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Key Techniques and Challenges for Processing of Heart Sound Signals

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Applied Intelligence and Informatics (AII 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1435))

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Abstract

Recently, new advances and emerging technologies in healthcare and medicine have been growing rapidly, allowing for automatic disease diagnosis. Healthcare technology advances entail monitoring devices and processing signals. Advanced signal processing and analytical techniques were effectively implemented in numerous research domains. Thus, adopting such methods for biomedical signal processing is an essential study field. The signal processing techniques are explicitly applied to heart sound (called phonocardiogram or PCG) signals as part of biomedical signals for heart health monitoring in this paper. The automatic detection of life-threatening cardiac arrhythmias has been a subject of interest for many decades. However, the computer-based PCG segmentation and classification methods are still not an end-to-end task; the process involves several tasks and challenges to overcome. The conducted evaluation scheme of the classifier also has a significant impact on the reliability of the proposed method. Our main contributions are twofold. First, we provided a systematic overview of various methods that can be employed in real applications for heart sound abnormalities. Second, we indicated potential future research opportunities. PCG segmentation is critical, and arguably the hardest stage in PCG processing. Basically, basic heart sounds can be identified by detecting the offset R-peak and T-wave in the ECG signal. Unfortunately, utilizing the ECG signal as a reference to the PCG segment is not always an easy operation because: it requires synchronous recording of ECG and PCG signals; precise identification of T-wave offset is often difficult; and ECG-PCG temporal alignment is not always consistent. Using machine learning methods in PCG segmentation involves multiple types and many features retrieved in both univariate or multivariate formats. This leads to selecting the best PCG-segmentation performance feature sets. PCG segmentation approaches that use featureless methods based on powerful statistical models have the potential to solve the problem of feature extraction and minimize the total computational cost of the segmentation approach.

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Author information

Authors and Affiliations

  1. HealUltra PLT, 020 Jalan Pulai 18, Taman Pulai Utama, 81300, Skudai, Johore, Malaysia

    Sheikh Hussain Shaikh Salleh & Siti Hadrina Bt Sheikh Hussain

  2. School of Information Technology, Monash University Malaysia, 47500, Bandar Sunway, Selangor, Malaysia

    Fuad M. Noman & Ting Chee-Ming

  3. Department of Cardiothoracic Surgery, Hospital Sultanah Aminah, 80100, Johor Bahru, Johor, Malaysia

    Syed Rasul Bin G. Syed Hamid

  4. Department of Physics, Faculty of Science, Universiti Teknologi Malaysia, 81310, Skudai, Johor, Malaysia

    M. A. Jalil

  5. Faculty of Medicine, Universiti Sultan Zainal Abidin, Medical Campus, 20400, Kuala Terengganu, Terengganu, Malaysia

    A. L. Ahmad Zubaidi

  6. Faculty of Applied Sciences and Technology, Universiti Tun Hussein Onn Malaysia, Parit Raja, Malaysia

    Kavikumar Jacob

  7. Amity School of Applied Sciences, Amity University, Jaipur, 303001, Rajasthan, India

    Kanad Ray

  8. Institute of Information Technology, Jahangirnagar University, Savar, Dhaka, 1342, Bangladesh

    M. Shamim Kaiser

  9. Asia Metropolitan University, 6, Jalan Lembah, Bandar Baru Seri Alam, 81750, Masai, Johor, Malaysia

    Jalil Ali

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  1. Sheikh Hussain Shaikh Salleh
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Corresponding author

Correspondence to M. Shamim Kaiser .

Editor information

Editors and Affiliations

  1. Nottingham Trent University, Nottingham, UK

    Mufti Mahmud

  2. Jahangirnagar University, Savar, Dhaka, Bangladesh

    M. Shamim Kaiser

  3. Auckland University of Technology, Auckland, New Zealand

    Nikola Kasabov

  4. Old Dominion University, Norfolk, VA, USA

    Khan Iftekharuddin

  5. Maebashi Institute of Technology, Maebashi, Japan

    Ning Zhong

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Shaikh Salleh, S.H. et al. (2021). Key Techniques and Challenges for Processing of Heart Sound Signals. In: Mahmud, M., Kaiser, M.S., Kasabov, N., Iftekharuddin, K., Zhong, N. (eds) Applied Intelligence and Informatics. AII 2021. Communications in Computer and Information Science, vol 1435. Springer, Cham. https://doi.org/10.1007/978-3-030-82269-9_11

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  • DOI: https://doi.org/10.1007/978-3-030-82269-9_11

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