An Improved Deep Learning Model for Automated Detection of BBB Using S-T Spectrograms of Smoothed VCG Signal

Gupta, Kapil; Bajaj, Varun; Ansari, Irshad Ahmad

doi:10.1109/jsen.2022.3162022

Cited by 20 publications

(6 citation statements)

References 32 publications

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“…Bashar et al (2020) and Bashar, Han, et al (2021) combined features from P‐wave characteristics and ECG R‐R interval variability to detect AF. García‐Isla et al (2021) and Gupta et al (2023), employed local mean decomposition to decompose ECG data and extract entropy‐based features, then they used an ensemble boosted trees classifier for AF detection. Hartikainen et al (2019) used ECG data from Holter recordings using entropy measures to detect AF.…”

Section: Resultsmentioning

confidence: 99%

Artificial intelligence for atrial fibrillation detection, prediction, and treatment: A systematic review of the last decade (2013–2023)

Salvi,

Acharya,

Seoni

et al. 2024

WIREs Data Min & Knowl

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Atrial fibrillation (AF) affects more than 30 million individuals worldwide, making it the most prevalent cardiac arrhythmia on a global scale. This systematic review summarizes recent advancements in applying artificial intelligence (AI) techniques for AF detection, prediction, and guiding treatment selection and risk stratification. In adherence with the PRISMA guidelines (Preferred Reporting Items for Systematic Reviews and Meta‐Analyses), a total of 171 pertinent studies conducted between 2013 and 2023 were examined. Studies applying machine learning (ML) and deep learning (DL) to electrocardiogram (ECG), photoplethysmography (PPG), wearable data, and other sources were evaluated. For AF detection, most works employed DL (48 studies) and ML (28 studies) on ECG data. DL methods directly analyzed ECG waveforms and outperformed approaches relying on hand‐crafted features. For prediction and risk stratification, 22 studies used ML while 7 leveraged DL on ECG. An emerging trend showed the growing potential of PPG for AF screening. Overall, AI demonstrated promising capabilities across various AF‐related tasks. However, real‐world implementation faces challenges including a lack of interpretability, the need for multimodal data integration, prospective performance validation, and regulatory compliance. Future research directions involve quantifying model uncertainty, enhancing transparency, and conducting population‐based clinical trials to facilitate translation into practice.This article is categorized under: Application Areas > Health Care Application Areas > Science and Technology Technologies > Artificial Intelligence

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Section: Resultsmentioning

confidence: 99%

Artificial intelligence for atrial fibrillation detection, prediction, and treatment: A systematic review of the last decade (2013–2023)

Salvi,

Acharya,

Seoni

et al. 2024

WIREs Data Min & Knowl

View full text Add to dashboard Cite

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“…For example, Gupta et al. [19] proposed a novel DLM for the screening of bundle branch blocks using vectorcardiogram signals. In [20] , authors proposed a new deep learning framework namely Hyp-Net for the automated detection of hypertension using time-frequency images of 1-D ballistocardiogram signals.…”

Section: Previous Workmentioning

confidence: 99%

Deep learning models-based CT-scan image classification for automated screening of COVID-19

Gupta

Bajaj²

2023

Biomedical Signal Processing and Control

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“…Deep learning, classical machine learning algorithms, and signal processing techniques can be used for data quality enhancement and predictions, which are codependent due to the important role of data quality on algorithm predictions. In the literature, different algorithms and methodologies for data quality evaluation using artifact and anomaly detection are available (Megjhani et al,2019;Feng, Loy, Zhang, and Guan, 2011;Edinburgh, Smielewski, Czosnyka, Eglen, and Ercole, 2019;Subramanian et al, 2021;Gupta et al, 2022). Also, from the prediction application standpoint, different neural network applications can be found due to their capacity to recognize patterns and automatically extract features (Roy et al 2020;Bar et al 2015;Hussein, et al 2019).…”

Section: Literature Reviewmentioning

confidence: 99%

“…The results show that KNN and 1 class SVM are the best options with the shortest contiguous length of artifact of 14.5039 seconds for both algorithms. Gupta et al (2022) presented an artifact detector method based on the Savitzky-Golay filter (S-GF) with wavelet-based noise remover (WBNR). The artifact detector significantly increases the accuracy of automated detection of Bundle Branch Block (BBB), a cardiovascular complication, to 98.80%.…”

Section: Literature Reviewmentioning

confidence: 99%

A Novel Technique for Data Quality Improvement in Human Health PHM

Taco¹,

Foreman²,

Kundu³

et al. 2022

PHM_CONF

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Human healthcare data cover different signals related to the functioning of the human body such as blood pressure, blood glucose, heart rate, among others. This kind of data plays an important role on ill patients in the intensive care unit. Unfortunately, the recorded data may include connection & human errors, measurement errors due to the movement of patients, among others. These errors are better known as Artifacts and they should be removed in case the data needs to be used for clinical purposes. Different methods have been proposed for artifact detection; however, the existing methods only analyze one signal at a time or rely on feature engineering. In this study, we present an alternative solution for artifact detection that integrates signals such as Intracranial Pressure (ICP), Electrocardiogram (ECG), and Arterial Blood Pressure (ABP). Time raw domain data, Fourier transform, and the combination of both were studied as the input of neural network models. Four deep learning algorithms were employed: convolutional neural network (CNN), long short-term memory (LSTM), bidirectional LSTM (BiLSTM), and transformer neural network. By performing a cross-validation ensemble using a dataset of 39 patients with noisy signals, the Fourier transform and CNN (FT-CNN) model outperformed the other deep learning models in terms of accuracy and computational time. This study shows that deep learning, Fourier transform, and cross-validation ensemble combined have a great potential in data quality improvement in healthcare.

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An Improved Deep Learning Model for Automated Detection of BBB Using S-T Spectrograms of Smoothed VCG Signal

Cited by 20 publications

References 32 publications

Artificial intelligence for atrial fibrillation detection, prediction, and treatment: A systematic review of the last decade (2013–2023)

Artificial intelligence for atrial fibrillation detection, prediction, and treatment: A systematic review of the last decade (2013–2023)

Deep learning models-based CT-scan image classification for automated screening of COVID-19

A Novel Technique for Data Quality Improvement in Human Health PHM

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