Heart Rhythm Abnormality Detection and Classification using Machine Learning Technique

Kumari, Ch. Usha; Ankita, R.; Pavani, T.; Vignesh, N. Arun; Varma, Nithin; Manzar, Md. Aqeel; Reethika, A.

doi:10.1109/icoei48184.2020.9142914

Cited by 14 publications

(3 citation statements)

References 12 publications

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“…Huang et al [20] applies STFT spectrogram and 2D-CNN, achieving an accuracy of 99.00%, while Singh et al [21] relies on RNN LSTM, resulting Orhan [22] uses a CNN and reports an accuracy of 98.97%. Kaouter et al [23] combines CNN with Continuous Wavelet Transform (CWT), obtaining an accuracy of 93.75%, and Kumari et al [24] utilizes CWT and SVM, reaching an accuracy of 95.92%. Proposed approach, excels in accuracy, showcasing its potential for advanced healthcare applications.…”

Section: Comparison With Related Workmentioning

confidence: 99%

ECG-Based Cardiac Disease Detection in Time-Frequency Domain using Grid Search Optimized Wavelet Transforms

Diwakar,

Deepa

2024

Preprint

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Cardiovascular diseases (CVDs), including abnormal arrhythmias and congestive heart failure, are a leading cause of mortality worldwide, with electrocardiogram (ECG) signals serving as a critical diagnostic tool. This study introduces a novel approach for classifying diseases in ECG signals into three categories: normal sinus rhythm (NSR), abnormal arrhythmia (ARR), and congestive heart failure (CHF). The classification is based on a combination of features extracted from both the time domain (mean and standard deviation) and the frequency domain (power spectral density and spectral centroid) of the ECG signals. Additionally, energy values from selected frequency bands are utilized. To enhance the model's robustness, incorporate data augmentation techniques, including time-shifting and flipping of the signals. These augmented datasets are then employed with various classifiers, and an optimization process using grid search is applied to enhance the classification performance. This methodology presents a promising framework for automated ECG signal analysis, The results of the proposed work have been exceptionally promising, showcasing a remarkable specificity rate of 99.7% and achieving an accuracy level of 99.58%. These findings hold significant promise for advancing early detection methods and enhancing patient outcomes in the realm of CVDs.

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Section: Comparison With Related Workmentioning

confidence: 99%

ECG-Based Cardiac Disease Detection in Time-Frequency Domain using Grid Search Optimized Wavelet Transforms

Diwakar,

Deepa

2024

Preprint

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“…As according to study by Kumari et al [31] and Shams-Baboli and Ezoji [32], the training and validation or testing dataset was set at 70% and 30% respectively, with good accuracy result. Hence, the dataset was divided into three part 70% of the total data was used for training, 15% used for validation, and the remaining 15% used for testing.…”

Section: Training and Testingmentioning

confidence: 99%

Gait cycle prediction model based on gait kinematic using machine learning technique for assistive rehabilitation device

Izhar

Hussain

Maruzuki

et al. 2021

IJ-AI

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The gait cycle prediction model is critical for controlling assistive rehabilitation equipment like orthosis. The human gait model has recently used statistical models, but the dynamic properties of human physiology limit the current approach. Current human gait cycle prediction models need detailed kinematic and kinetic data of the human body as input parameters, and measuring them requires special instruments, making them difficult to use in real-world applications. In our study, three separate machine learning algorithms were used to create a human gait model: Gaussian process regression, support vector machine, and decision tree. The algorithm used to create the model's input parameters are height, weight, hip and knee angle, and ground reaction force (GRF). For better gait cycle model prediction, the models produced were enhanced by incorporating different sliding window data. The best gait period prediction model was DT with sliding window data (t−3), which had a root mean square error of 3.3018 and the R-squared (R-Value) of 0.97. The projection model focused on hip and knee angle and GRF was a feasible solution to controlling assistive rehabilitation devices during the gait cycle.

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“…Due to various factors like baseline wander, external noise, physical variations among individuals [5] the heart beat classification automatically using ECG has become a challenging task. In case of a healthy person also there may be difference in rhythm and morphology of heartbeats during various circumstances [6]. Few elements that are vigorous against such situations are used in classification of heartbeat like characteristic points relating to morphological features, abstract features and transformation features generated by seizure interpretation.…”

Section: Introductionmentioning

confidence: 99%

Detection of Cardiac Arrhythmia Using Multi-Perspective Convolutional Neutral Network for ECG Heartbeat Classification

Indira¹,

Lakshmi²,

Markapudi³

et al. 2022

RIA

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The leading death cause all over the world is heart disease. The presence of arrhythmias has to be examined to detect heart disease in early stage. The abnormality in heart beat rhythm is known as Arrhythmia. The speed of heart beat can be detected by Arrhythmia, it may be too slow, too fast or irregular patterns of heart beat are considered as Arrhythmia and there are various types of Arrhythmia. The electrocardiogram (ECG) produces signals, classification of such signals is very crucial for knowing the irregularity in the patterns of heart beat. As detection of arrhythmia is a challenging task, there is a great demand for an automatic detection technique to identify abnormal signals produced by heart which cannot be done manually. Therefore this paper provides a method for detection of Cardiac Arrhythmia using Multi-Perspective Convolutional Neutral Network (MPCNN) for ECG Heartbeat Classification. Basing on Physionet's MIT-BIH Arrhythmia Dataset the signals of ECG arrhythmia can be categorized into five classes. Number of layers, filters and size of the filter are appropriate parameters which are heuristically optimized for operating swiftly for operation of MPCNN effectively. Compared with the ultra-modern methods as Quantum Neural Networks and Deep Convolutional Neural Networks, the proposed method results in efficient performance having high level accuracy of 96.46%, 98.1% and 96.2% are the F1 scores for SVP and PVC respectively. The effective detection of heartbeat rhythm and irregularities can be identified using this model.

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Heart Rhythm Abnormality Detection and Classification using Machine Learning Technique

Cited by 14 publications

References 12 publications

ECG-Based Cardiac Disease Detection in Time-Frequency Domain using Grid Search Optimized Wavelet Transforms

ECG-Based Cardiac Disease Detection in Time-Frequency Domain using Grid Search Optimized Wavelet Transforms

Gait cycle prediction model based on gait kinematic using machine learning technique for assistive rehabilitation device

Detection of Cardiac Arrhythmia Using Multi-Perspective Convolutional Neutral Network for ECG Heartbeat Classification

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