Heart Diseases Diagnosis Using Intelligent Algorithm Based on PCG Signal Analysis

Nabih-Ali, Mohammed; El‐Dahshan, El‐Sayed A.; Yahia, Ashraf

doi:10.4236/cs.2017.87012

Cited by 25 publications

(18 citation statements)

References 10 publications

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“…Feature extraction is a process of deriving a compact and useful representation of the information from the signal [13], [14]. The heart sound signals in the database are redundant in nature.…”

Section: Feature Extractionmentioning

confidence: 99%

Time-Frequency Analysis, Denoising, Compression, Segmentation, and Classification of PCG Signals

Chowdhury

Poudel

2020

IEEE Access

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Phonocardigraphy (PCG) is the graphical representation of heart sounds. The PCG signal contains useful information about the functionality and the condition of the heart. It also provides an early indication of potential cardiac abnormalities. Extracting cardiac information from heart sounds and detecting abnormal heart sounds to diagnose heart diseases using the PCG signal can play a vital role in remote patient monitoring. In this paper, we have combined different signal processing techniques and a deep learning method to denoise, compress, segment, and classify PCG signals effectively and accurately. First, the PCG signal is denoised and compressed by using a multi-resolution analysis based on the Discrete Wavelet Transform (DWT). Then, a segmentation algorithm, based on the Shannon energy envelope and zerocrossing, is applied to segment the PCG signal into four major parts: the first heart sound (S1), the systole interval, the second heart sound (S2), and the diastole interval. Finally, Mel-scaled power spectrogram and Mel-frequency cepstral coefficients (MFCC) are employed to extract informative features from the PCG signal, which are then fed into a classifier to classify each PCG signal into a normal or an abnormal signal by using a deep learning approach. For the classification, a 5-layer feed-forward Deep Neural Network (DNN) model is used, and overall testing accuracy of around 97.10% is achieved. Besides providing valuable information regarding heart condition, this signal processing approach can help cardiologists take appropriate and reliable steps toward diagnosis if any cardiovascular disorder is found in the initial stage.

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Section: Feature Extractionmentioning

confidence: 99%

Time-Frequency Analysis, Denoising, Compression, Segmentation, and Classification of PCG Signals

Chowdhury

Poudel

2020

IEEE Access

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“…Some of them tried to develop an automatic diagnosis based on the techniques of medical imaging, echocardiography, or magnetic resonance imaging (MRI). Other researches were based on the analysis of electrocardiogram signals (ECG) [3,4], while some others chose phonocardiogram signals (PCG) [5,6]. The starting information and methods are different, but the goal remains the samethe development of an automatic diagnostic tool.…”

Section: Introductionmentioning

confidence: 99%

Classification of Cardiovascular disease using dysphonia measurement in speech

Bourouhou¹,

Jilbab²,

Nacir³

et al. 2021

Diagnostyka

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Cardiovascular disease is the leading cause of death worldwide. The diagnosis is made by non-invasive methods, but it is far from being comfortable, rapid, and accessible to everyone.Speech analysis is an emerging non-invasive diagnostic tool, and a lot of researches have shown that it is efficient in speech recognition and in detecting Parkinson's disease, so can it be effective for differentiating between patients with cardiovascular disease and healthy people?This present work answers the question posed, by collecting a database of 75 people, 35 of whom suffering from cardiovascular diseases, and 40 are healthy. We took from each one three vocal recordings of sustained vowels (aaaaa…, ooooo… .. and iiiiiiii… ..). By measuring dysphonia in speech, we were able to extract 26 features, with which we will train three types of classifiers: the k-near-neighbor, the support vectors machine classifier, and the naive Bayes classifier.The methods were tested for accuracy and stability, and we obtained 81% accuracy as the best result using the k-near-neighbor classifier.

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“…During its work, the human heart emits sounds and vibrations that reflect its operating state, from which the auscultation comes as an important diagnostic tool since the dawn of time [3] [4]. The use of auscultation techniques invented by Laennec for analysis of heart sounds still insufficient, because the human ear isn't sensitive enough to fast signals with a low power.…”

Section: Introductionmentioning

confidence: 99%

“…The use of auscultation techniques invented by Laennec for analysis of heart sounds still insufficient, because the human ear isn't sensitive enough to fast signals with a low power. However, the skills that will allow the doctor to differentiate between a pathological heart and a normal heart from heart sounds, take a long time to be acquired, and leave a significant margin of error more or less important according to the subjectivity of the analyst [5] [6].…”

Section: Introductionmentioning

confidence: 99%

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Heart Sounds Classification for a Medical Diagnostic Assistance

et al. 2019

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<span lang="EN-US">In order to develop the assessment of phonocardiogram “PCG” signal for discrimination between two of people classes – individuals with heart disease and healthy one- we have adopted the database provided by "The PhysioNet/Computing in Cardilogy Challenge 2016", which contains records of heart sounds 'PCG '. This database is chosen in order to compare and validate our results with those already published. We subsequently extracted 20 features from each provided record. For classification, we used the Generalized Linear Model (GLM), and the Support Vector Machines (SVMs) with its different types of kernels (i.e.; Linear, polynomial and MLP). The best classification accuracy obtained was 88.25%, using the SVM classifier with an MLP kernel.</span>

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Heart Diseases Diagnosis Using Intelligent Algorithm Based on PCG Signal Analysis

Cited by 25 publications

References 10 publications

Time-Frequency Analysis, Denoising, Compression, Segmentation, and Classification of PCG Signals

Time-Frequency Analysis, Denoising, Compression, Segmentation, and Classification of PCG Signals

Classification of Cardiovascular disease using dysphonia measurement in speech

Heart Sounds Classification for a Medical Diagnostic Assistance

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