Neural Networks for Detecting Cardiac Arrhythmia from PCG Signals

Athreya, Avanti; Paramesha, K.; Avani, H. S.; Pooja, .; Sudan, Madhu

doi:10.1007/978-981-16-7771-7_9

Cited by 2 publications

(1 citation statement)

References 10 publications

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“…Machine Learning based phonocardiography systems models used in real-time to analyze Phonocardiography (PCG) recordings and provide diagnostic information, which can be useful in critical care settings. Overall, the combination of machine learning and PCG data can provide more accurate and objective results and helps to improve the diagnosis and monitoring of heart conditions [10]. The generic ML-based phonocardiography systems are depicted in Fig.…”

Section: An Overview Of Phonocardiographymentioning

confidence: 99%

Systematic Review for Phonocardiography Classification Based on Machine Learning

Altaf,

Mahdin,

Alive

et al. 2023

IJACSA

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Phonocardiography, the recording and analysis of heart sounds, has become an essential tool in diagnosing cardiovascular diseases (CVDs). In recent years, machine learning and deep learning techniques have dramatically improved the automation of phonocardiogram classification, making it possible to delve deeper into intricate patterns that were previously difficult to discern. Deep learning, in particular, leverages layered neural networks to process data in complex ways, mimicking how the human brain works. This has contributed to more accurate and efficient diagnoses. This systematic review aims to examine the existing literature on phonocardiography classification based on machine learning, focusing on algorithms, datasets, feature extraction methods, and classification models utilized. The materials and methods used in the study involve a comprehensive search of relevant literature and a critical evaluation of the selected studies. The review also discusses the challenges encountered in this field, especially when incorporating deep learning techniques, and suggests future research directions. Key findings indicate the potential of machine and deep learning in enhancing the accuracy of phonocardiography classification, thereby improving cardiovascular disease diagnosis and patient care. The study concludes by summarizing the overall implications and recommendations for further advancements in this area.

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Section: An Overview Of Phonocardiographymentioning

confidence: 99%

Systematic Review for Phonocardiography Classification Based on Machine Learning

Altaf,

Mahdin,

Alive

et al. 2023

IJACSA

View full text Add to dashboard Cite

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Noise Removal-based Thresholding framework for Arrhythmia classification

Prajitha

Sridhar

Baskar

2023

IFS

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Electrocardiogram (ECG) signal analyses can enhance human life in various ways, from detecting and treating heart illness to controlling the lives of cardiac-diseased people. ECG analysis has become crucial in medical studies for accurately detecting cardiovascular diseases (CVDs). Cardiac Arrhythmia is one of the major life-threatening diseases. Analyzing ECG signals is the easiest way to detect Arrhythmia. Different noises often corrupt the ECG signals, like power line interference, electromyographic (EMG) noise, and electrode motion artifact noise. Such noises make it difficult to identify the various peaks in the ECG signal for arrhythmia classification. To overcome such problems, Noise Removal-based Thresholding (NRT) framework has been introduced to remove noises from ECG signals and accurately classify Arrhythmia. Discrete Wavelet transform reduces noise from ECG signals in the pre-processing stage. The noise-removed signal is segmented by K-means clustering for R-peak detection by finding all local maximum points from the signal. The signal features are extracted by Burg’s method to obtain good frequency resolution and quick integration for short-time signals in the form of a cumulative distribution function. All features collected from R-peak are fed to the Iterative Convolutional Neural Network (ICNN) and classified the arrhythmia types based on the alignment of a few variables to work well with the Euclidean distance metric. The NRT framework is evaluated based on the data obtained from the MIT-BIH Arrhythmia dataset and achieves the Accuracy of 99.45 %, Positive Prediction of 98.92%, F1-Score of 98.95%, SNR of 35 dB, MSE of 0.001, RMSE of 0.002

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Neural Networks for Detecting Cardiac Arrhythmia from PCG Signals

Cited by 2 publications

References 10 publications

Systematic Review for Phonocardiography Classification Based on Machine Learning

Systematic Review for Phonocardiography Classification Based on Machine Learning

Noise Removal-based Thresholding framework for Arrhythmia classification

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