Classification of Myocardial Infarction using MFCC and Ensemble Subspace KNN

Khan, Muhammad Umar; Mushtaq, Zohaib; Shakeel, M. Saad; Aziz, Sumair; Naqvi, Syed Zohaib Hassan

doi:10.1109/icecce49384.2020.9179345

Cited by 12 publications

(12 citation statements)

References 21 publications

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“…The study was performed within 5 s of each sample on a limited number of healthy people and patients suffering from this disease. 18 In his most recent research, Khan et al 19 studied different CAD samples alongside healthy samples in the same 5 s time period. The investigated sound signals were also collected with the aid of a modified stethoscope.…”

Section: Basic Concepts and Literature Reviewmentioning

confidence: 99%

“…However, in the method proposed by Thomae, the content of the input data has been changed. Using a PC via sound card, Khan et al 18 collected PCG of healthy and patient samples of myocardial infarction (MI). The study was performed within 5 s of each sample on a limited number of healthy people and patients suffering from this disease.…”

Section: Basic Concepts and Literature Reviewmentioning

confidence: 99%

“…In some cases, the content of the data has been changed, 17 or investigations have been conducted on a small number of people under the same sampling conditions. 18,19 Such issues and the use of various noise removal filters have caused previous methods to remain dependent on a specific dataset. Thus, by changing the dataset, the need for a new filtering process was felt.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Effective features extraction by analyzing heart sound for identifying cardiovascular diseases related to COVID-19: A diagnostic model

Sabouri

Ghadimi

Kiani-Sarkaleh

et al. 2022

Proc Inst Mech Eng H

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Incidence and exacerbation of some of the cardiovascular diseases in the presence of the coronavirus will lead to an increase in the mortality rate among patients. Therefore, early diagnosis of such diseases is critical, especially during the COVID-19 pandemic (mild COVID-19 infection). Thus, for diagnosing the heart diseases related to the COVID-19, an automatic, non-invasive, and inexpensive method based on the heart sound processing approach is proposed. In the present study, a set of features related to the nature of heart signals is defined and extracted. The investigated features included morphological and statistical features in the heart sound frequencies. By extracting and selecting a set of effective features related to the mentioned diseases, and avoiding to use different segmentation and filtering techniques, dependence on a limited dataset and specific sampling procedures has been eliminated. Different classifiers with various kernels are applied for diagnosis in data unbalanced and balanced conditions. The results showed 93.15% accuracy and 93.72% F1-score using 60 effective features in data balanced conditions. The identification system using the extracted features from Azad dataset is able to achieve the desired results in a generalized dataset. In this way, in the shortest possible sampling time, the present system provided an effective and generalizable method and a practical model for diagnosing important cardiovascular diseases in the presence of coronavirus in the COVID-19 pandemic.

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Section: Basic Concepts and Literature Reviewmentioning

confidence: 99%

Section: Basic Concepts and Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Effective features extraction by analyzing heart sound for identifying cardiovascular diseases related to COVID-19: A diagnostic model

Sabouri

Ghadimi

Kiani-Sarkaleh

et al. 2022

Proc Inst Mech Eng H

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“…This study used "the PASCAL Classifying Heart Sounds Challenge 2011" dataset [4,11], achieving the accuracy of 96% and 100% on stethoscope (Dataset A) and digiscope (Dataset B), respectively [11]. Khan et al [15], this study uses Mel frequency cepstral coefficients (MFCCs) as a key feature extraction method [15]. Xiao et al [22], this study proposes a novel 1D CNN architecture to classify heart sound using a publicly available PCG dataset.…”

Section: Related Workmentioning

confidence: 99%

“…Feature extraction is a process that extracts necessary features for simplifying the methods for heart sound classification [15]. For the feature extraction of the preprocessed data, the Mel frequency cepstrum coefficient (MFCC) is used.…”

Section: Feature Extractionmentioning

confidence: 99%

Comparative Study of Heart Sound Classifier with Varying Hyperparameter of CNN

Rai

Singh

2021

Advances in Intelligent Systems and Computing

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Cardiovascular is a serious but manageable disease if diagnosed early. So it would be better if there is a faster and reliable system for the diagnosis. This study proposes 16 Convolutional Neural Network (CNN) models with different hyperparameters for heart sound classification. The Mel frequency cepstral coefficients (MFCC) is a well-known method used for feature extraction of sound data, and this method is used in this study. The objective of this study is to analyze various CNN models with varying parameters like activation function, stride length, and layers used. The optimal model proposed in this study was able to obtain an F1 score of 91% on "the PASCAL classifying heart sound dataset."

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Enhanced Myocardial Infarction Identification in Phonocardiogram Signals Using Segmented Feature Extraction and Transfer Learning-Based Classification

Mandala,

Amini,

Adiwijaya

et al. 2023

IEEE Access

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Myocardial Infarction (MI), commonly known as a heart attack, is a type of cardiovascular disease characterized by the death of heart muscle cells. This condition occurs due to the blockage of blood vessels around the heart, inhibiting blood flow and causing an insufficient oxygen supply to the body. Typically, cardiovascular disease tests involve electrocardiogram (ECG) and photoplethysmogram (PPG) signals. In recent years, researchers have explored the application of Phonocardiogram (PCG) signals for cardiovascular detection due to their non-invasive, efficient, accessible, and cost-effective nature. While deep learning has been successful in object detection in digital images, its application to PCG signals for heart attack detection is rare. This study bridges this gap by introducing an enhanced technique called the Myocardial Infarction Detection System (MIDs). In contrast to previous deep learning research, this study employs a transfer learning algorithm as a classifier for MI feature datasets. Feature extraction is performed in segments to obtain more accurate MI features. Six feature extraction methods and transfer learning models based on Convolutional Neural Networks (CNN) using the VGG-16 architecture were selected as the primary components for MI identification. Additionally, this study compares these models with other CNN transfer learning models, such as VGG-19 and Xception, to assess their performance. Two experimental scenarios were conducted to evaluate MIDs performance in MI detection: experiments without hyperparameter tuning and with hyperparameter tuning. The results indicate that MIDs with CNN (VGG-16) after tuning exhibited the highest detection performance compared to other transfer learning CNN models, both with and without tuning. The accuracy, specificity, and sensitivity of MIDS detection with this configuration were 96.7%, 96.0%, and 97.4%, respectively. This research contributes to the development of an enhanced MI detection technique based on PCG signals using a transfer learning CNN.

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Classification of Myocardial Infarction using MFCC and Ensemble Subspace KNN

Cited by 12 publications

References 21 publications

Effective features extraction by analyzing heart sound for identifying cardiovascular diseases related to COVID-19: A diagnostic model

Effective features extraction by analyzing heart sound for identifying cardiovascular diseases related to COVID-19: A diagnostic model

Comparative Study of Heart Sound Classifier with Varying Hyperparameter of CNN

Enhanced Myocardial Infarction Identification in Phonocardiogram Signals Using Segmented Feature Extraction and Transfer Learning-Based Classification

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