Diagnosis of Cardiac Abnormalities Applying Scattering Transform and Fourier-Bessel Expansion on ECG Signals

Sawant, Nidhi Kalidas; Patidar, Shivnarayan

doi:10.23919/cinc53138.2021.9662751

Cited by 2 publications

(3 citation statements)

References 6 publications

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“…Table 2 tabulates the five-fold cross-validation scheme-based results for the proposed method using training data of the different ECG lead combinations. The obtained fivefold cross-validation results are compared to our previous work (Sawant and Patidar 2021). In the previous work, FB-based features were extracted and concatenated with scattering transform-based features to form a complete set of features.…”

Section: Offline Validation Resultsmentioning

confidence: 99%

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Application of Fourier-Bessel expansion and LSTM on multi-lead ECG for cardiac abnormalities identification

Sawant¹,

Patidar²

2022

Physiol. Meas.

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Objective: The availability of online electrocardiogram (ECG) repositories can aid researchers in developing automated cardiac abnormality diagnostic systems. Using such ECG repositories, this study aims to develop an algorithm that can assist physicians in diagnosing cardiac abnormalities. Approach: The PhysioNet/CinC 2021 Challenge has opened the venues for creating benchmark algorithms using standard and relatively diverse 12-lead ECG datasets. This work attempts to create a new machine learning approach for identifying common cardiac abnormalities using an ensemble-based classification with two models resulting from two different feature sets. The first feature set extracts RR variability based information by deploying Fourier-Bessel (FB) expansion. The second feature set is composed of time- and frequency-domains-based hand-crafted features. Two long short-term memory (LSTM)-based classifiers are trained using these two feature sets as input to categorize ECG signals. Predictions from these two models are fused to arrive at a final medical decision that improves the multi-label classification of the given ECG signals into twenty-six categories. \\ Main results: We participated in the George B. Moody Physionet Challenge 2021 as team 'Medics', and the proposed methodology was evaluated for all five lead combinations. The challenge scoring metrics obtained on the test data for twelve-, six-, four-, three-, and two-leads combinations are 0.360, 0.368, 0.376, 0.323, and 0.381, respectively. The proposed methodology was ranked 11th among all the follow-up entries of the Challenge. Significance: The obtained results of the proposed method justify the use of an ensemble classifier developed using the extracted feature sets for devising a diagnostic system for detecting and identifying common cardiac problems.

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Section: Offline Validation Resultsmentioning

confidence: 99%

“…This work is an extension of our previous work, which we have developed for submission as an open-source entry in Python to the 2021 PhysioNet Challenge (Sawant and Patidar 2021). Here, the proposed multi-label classification performance is compared with the same, along with other related studies.…”

Section: Discussionmentioning

confidence: 99%

Application of Fourier-Bessel expansion and LSTM on multi-lead ECG for cardiac abnormalities identification

Sawant¹,

Patidar²

2022

Physiol. Meas.

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“…In the work of Saadatnejad [ 11 ], the ECG feature and wavelet feature of ECG signal are extracted and then the features are fed into two LSTM models to classify the record. Sawant proposed a multilabel classification model based on gated recurrent unit (GRU) with time‐frequency features extracted by Fourier Bessel Expansion and scattering transform [ 12 ].…”

Section: Related Workmentioning

confidence: 99%

Automatic cardiac arrhythmias classification using CNN and attention‐based RNN network

Sun

2023

Healthcare Tech Letters

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Cardiac disease has become a severe threat to public health according to the government report. In China, there are 0.29 billion cardiac patients and early diagnosis will greatly reduce mortality and improve life quality. Electrocardiogram (ECG) signal is a priority tool in the diagnosis of heart diseases because it is non-invasive and easily available with a simple diagnostic tool of low cost. The paper proposes an automatic classification model by combing convolutional neural network (CNN) and recurrent neural network (RNN) to distinguish different types of cardiac arrhythmias. Morphology features of the raw ECG signals are extracted by CNN blocks and fed into a bidirectional gated recurrent unit (GRU) network. Attention mechanism is used to highlight specific features of the input sequence and contribute to the performance improvement of classification. The model is evaluated with two datasets considering the class imbalance problem constructed with records from MIT-BIH arrhythmia database and China Physiological Signal Challenge 2018 database. Experimental results show that this model achieves good performance with an average F1 score of 0.9110 on public dataset and 0.9082 on subject-specific dataset, which may have potential practical applications.

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Diagnosis of Cardiac Abnormalities Applying Scattering Transform and Fourier-Bessel Expansion on ECG Signals

Cited by 2 publications

References 6 publications

Application of Fourier-Bessel expansion and LSTM on multi-lead ECG for cardiac abnormalities identification

Application of Fourier-Bessel expansion and LSTM on multi-lead ECG for cardiac abnormalities identification

Automatic cardiac arrhythmias classification using CNN and attention‐based RNN network

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