Automated Pediatric Cardiac Auscultation

Vos, J. P. de; Blanckenberg, Mike

doi:10.1109/tbme.2006.886660

Cited by 76 publications

(40 citation statements)

References 17 publications

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“…Klasifikasi sinyal jantung diperlukan untuk mengetahui penyakit jantung yang seringkali datang secara tiba-tiba. Kondisi jantung dapat diketahui melalui aktivitas listrik jantung yang direpresentasikan dalam bentuk grafik menggunakan sebuah instrumen medis yang disebut elektrokardiogram (EKG) [2]. Jantung merupakan organ yang mampu memproduksi muatan listrik.…”

Section: Pendahuluanunclassified

Pembuatan Elektrokardiogram dan Penentuan Interval QRS secara Otomatis

Astuti

Nuryani

Lestari

2017

JFA

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IntisariPada penelitian ini telah dibuat rancangan sistem elektrokardiogram (EKG) yang dilengkapi de-ngan sistem penentuan interval QRS secara otomatis. Sistem EKG yang dibuat menggunakan arduino dan komputer. Hasil dari penelitian ini diimplementasikan menggunakan perangkat lunak processing. Data sinyal jantung yang digunakan pada penelitian ini diambil langsung dari pasien. Untuk menentukan interval QRS maka terlebih dahulu dilakukan penentuan posisi puncak R. Posisi puncak R selanjutnya digunakan untuk menentukan posisi titik Q dan posisi titik S. Hasil akurasi penentuan interval QRS pada penelitian ini adalah sebesar 95,89%. ABSTRACTIn this research, system design electrocardiogram (ECG) is equipped with a QRS interval determination system automatically. ECG systems was created using arduino and computer. The results of this research is implemented using software processing. Cardiac signal data used were taken from patients. To determine the QRS interval, the first is the determination of the peak position R. Peak position R is then used to determine the position of the point Q and the position of the point S. Result for accuracy determination QRS interval in this research is up to 95.89%.KATA KUNCI: Electrocardiogram, Interval QRS, Arduino, software processing http://dx

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Section: Pendahuluanunclassified

Pembuatan Elektrokardiogram dan Penentuan Interval QRS secara Otomatis

Astuti

Nuryani

Lestari

2017

JFA

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“…Previous authors have also addressed the analysis and classification of heart sounds [9][10][11][12][13] . Signal processing techniques implemented in the analysis of heart sounds include the Fast Fourier Transform (FFT), Short-Time Fourier Transform (STFT), Wigner Distribution (WD), ChoiWilliams Distribution (CWD) and the Wavelet Transform (WT).…”

Section: Classification Of Heart Soundsmentioning

confidence: 99%

Autonomous detection of heart sound abnormalities using an auscultation jacket

Visagie

Scheffer

Lubbe

et al. 2009

Australas. Phys. Eng. Sci. Med.

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This paper presents a study using an auscultation jacket with embedded electronic stethoscopes, and a software classification system capable of differentiating between normal and certain auscultatory abnormalities. The aim of the study is to demonstrate the potential of such a system for semi-automated diagnosis for underserved locations, for instance in rural areas or in developing countries where patients far outnumber the available medical personnel. Using an "auscultation jacket", synchronous data was recorded at multiple chest locations on 31 healthy volunteers and 21 patients with heart pathologies. Electrocardiograms (ECGs) were also recorded simultaneously with phonocardiographic data. Features related to heart pathologies were extracted from the signals and used as input to a feed-forward artificial neural network. The system is able to classify between normal and certain abnormal heart sounds with a sensitivity of 84% and a specificity of 86%. Though the number of training and testing samples presented are limited, the system performed well in differentiating between normal and abnormal heart sounds in the given database of available recordings. The results of this study demonstrate the potential of such a system to be used as a fast and cost-effective screening tool for heart pathologies.

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“…Artificial neural networks (ANNs) have been the most widely used machine learning-based approach for heart sound classification. Typical relevant studies used different signal features as the input to the ANN classifier, including wavelet features [7], time, frequency and complexity-based features [8], and time-frequency features [9]. A number of researchers have also applied support vector machines (SVM) for heart sound classification in recent years.…”

Section: Introductionmentioning

confidence: 99%