Classification of Myocardial Infarction using MFCC and Ensemble Subspace KNN

Myocardial Infarction and Heart stroke are most prominent in the top ten deadly diseases worldwide. These diseases remained the prime reason for mortality in the last fifteen years. There is a need to develop a system capable enough to detect the disorders efficiently in a reliable manner. We have proposed an algorithm by using Phonocardiography (PCG) to classify different categories of Myocardial Infarction (MI). The database used in this study consists of self-collected PCGs acquired from various hospitals. Mel-frequency Cepstral Coefficients (MFCCs) are extracted directly from the acquired data of heart sound for key feature extraction and performed classification by Ensemble Subspace K Nearest Neighbor (KNN) method in MATLAB 2019a. Simulation results evidenced that the proposed algorithm achieved a mean accuracy of 94.9%. Further research can be done to design an embedded system of the proposed methodology to assist medical specialists in clinical diagnosis.

[1]  Sumair Aziz,et al.  Electricity Theft Detection using Empirical Mode Decomposition and K-Nearest Neighbors , 2020, 2020 International Conference on Emerging Trends in Smart Technologies (ICETST).

[2]  Tero Koivisto,et al.  A smartphone-only solution for detecting indications of acute myocardial infarction , 2017, 2017 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI).

[3]  Songbo Tan,et al.  An effective refinement strategy for KNN text classifier , 2006, Expert Syst. Appl..

[4]  Reza Tafreshi,et al.  Real-Time Detection of Myocardial Infarction by Evaluation of ST-Segment in Digital ECG , 2011 .

[5]  Shing-Chow Chan,et al.  Myocardial infarction detection and classification — A new multi-scale deep feature learning approach , 2016, 2016 IEEE International Conference on Digital Signal Processing (DSP).

[6]  U. Rajendra Acharya,et al.  Automated Diagnosis of Myocardial Infarction ECG Signals Using Sample Entropy in Flexible Analytic Wavelet Transform Framework , 2017, Entropy.

[7]  Samarendra Dandapat,et al.  Multiscale Energy and Eigenspace Approach to Detection and Localization of Myocardial Infarction , 2015, IEEE Transactions on Biomedical Engineering.

[8]  Muhammad Umar Khan,et al.  An Automated System towards Diagnosis of Pneumonia using Pulmonary Auscultations , 2019, 2019 13th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS).

[9]  Alan D. Lopez,et al.  Global, Regional, and National Burden of Cardiovascular Diseases for 10 Causes, 1990 to 2015 , 2017, Journal of the American College of Cardiology.

[10]  Muhammad Umar Khan,et al.  Detection of Subacute Intestinal Obstruction from Surface Electromyography Signatures , 2020, 2020 International Conference on Emerging Trends in Smart Technologies (ICETST).

[11]  C McRae,et al.  Myocardial infarction. , 2019, Australian family physician.

[12]  Madhuchhanda Mitra,et al.  Automated Identification of Myocardial Infarction Using Harmonic Phase Distribution Pattern of ECG Data , 2018, IEEE Transactions on Instrumentation and Measurement.

[13]  Muhammad Umar Khan,et al.  Electromyography (EMG) Data-Driven Load Classification using Empirical Mode Decomposition and Feature Analysis , 2019, 2019 International Conference on Frontiers of Information Technology (FIT).

[14]  Tin Kam Ho,et al.  The Random Subspace Method for Constructing Decision Forests , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  Sumair Aziz,et al.  Pattern Analysis Towards Human Verification using Photoplethysmograph Signals , 2020, 2020 International Conference on Emerging Trends in Smart Technologies (ICETST).

[16]  Sumair Aziz,et al.  Emotion Recognition System using Pulse Plethysmograph , 2020, 2020 International Conference on Emerging Trends in Smart Technologies (ICETST).

[17]  U. Rajendra Acharya,et al.  Automated characterization and classification of coronary artery disease and myocardial infarction by decomposition of ECG signals: A comparative study , 2017, Inf. Sci..

[18]  A. Ibrahim,et al.  Acute myocardial infarction. , 2014, Critical care clinics.

[19]  Samarendra Dandapat,et al.  Third-order tensor based analysis of multilead ECG for classification of myocardial infarction , 2017, Biomed. Signal Process. Control..

[20]  Muhammad Umar Khan,et al.  Automated Detection and Classification of Gastrointestinal Diseases using surface-EMG Signals , 2019, 2019 22nd International Multitopic Conference (INMIC).

[21]  U. Rajendra Acharya,et al.  Application of deep convolutional neural network for automated detection of myocardial infarction using ECG signals , 2017, Inf. Sci..

[22]  Ashutosh Kumar Singh,et al.  Global, regional, and national incidence, prevalence, and years lived with disability for 310 diseases and injuries, 1990–2015: a systematic analysis for the Global Burden of Disease Study 2015 , 2016, Lancet.

[23]  Mattias Ohlsson,et al.  Detecting acute myocardial infarction in the 12-lead ECG using Hermite expansions and neural networks , 2004, Artif. Intell. Medicine.

[24]  U. Rajendra Acharya,et al.  Classification of myocardial infarction with multi-lead ECG signals and deep CNN , 2019, Pattern Recognit. Lett..

[25]  Muhammad Umar Khan,et al.  System Design for Early Fault Diagnosis of Machines using Vibration Features , 2019, 2019 International Conference on Power Generation Systems and Renewable Energy Technologies (PGSRET).

[26]  Reza Boostani,et al.  A SYSTEM FOR ACCURATELY PREDICTING THE RISK OF MYOCARDIAL INFARCTION USING PCG, ECG AND CLINICAL FEATURES , 2017 .

[27]  Xuelong Li,et al.  Asymmetric bagging and random subspace for support vector machines-based relevance feedback in image retrieval , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.