Methods for classification of phonocardiogram

Muruganantham, J.; Amarnath, R.; Jawahar, Kaanthan; KalyanaSundaram, C.

doi:10.1109/tencon.2003.1273172

Cited by 14 publications

(3 citation statements)

References 5 publications

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“…A deep network reacts to the inputs provided, does difficult computations on them, and eventually generates output. Besides this, backpropagation is the working algorithm in training these deep learning models [23,30]. The skeleton structure of the convolutional neural network based deep learning network is provided in Fig.…”

Section: Proposed Cnn-based Squeeze Networkmentioning

confidence: 99%

Conv-Random Forest-Based IoT: A Deep Learning Model Based on CNN and Random Forest for Classification and Analysis of Valvular Heart Diseases

Sinha Roy,

Roy,

Mandal

2023

IEEE Open J. Instrum. Meas.

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Cardiovascular diseases are growing rapidly in this world. Around 70 % of the world's population is suffering from the same. The entire research work is grouped into classification and analysis of heart sound. We defined a new Squeeze network based deep learning model -Convolutional Random Forest for real time valvular heart sound classification and analysis using industrial raspberry pi 4B. The proposed electronic stethoscope is internet enabled using ESP32, and raspberry pi. The said IOT based model is also low cost, portable, and can be reachable to distant remote places where doctors are not available. As far as the classification part is concerned, the multiclass classification is done for seven types of valvular heart sound. The random forest classifier scored a good accuracy among other ensemble methods in small training set data. The CNN-based Squeeze Net model achieved a decent accuracy of 98.65 % after its hyper parameters were optimized for heart sound analysis. The proposed IOT based model overcomes the drawbacks faced individually in both squeeze network and random forest. CNN-based Squeeze net model and Random Forest classifier combined together improved the performance of classification accuracy. The squeeze net model plays a pivotal part in feature extraction of heart sound, and a Random forest classifier acts as a classifier in the class prediction layer for predicting class labels. Experimental results on several datasets like the Kaggle dataset, Physio net challenge, and Pascal Challenge showed that the Conv-RF model works the best. The proposed IOT based Conv-RF model is also applied on selected subjects with different age groups and genders having history of heart diseases. The Conv-Random Forest method scored an accuracy of 99.37+ 0.05% on the different test datasets with a sensitivity of 99.5 + 0.12% and specificity of 98.9 + 0.03%. The proposed model is also examined with the current state of art models in terms of accuracy.

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Section: Proposed Cnn-based Squeeze Networkmentioning

confidence: 99%

Conv-Random Forest-Based IoT: A Deep Learning Model Based on CNN and Random Forest for Classification and Analysis of Valvular Heart Diseases

Sinha Roy,

Roy,

Mandal

2023

IEEE Open J. Instrum. Meas.

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“…All inception network parameters and hyperparameters are adjusted during the training phase of the deep learning model. A Python-based Keras sequential model [42,43] has been taken for implementation. The entire design of the deep learning model is shown in Fig.…”

Section: Proposed Cnn-based Inception Networkmentioning

confidence: 99%

Design of ear-contactless stethoscope and improvement in the performance of deep learning based on CNN to classify the heart sound

Roy

Roy²,

Mandal

2023

Med Biol Eng Comput

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Cardiac-related disorders are rapidly growing throughout the world. Accurate classification of cardiovascular diseases is an important research topic in healthcare. During COVID-19, auscultating heart sounds was challenging as health workers and doctors wear protective clothing, and direct contact with patients can spread the outbreak. Thus, contactless auscultation of heart sound is necessary. In this paper, a low-cost ear contactless stethoscope is designed where auscultation is done with the help of a bluetooth-enabled micro speaker instead of an earpiece. The PCG recordings are further compared with other standard electronic stethoscopes like Littman 3 M. This work is made to improve the performance of deep learning-based classifiers like recurrent neural networks (RNN) and convolutional neural networks (CNN) for different valvular heart problems using tuning of hyperparameters like learning rate of optimizers, dropout rate, and hidden layer. Hyper-parameter tuning is used to optimize the performances of various deep learning models and their learning curves for real-time analysis. The acoustic, time, and frequency domain features are used in this research. The investigation is made on the heart sounds of normal and diseased patients available from the standard data repository to train the software models. The proposed CNN-based inception network model achieved an accuracy of 99.65 ± 0.06% on the test dataset with a sensitivity of 98.8 ± 0.05% and specificity of 98.2 ± 0.19%. The proposed hybrid CNN-RNN architecture attained 91.17 ± 0.03% accuracy on test data after hyperparameter optimization, whereas the LSTM-based RNN model achieved 82.32 ± 0.11% accuracy. Finally, the evaluated results were compared with machine learning algorithms, and the improved CNN-based Inception Net model is the most effective among others.

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“…Figure25compares the deep-learning-based classification[39,50] method used for valvular Heart Sound analysis with the traditional machine-learning methods. It highlights low-level features used for deep…”

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confidence: 99%

Recent Advances in PCG Signal Analysis using AI: A Review

Roy,

Mandal

et al. 2024

International Journal on Smart Sensing and Intelligent Systems

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The paper reviews the milestones and various modern-day approaches in developing phonocardiogram (PCG) signal analysis. It also explains the different phases and methods of the Heart Sound signal analysis. Many physicians depend heavily on ECG experts, inviting healthcare costs and ignorance of stethoscope skills. Hence, auscultation is not a simple solution for the detection of valvular heart disease; therefore, doctors prefer clinical evaluation using Doppler Echo-cardiogram and another pathological test. However, the benefits of auscultation and other clinical evaluation can be associated with computer-aided diagnosis methods that can help considerably in measuring and analyzing various Heart Sounds. This review covers the most recent research for segmenting valvular Heart Sound during preprocessing stages, like adaptive fuzzy system, Shannon energy, time-frequency representation, and discrete wavelet distribution for analyzing and diagnosing various heart-related diseases. Different Convolutional Neural Network (CNN) based deep-learning models are discussed for valvular Heart Sound analysis, like LeNet-5, AlexNet, VGG16, VGG19, DenseNet121, Inception Net, Residual Net, Google Net, Mobile Net, Squeeze Net, and Xception Net. Among all deep-learning methods, the Xception Net claimed the highest accuracy of 99.43 + 0.03% and sensitivity of 98.58 + 0.06%. The review also provides the recent advances in the feature extraction and classification techniques of Cardiac Sound, which helps researchers and readers to a great extent.

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Methods for classification of phonocardiogram

Cited by 14 publications

References 5 publications

Conv-Random Forest-Based IoT: A Deep Learning Model Based on CNN and Random Forest for Classification and Analysis of Valvular Heart Diseases

Conv-Random Forest-Based IoT: A Deep Learning Model Based on CNN and Random Forest for Classification and Analysis of Valvular Heart Diseases

Design of ear-contactless stethoscope and improvement in the performance of deep learning based on CNN to classify the heart sound

Recent Advances in PCG Signal Analysis using AI: A Review

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