

confidence: 99%

“…ere are three types of threshold functions commonly used in ANN models [15]: (i) Hard limiter function (ii) reshold function (iii) Sigmoid function.…”

Section: Artificial Neural Network Modelmentioning

confidence: 99%

Detection of Heart Arrhythmia on Electrocardiogram using Artificial Neural Networks

Badr¹,

Al-Otaibi

Alturki

et al. 2022

Computational Intelligence and Neuroscience

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“…This program is not intended for clinical diagnosis but for scientific research purposes. [13] proposed an image processing method to convert samples at desired intervals, so that the MIT-BIH database could be used widely and universally. The MIT-BIH database for normal sinus rhythm is sampled at a frequency of 128 Hz and data is available at uniform intervals of 7.8125 ms.…”

Section: Improved Information On Heart Examination Resultsmentioning

confidence: 99%

“…[20] states that Matlab can measure waves, segments and ECG signal intervals using the Pan-Tompkins algorithm. Noting some observers who have examined the PQRST, it appears that ECG discrete data were obtained from image sampling (Ashis Birle, Hussain, Jitu, Lai Khin, Marcus), from the results of sound sampling (Ali Tariq, JS Dhir), and from MIT data BIH [21], [22], [23]. In this study, 30 patient samples were examined by clinical standards using Electrocardiograph Discrete (ECGd).…”

Section: Improved Information On Heart Examination Resultsmentioning

confidence: 99%

Improved Information on Heart Examination Results Uses a 12-lead Discrete Electrocardiograph

Setiawidayat

2020

EJECE

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The results of examination of the condition of the heart using an electrocardiograph are generally presented in an ecg paper. Using ecg paper because in the paper there are boxes that are used as guidelines for calculating wave amplitude and wave duration. If the required amplitude is information on peak amplitude and wave morphology information, then the duration is information on the value of segment duration, interval duration, and heart rate. As is known in the ECG paper the results of the examination that only some information is presented, so information that does not yet exist must be calculated manually using existing boxes. This manual calculation requires time and accuracy, so that this waiting time can cause the patient's disease stage to increase, and on the other hand that the lack of accuracy of the calculation can cause misdiagnosis. This study aims to provide information on clinical standard cardiac examination results using Electrocardiograph discrete (ECGd) and standard Electrocardiograph (ECGs). In ECGd, the leads of the heart signal are sampled at a frequency of 250 Hz so that it becomes discrete data. Maximum filtering on 1.6 mV treshold data discrete peak R is obtained in each cycle. The PQRST algorithm method is used to get the PQST peak and duration parameters. The results showed that the percentage of the amount of information from the ECGd examination was 82.4% while the results of the ECGs examination were 25.4%

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“…The third dataset was the Massachusetts institute of technology-Beth Israel hospital (MIT-BIH) biosignal dataset from Beth Israel Hospital in Boston, USA [ 14 , 15 ]. We used the MIT-BIH arrhythmia dataset collected from 47 patients diagnosed with arrhythmias as well as the normal SR dataset collected from 18 patients who had no significant arrhythmias.…”

Section: Methodsmentioning

confidence: 99%

Unsupervised feature learning for electrocardiogram data using the convolutional variational autoencoder

et al. 2021

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Most existing electrocardiogram (ECG) feature extraction methods rely on rule-based approaches. It is difficult to manually define all ECG features. We propose an unsupervised feature learning method using a convolutional variational autoencoder (CVAE) that can extract ECG features with unlabeled data. We used 596,000 ECG samples from 1,278 patients archived in biosignal databases from intensive care units to train the CVAE. Three external datasets were used for feature validation using two approaches. First, we explored the features without an additional training process. Clustering, latent space exploration, and anomaly detection were conducted. We confirmed that CVAE features reflected the various types of ECG rhythms. Second, we applied CVAE features to new tasks as input data and CVAE weights to weight initialization for different models for transfer learning for the classification of 12 types of arrhythmias. The f1-score for arrhythmia classification with extreme gradient boosting was 0.86 using CVAE features only. The f1-score of the model in which weights were initialized with the CVAE encoder was 5% better than that obtained with random initialization. Unsupervised feature learning with CVAE can extract the characteristics of various types of ECGs and can be an alternative to the feature extraction method for ECGs.

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Uniform Sampling of ECG Waveform of MIT-BIH Normal Sinus Rhythm Database at Desired Intervals

Cited by 5 publications

References 4 publications

Detection of Heart Arrhythmia on Electrocardiogram using Artificial Neural Networks

Detection of Heart Arrhythmia on Electrocardiogram using Artificial Neural Networks

Improved Information on Heart Examination Results Uses a 12-lead Discrete Electrocardiograph

Unsupervised feature learning for electrocardiogram data using the convolutional variational autoencoder

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