Heart sound classification using wavelet scattering transform and support vector machine

被引:0
|
作者
Shervegar, Vishwanath Madhava [1 ]
机构
[1] Visvesvaraya Technol Univ, Moodlakatte Inst Technol Kundapura, Dept Elect & Commun Engn, Mudalkatte, Karnataka, India
关键词
Phonocardiogram (PCG); Support Vector Machine (SVM); Coronary Artery Disease (CAD); Low-Pass Butterworth Filter (LPBF); WS Transform (WST); Feature Extraction (FE); CORONARY-ARTERY-DISEASE; NEURAL-NETWORK; IDENTIFICATION; DECOMPOSITION; DIAGNOSIS; FEATURES; RISK; PCG;
D O I
10.3233/IDA-237432
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
OBJECTIVE: A representation of the sound recordings that are associated with the movement of the entire cardiac structure is termed the Phonocardiogram (PCG) signal. In diagnosing such diverse diseases of the heart, PCG signals are helpful. Nevertheless, as recording PCG signals are prone to several surrounding noises and other disturbing signals, it is a complex task. Thus, prior to being wielded for advanced processing, the PCG signal needs to be denoised. This work proposes an improved heart sound classification by utilizing two-stage Low pass filtering and Wavelet Threshold (WT) technique with subsequent Feature Extraction (FE) using Wavelet Scatter Transform and further classification utilizing the Cubic Polynomial Support Vector Machine (SVM) technique for CVD. METHOD: A computer-aided diagnosis system for CVD detection centered on PCG signal analysis is offered in this work. Initially, by heavily filtering the signal, the raw PCG signals obtained using the database were pre-processed. Then, to remove redundant information and noise, it is denoised via the WT technique. From the denoised PCG, wavelet time scattering features were extracted. After that, by employing SVMs, these features were classified for pathology. RESULTS: For the analysis, the PCG signal obtained from the Physionet dataset was considered. Heavy low-pass filtering utilizing a Low-Pass Butterworth Filter (LPBF) is entailed in the pre-processing step. This removed 98% of the noise inherently present in the signal. Further, the signal strength was ameliorated by denoising it utilizing the WT technique. Promising results with maximum noise removal of up to 99% are exhibited by the method. From the PCG, Wavelet Scattering (WS) features were extracted, which were later wielded to categorize the PCG utilizing SVMs with 99.72% accuracy for different sounds. DISCUSSION: The Classification accuracies are analogized with other classification techniques present in the literature. This technique exhibited propitious outcomes with a 3% improvement in the F1 score when weighed against the top-notch techniques. The improvement in the metrics is attributed to the usage of the pre-processing stage comprising of Low-pass filter and WT method, WS Transform (WST), and SVMs. CONCLUSION: The superiority of the proposed technique is advocated by the comparative investigation with prevailing methodologies. The system revealed that Coronary Artery Disease (CAD) can be implemented with superior methods to achieve high accuracy.
引用
收藏
页码:S47 / S63
页数:17
相关论文
共 50 条
  • [42] Heart sound classification using wavelet transform and incremental self-organizing map
    Dokur, Zuemray
    Olmer, Tamer
    [J]. DIGITAL SIGNAL PROCESSING, 2008, 18 (06) : 951 - 959
  • [43] Fundamental Heart Sound Classification using the Continuous Wavelet Transform and Convolutional Neural Networks
    Meintjes, Andries
    Lowe, Andrew
    Legget, Malcolm
    [J]. 2018 40TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC), 2018, : 409 - 412
  • [44] Passive Image Manipulation Detection Using Wavelet Transform and Support Vector Machine Classifier
    Birajdar, Gajanan K.
    Mankar, Vijay H.
    [J]. PROCEEDINGS OF INTERNATIONAL CONFERENCE ON ICT FOR SUSTAINABLE DEVELOPMENT, ICT4SD 2015, VOL 1, 2016, 408 : 447 - 455
  • [45] Epileptic Seizure Detection Using Discrete Wavelet Transform Based Support Vector Machine
    Deshmukh, Prashant
    Ingle, Rahul
    Kehri, Vikram
    Awale, R. N.
    [J]. 2017 INTERNATIONAL CONFERENCE ON COMMUNICATION AND SIGNAL PROCESSING (ICCSP), 2017, : 1933 - 1937
  • [46] Automatic Arrhythmia Detection Using Support Vector Machine Based on Discrete Wavelet Transform
    Hamed, Ibrahim
    Owis, Mohamed I.
    [J]. JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS, 2016, 6 (01) : 204 - 209
  • [47] Voice activity detection in car environment using support vector machine and wavelet transform
    Chen, Shi-Huang
    Guido, Rodrigo Capobianco
    Chen, Shih-Hao
    [J]. ISM WORKSHOPS 2007: NINTH IEEE INTERNATIONAL SYMPOSIUM ON MULTIMEDIA - WORKSHOPS, PROCEEDINGS, 2007, : 252 - +
  • [48] Extraction of text under complex background using wavelet transform and support vector machine
    Sun, Hongxing
    Zhao, Nannan
    Xu, Xinhe
    [J]. IEEE ICMA 2006: PROCEEDING OF THE 2006 IEEE INTERNATIONAL CONFERENCE ON MECHATRONICS AND AUTOMATION, VOLS 1-3, PROCEEDINGS, 2006, : 1493 - +
  • [49] Automatic detection of atrial fibrillation using stationary wavelet transform and support vector machine
    Asgari, Shadnaz
    Mehrnia, Alireza
    Moussavi, Maryam
    [J]. COMPUTERS IN BIOLOGY AND MEDICINE, 2015, 60 : 132 - 142
  • [50] Wavelet Transform Based Consonant - Vowel (CV) Classification Using Support Vector Machines
    Thasleema, T. M.
    Narayanan, N. K.
    [J]. NEURAL INFORMATION PROCESSING, ICONIP 2012, PT II, 2012, 7664 : 250 - 257