PCG Classification Using a Neural Network Approach

Grzegorczyk, Iga; Soliński, Mateusz; Łepek, Michał; Perka, Anna; Rosinski, Jacek; Rymko, Joanna; Stępień, Katarzyna; Gieraltowski, Jan

doi:10.22489/cinc.2016.323-252

Cited by 40 publications

(27 citation statements)

References 5 publications

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“…Recent applications implemented a convolutional neural network (CNN) and multilayer perceptron (MLP) for fetal heart rate records assessment and reached 85% accuracy [48]; a recurrent neural network (RNN) was also suggested for automatic detection of irregular beating rhythm in records with 83% accuracy [49]. A long-short term memory (LSTM) network was used for atrial fibrillation classification from diverse electrocardiographic signals and reached 78% accuracy in [50], and 79% F1 score in [51]. A pediatric heart disease screening application was also solved using a CNN model for the task of automatic structural heart abnormality risk detection from digital phonocardiogram (PCG) signals [52].…”

Section: Related Workmentioning

confidence: 99%

Ensemble Deep Learning Models for Heart Disease Classification: A Case Study from Mexico

et al. 2020

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Heart diseases are highly ranked among the leading causes of mortality in the world. They have various types including vascular, ischemic, and hypertensive heart disease. A large number of medical features are reported for patients in the Electronic Health Records (EHR) that allow physicians to diagnose and monitor heart disease. We collected a dataset from Medica Norte Hospital in Mexico that includes 800 records and 141 indicators such as age, weight, glucose, blood pressure rate, and clinical symptoms. Distribution of the collected records is very unbalanced on the different types of heart disease, where 17% of records have hypertensive heart disease, 16% of records have ischemic heart disease, 7% of records have mixed heart disease, and 8% of records have valvular heart disease. Herein, we propose an ensemble-learning framework of different neural network models, and a method of aggregating random under-sampling. To improve the performance of the classification algorithms, we implement a data preprocessing step with features selection. Experiments were conducted with unidirectional and bidirectional neural network models and results showed that an ensemble classifier with a BiLSTM or BiGRU model with a CNN model had the best classification performance with accuracy and F1-score between 91% and 96% for the different types of heart disease. These results are competitive and promising for heart disease dataset. We showed that ensemble-learning framework based on deep models could overcome the problem of classifying an unbalanced heart disease dataset. Our proposed framework can lead to highly accurate models that are adapted for clinical real data and diagnosis use.

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Section: Related Workmentioning

confidence: 99%

Ensemble Deep Learning Models for Heart Disease Classification: A Case Study from Mexico

et al. 2020

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“…less than 1000 samples) such as references [8,30,33,38], as seen in Table V. However, some techniques in Table V employed more than 3000 HS signals [14,31,[34][35][36][37]; they had attempted some of these difficult signals, but they needed to explore high number of input parameters in the range of 13-124 different signal attributes. Thus, they reported better reliable performance than ANFIS, but this would not affect the suggestive capability of ANFIS to classify signals after training, particularly if the number of features was increased (e.g.…”

Section: Figure 6 the Anfis's Outputs On 78-test Samples For Class Amentioning

confidence: 99%

A Framework Classification of Heart Sound Signals in PhysioNet Challenge 2016 Using High Order Statistics and Adaptive Neuro-Fuzzy Inference System

et al. 2020

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“…The signal quality assessment block [4] ensures that these files should be deleted. Now the data set is only with good quality audio duo files Which is potentially fit for classification.…”

Section: Feature Extraction and Classificationmentioning

confidence: 99%

Heart Anomaly Detection using Deep Learning Approach Supported Signal Analysis

Dhabale¹

2020

IJERT

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Phonocardiography is an effective technique Which helps for the recording of heart sounds during the heart cycle Identification and further diagnosis of human related diseases Heart. Contraction of the heart muscles and closure of the heart valve the heart produces sound, which can be analysed by the experienced Cardiologist. The aim of this study is to create an automated classification method using a one-dimensional solution Neural network based on phonocardiogram data for discrepancies in the heart sound. The proposed system consists of three phases. 1) Data Acquisition 2) Pre-Process 2) 3) Feature Extraction and Classification. We have proposed an intelligent neural network approach for the classification of PCG data. Heart sound recording (PCG data) Which is nothing but a audio file converted in its time Domain representation. This is provided as converted PCG data input for the Neural Network. Was also emphasized on Noisy heart sound recording. It Can reduce noise efficiency Classification because it disrupts the values of the neural network. The accuracy of the proposed system is 91.5% with sensitivity of 0.92 and specificity 0.91.

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PCG Classification Using a Neural Network Approach

Abstract: Abstract

Cited by 40 publications

References 5 publications

Ensemble Deep Learning Models for Heart Disease Classification: A Case Study from Mexico

Ensemble Deep Learning Models for Heart Disease Classification: A Case Study from Mexico

A Framework Classification of Heart Sound Signals in PhysioNet Challenge 2016 Using High Order Statistics and Adaptive Neuro-Fuzzy Inference System

Heart Anomaly Detection using Deep Learning Approach Supported Signal Analysis

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