The improvement of phonocardiograph signal (PCG) representation through the electronic stethoscope

Sumarna,; Astono, Juli; Purwanto, Agus; Agustika, Dyah Kurniawati

doi:10.1109/eecsi.2017.8239099

Cited by 5 publications

(6 citation statements)

References 4 publications

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“…The data were collected in the form of heart rate signal recordings in .wav file TELKOMNIKA Telecommun Comput El Control  Classification of heart disease based on PCG signal using CNN (Aditya Wisnugraha Sugiyarto) 1699 format from 75 volunteers with heart disease and 25 normal people. The data recording process is based on research conducted by [43].…”

Section: Methodsmentioning

confidence: 99%

Classification of heart disease based on PCG signal using CNN

2021

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Cardiovascular disease is the leading cause of death in the world, so early detection of heart conditions is very important. Detection related to cardiovascular disease can be conducted through the detection of heart signals interference, one of which is called phonocardiography. This study aims to classify heart disease based on phonocardiogram (PCG) signals using the convolutional neural networks (CNN). The study was initiated with signal preprocessing by cutting and normalizing the signal, followed by a continuous wavelet transformation process using a mother wavelet analytic morlet. The decomposition results are visualized using a scalogram, then the results are used as CNN input. In this study, the PCG signals used were classified into normal, angina pectoris (AP), congestive heart failure (CHF), and hypertensive heart disease (HHD). The total data used, classified into 80 training data and 20 testing data. The obtained model shows the level of accuracy, sensitivity, and diagnostic specificity of 100%, 100%, and 100% for training data, respectively, while the prediction results for testing data indicate the level of accuracy, sensitivity, and specificity of 85%, 80%, and 100%, respectively. This result proved to be better than the mother wavelet or other classifier methods, then the model was deployed into the graphical user interface (GUI).

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Section: Methodsmentioning

confidence: 99%

Classification of heart disease based on PCG signal using CNN

2021

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“…In this case, a homemade electronic stethoscope has been produced. This device was created to improve the shortcomings of conventional acoustic stethoscopes [10]. This device was first designed and created in [11].…”

Section: Phonocardiogrammentioning

confidence: 99%

“…This device was first designed and created in [11]. Furthermore, the PCG signal from this device is improved in [10]. Especially in this study, the component spectrum of the resulting signal is determined.…”

Section: Phonocardiogrammentioning

confidence: 99%

Study of Amplifier Operating Point to Improve Phonocardiogram Signal Representation in a Custom-made Electronic Stethoscope

Astono¹,

Kuswanto²,

Sumarna³

et al. 2022

TEM Journal

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The performance of a custom-made electronic stethoscope needs to be improved through a study on the operating point of the amplifier. The aim of this study is to analyse the composition and content of the frequency components contained in the phonocardiogram signal by spectral extraction. The output is a voltage signal that represents the heart rate. The operating point of the amplifier is varied. The signal is recorded on a laptop and analysed. The results show that the operating point produces a balanced swing signal. Hence, the amplifier’s operating point does not affect the component spectrum in the recorded signal.

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“…The combination of a stethoscope’s head and a microphone is an established approach. In many cases the head is constructed using three-dimensional (3D) printed stethoscope [ 18 ] together with an Electret [ 19 , 20 , 21 ], mechanical microphones [ 22 , 23 , 24 ], or Piezo-Electrical Film [ 25 ]. Applications include automatic analysis of the cardiac, lung, and even fetal-heart rate signals [ 26 ].…”

Section: Background and Related Workmentioning

confidence: 99%

“…We expose each stethoscope-microphone individually to a range of frequencies in the low-spectrum (21,41,61,81,101 Hz), in the middle-spectrum (501, 751, 1001, 1251 Hz), and in the high-spectrum (1501, 1751, 2001, 2251, 2501 Hz) for 20 s each frequency with a square-wave signal. We used an android application in a Samsung Galaxy S8 on top of the stethoscope-head (3 cm separation) at the highest available volume to generate the signals (Figure 4).…”

Section: Single Microphones Discrete Frequency Response Calibrationmentioning

confidence: 99%

Facial Muscle Activity Recognition with Reconfigurable Differential Stethoscope-Microphones

Bello

Zhou

Lukowicz

2020

Sensors

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Many human activities and states are related to the facial muscles’ actions: from the expression of emotions, stress, and non-verbal communication through health-related actions, such as coughing and sneezing to nutrition and drinking. In this work, we describe, in detail, the design and evaluation of a wearable system for facial muscle activity monitoring based on a re-configurable differential array of stethoscope-microphones. In our system, six stethoscopes are placed at locations that could easily be integrated into the frame of smart glasses. The paper describes the detailed hardware design and selection and adaptation of appropriate signal processing and machine learning methods. For the evaluation, we asked eight participants to imitate a set of facial actions, such as expressions of happiness, anger, surprise, sadness, upset, and disgust, and gestures, like kissing, winkling, sticking the tongue out, and taking a pill. An evaluation of a complete data set of 2640 events with 66% training and a 33% testing rate has been performed. Although we encountered high variability of the volunteers’ expressions, our approach shows a recall = 55%, precision = 56%, and f1-score of 54% for the user-independent scenario(9% chance-level). On a user-dependent basis, our worst result has an f1-score = 60% and best result with f1-score = 89%. Having a recall ≥60% for expressions like happiness, anger, kissing, sticking the tongue out, and neutral(Null-class).

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The improvement of phonocardiograph signal (PCG) representation through the electronic stethoscope

Cited by 5 publications

References 4 publications

Classification of heart disease based on PCG signal using CNN

Classification of heart disease based on PCG signal using CNN

Study of Amplifier Operating Point to Improve Phonocardiogram Signal Representation in a Custom-made Electronic Stethoscope

Facial Muscle Activity Recognition with Reconfigurable Differential Stethoscope-Microphones

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