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All Journal Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer JOIV : International Journal on Informatics Visualization Specta Journal of Technology Jurnal Abdimas Mandiri Journal of Informatics, Information System, Software Engineering and Applications (INISTA) Abditeknika - Jurnal Pengabdian Kepada Masyarakat Society: Jurnal Pengabdian Masyarakat Jurnal Medika: Medika
Nurseno Bayu Aji, Nurseno Bayu
Jurusan Teknik Informatika, Fakultas Teknologi Informasi, Institut Teknologi Sepuluh Nopember
Aerospace Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Medicine & Pharmacology Nursing Public Health Social Sciences

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CNN-LSTM for Heartbeat Sound Classification Aji, Nurseno Bayu; Kurnianingsih, Kurnianingsih; Masuyama, Naoki; Nojima, Yusuke
JOIV : International Journal on Informatics Visualization Vol 8, No 2 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.2.2115

Abstract

Cardiovascular disorders are among the primary causes of death. Regularly monitoring the heart is of paramount importance in preventing fatalities arising from heart diseases. Heart disease monitoring encompasses various approaches, including the analysis of heartbeat sounds. The auditory patterns of a heartbeat can serve as indicators of heart health. This study aims to build a new model for categorizing heartbeat sounds based on associated ailments. The Phonocardiogram (PCG) method digitizes and records heartbeat sounds. By converting heartbeat sounds into digital data, researchers are empowered to develop a deep learning model capable of discerning heart defects based on distinct cardiac rhythms. This study proposes the utilization of Mel-frequency cepstral coefficients for feature extraction, leveraging their application in voice data analysis. These extracted features are subsequently employed in a multi-step classification process. The classification process merges a convolutional neural network (CNN) with a long short-term memory network (LSTM), forming a comprehensive deep learning architecture. This architecture is further enhanced through optimization utilizing the Adagrad optimizer. To examine the effectiveness of the proposed method, its classification performance is evaluated using the "Heartbeat Sounds" dataset sourced from Kaggle. Experimental results underscore the effectiveness of the proposed method by comparing it with simple CNN, CNN with vanilla LSTM, and traditional machine learning methods (MLP, SVM, Random Forest, and k-NN).
Co-Authors Dwiana Hendrawati Fahriah, Sirli Fitriyani, Rizki Putri Hestinigsih, Idhawati Kuntardjo, Samuel Beta Kuntarjo, Samuel Beta Kurnianingsih Kurnianingsih Kurnianingsih, Kurnianingsih Lavindi, Eri Eli Mardiyono Mardiyono Masuyama, Naoki Muhammad Anif Nojima, Yusuke Nursuci Putri Husain Prayitno Prayitno Safitri, Zulfa Sahid Santosa, Naufal Adli Santoso, Kuwat Totok Prasetyo Tri Raharjo Yudantoro Triyono, Liliek Wahyu Sulistiyo Wiktasari Wiktasari, Wiktasari Yanwari, M. Irwan Yanwari, Muhammad Irwan