Time–Frequency-Domain Deep Learning Framework for the Automated Detection of Heart Valve Disorders Using PCG Signals

Karhade, Jay; Dash, Shaswati; Ghosh, Samit Kumar; Dash, Dinesh Kumar; Tripathy, Rajesh Kumar

doi:10.1109/tim.2022.3163156

Cited by 56 publications

(18 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The occurred stimulation outcome displayed that the developed end-to-end framework attained effectively superior outcomes to conventional models. Researchers [ 4 ] have initiated the Time–Frequency-Domain (TFD) with the deep structure to perform automated identification of Heart Valve Disorders (HVDs) by utilizing PCG signals. The Deep CNN classifier was utilized to identify four HVD models with the help of TF images in the PCG signals by utilizing baseline transformation approaches.…”

Section: Literature Workmentioning

confidence: 99%

“…At the same time, it can use only limited data for the analysis. TFDDL [ 4 ] proved superior clarity and maintain simplicity at the time of system analysis. However, it required a transfer function to perform the simulation, and they lack in time and frequency resolution.…”

Section: Literature Workmentioning

confidence: 99%

“…These types of data are rarely utilized for attaining medical-related decisions [ 3 ]. Medical application holds an enormous amount of unused data with them and they produce high complexity to convert it into effective information and so, professional support is needed to attain an effective decision [ 4 ]. All these factors are subject to research in the field of medical image processing because only a limited number of professionals are available, which makes a lot of cases to be examined wrongly so, and therefore, an efficient automation model is highly essential [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Intelligent Bi-LSTM with Architecture Optimization for Heart Disease Prediction in WBAN through Optimal Channel Selection and Feature Selection

Nithiyanantham¹,

Sivaraman²

2023

Biomedicines

View full text Add to dashboard Cite

Wireless Body Area Network (WBAN) is a trending technology of Wireless Sensor Networks (WSN) to enhance the healthcare system. This system is developed to monitor individuals by observing their physical signals to offer physical activity status as a wearable low-cost system that is considered an unremarkable solution for continuous monitoring of cardiovascular health. Various studies have discussed the uses of WBAN in Personal Health Monitoring systems (PHM) based on real-world health monitoring models. The major goal of WBAN is to offer early and fast analysis of the individuals but it is not able to attain its potential by utilizing conventional expert systems and data mining. Multiple kinds of research are performed in WBAN based on routing, security, energy efficiency, etc. This paper suggests a new heart disease prediction under WBAN. Initially, the standard patient data regarding heart diseases are gathered from benchmark datasets using WBAN. Then, the channel selections for data transmission are carried out through the Improved Dingo Optimizer (IDOX) algorithm using a multi-objective function. Through the selected channel, the data are transmitted for the deep feature extraction process using One Dimensional-Convolutional Neural Networks (ID-CNN) and Autoencoder. Then, the optimal feature selections are done through the IDOX algorithm for getting more suitable features. Finally, the IDOX-based heart disease prediction is done by Modified Bidirectional Long Short-Term Memory (M-BiLSTM), where the hyperparameters of BiLSTM are tuned using the IDOX algorithm. Thus, the empirical outcomes of the given offered method show that it accurately categorizes a patient’s health status founded on abnormal vital signs that is useful for providing the proper medical care to the patients.

show abstract

Section: Literature Workmentioning

confidence: 99%

Section: Literature Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Intelligent Bi-LSTM with Architecture Optimization for Heart Disease Prediction in WBAN through Optimal Channel Selection and Feature Selection

Nithiyanantham¹,

Sivaraman²

2023

Biomedicines

View full text Add to dashboard Cite

show abstract

“…The segments were divided evenly into five categories: normal, aortic stenosis, MR, mitral stenosis and mitral valve prolapse. Several works were carried out in handling this five-category classification task 23–29. Time-frequency magnitude and phase features were used,23 and graph-based feature was developed,24 with traditional machine learning methods used for classification.…”

Section: Introductionmentioning

confidence: 99%

Developing an AI-assisted digital auscultation tool for automatic assessment of the severity of mitral regurgitation: protocol for a cross-sectional, non-interventional study

Zhang,

Cheng,

et al. 2024

BMJ Open

View full text Add to dashboard Cite

IntroductionMitral regurgitation (MR) is the most common valvular heart disorder, with a morbidity rate of 2.5%. While echocardiography is commonly used in assessing MR, it has many limitations, especially for large-scale MR screening. Cardiac auscultation with electronic stethoscope and artificial intelligence (AI) can be a fast and economical modality for assessing MR severity. Our objectives are (1) to establish a deep neural network (DNN)-based cardiac auscultation method for assessing the severity of MR; and (2) to quantitatively measure the performance of the developed AI-based MR assessment method by virtual clinical trial.Methods and analysisIn a cross-sectional design, phonocardiogram will be recorded at the mitral valve auscultation area of outpatients. The enrolled patients will be checked by echocardiography to confirm the diagnosis of MR or no MR. Echocardiographic parameters will be used as gold standard to assess the severity of MR, classified into four levels: none, mild, moderate and severe. The study consists of two stages. First, an MR-related cardiac sound database will be created on which a DNN-based MR severity classifier will be trained. The automatic MR severity classifier will be integrated with the Smartho-D2 electronic stethoscope. Second, the performance of the developed smart device will be assessed in an independent clinical validation data set. Sensitivity, specificity, precision, accuracy and F1 score of the developed smart MR assessment device will be evaluated. Agreement on the performance of the smart device between cardiologist users and patient users will be inspected. The interpretability of the developed model will also be studied with statistical comparisons of occlusion map-guided variables among the four severity groups.Ethics and disseminationThe study protocol was approved by the Medical Ethics Committee of Huzhou Central Hospital, China (registration number: 202302009-01). Informed consent is required from all participants. Dissemination will be through conference presentations and peer-reviewed journals.Trial registration numberChiCTR2300069496.

show abstract

“…In such methods, the effectiveness of heart sound segmentation predominantly affects the accuracy of classification results. With the development of deep learning and the improvement of hardware environment, the processing of heart sound signals using deep learning methods has become the focus of research [3].…”

Section: Introductionmentioning

confidence: 99%

Murmur Detection and Clinical Outcome Classification Using a VGG-like Network and Combined Time-Frequency Representations of PCG Signals

Bai¹,

Yan²,

Chen³

et al. 2022

Computing in Cardiology Conference (CinC)

View full text Add to dashboard Cite

For the George B. Moody PhysioNet Challenge 2022, our team, PhysioDreamfly, developed a deep neural network approach for detecting murmurs and identifying abnormal clinical outcomes from phonocardiograms (PCGs). In our approach, a VGG-like CNN model is used as the classifier. Images consisting of Log-Mel spectrograms and wavelet scalogram that transformed from unsegmented PCGs are used as model inputs. We combined the murmur and outcome labels to address the two tasks as one multi-label task, and introduced a weighted focal loss function to optimize the model. Our murmur detection classifier received a weighted accuracy score of 0.752 (ranked 11th out of 40 teams) and Challenge cost score of 12831(ranked 18th out of 39 teams) on the hidden test set.

show abstract

Time–Frequency-Domain Deep Learning Framework for the Automated Detection of Heart Valve Disorders Using PCG Signals

Cited by 56 publications

References 46 publications

Intelligent Bi-LSTM with Architecture Optimization for Heart Disease Prediction in WBAN through Optimal Channel Selection and Feature Selection

Intelligent Bi-LSTM with Architecture Optimization for Heart Disease Prediction in WBAN through Optimal Channel Selection and Feature Selection

Developing an AI-assisted digital auscultation tool for automatic assessment of the severity of mitral regurgitation: protocol for a cross-sectional, non-interventional study

Murmur Detection and Clinical Outcome Classification Using a VGG-like Network and Combined Time-Frequency Representations of PCG Signals

Contact Info

Product

Resources

About