A Wireless Electronic Stethoscope to Classify Children Heart Sound Abnormalities

2019 ◽  
Author(s):  
Md. Riadul Islam ◽  
Md. Mahadi Hassan ◽  
M. Raihan ◽  
Sabuz Kumar Datto ◽  
Abdullah Shahriar ◽  
...  
Keyword(s):  
Heart Sound ◽  
Electronic Stethoscope

scholarly journals Electronic Stethoscope Design

2020 ◽  
pp. 10-16
Author(s):  
Mustafa Berkant Selek ◽  
Mert Can Duyar ◽  
Yalcin Isler
Keyword(s):  
Early Diagnosis ◽  
Heart Valve ◽  
Heart Sound ◽  
Late Diagnosis ◽  
Digital Data ◽  
Main Difficulty ◽  
Negative Effects ◽  
Electronic Stethoscope ◽  
Heart Murmurs ◽  
Second Heart Sound

Nowadays, despite the developing technology lots of patients lost their lives because of wrong and late diagnosis. With early diagnosis, most diseases and negative effects of the diseases for the patient can be prevented. Early diagnosis can also prevent cardiological diseases. Although auscultation of the chest with a stethoscope is an effective and basic method, a stethoscope isn't enough for the diagnosis of some diseases. One example of these diseases is heart valve malfunctions when the valves do not work as desired heart murmurs occur. The main goal of this project is to develop an electronic stethoscope and observing obtained signals as a graphic. The main difficulty while auscultation of chest with a stethoscope is, this procedure needs lots of experience and also even tough physician have enough experience, it's very hard to diagnose grade 1 and 2 heart murmurs. Furthermore, while auscultation tachycardia patients, generally it's very hard to decide where the first heart (S1) sound and second heart sound (S2) begins. In this project, it is planned to demonstrate heart sounds as a graphic. This method provides physicians to diagnose all kinds of chest sounds easily even the sounds which they cannot diagnose or recognize with their ears by stethoscope. Moreover, as the chest sounds are obtained as digital data, these data can be sent as desired. When a physician needs to get someone else's idea, these recordings can be sent to another professional.

scholarly journals HUMAN IDENTIFICATION BASED ON HEART SOUND AUSCULTATION POINT

2017 ◽  
Vol 79 (7) ◽  
Author(s):  
I. Nur Fariza ◽  
Sh-Hussain Salleh ◽  
Fuad Noman ◽  
Hadri Hussain
Keyword(s):  
Heart Sound ◽  
Recognition Rate ◽  
Human Identification ◽  
Personal Identification ◽  
Heart Sounds ◽  
Electronic Stethoscope ◽  
The Past ◽  
Technology Advancement ◽  
Aortic Area ◽  
Mitral Area

The application of human identification and verification has widely been used for over the past few decades.  Drawbacks of such system however, are inevitable as forgery sophisticatedly developed alongside the technology advancement.  Thus, this study proposed a research on the possibility of using heart sound as biometric. The main aim is to find an optimal auscultation point of heart sounds from either aortic, pulmonic, tricuspid or mitral that will most suitable to be used as the sound pattern for personal identification.  In this study, the heart sound was recorded from 92 participants using a Welch Allyn Meditron electronic stethoscope whereas Meditron Analyzer software was used to capture the signal of heart sounds and ECG simultaneously for duration of 1 minute.  The system is developed by a combination Mel Frequency Cepstrum Coefficients (MFCC) and Hidden Markov Model (HMM).  The highest recognition rate is obtained at aortic area with 98.7% when HMM has 1 state and 32 mixtures, the lowest Equal Error Rate (EER) achieved was 0.9% which is also at aortic area.  In contrast, the best average performance of HMM for every location is obtained at mitral area with 99.1% accuracy and 17.7% accuracy of EER at tricuspid area.

scholarly journals A Low-Noise-Level Heart Sound System Based on Novel Thorax-Integration Head Design and Wavelet Denoising Algorithm

Micromachines ◽  
2019 ◽  
Vol 10 (12) ◽  
pp. 885
Author(s):  
Shuo Zhang ◽  
Ruiqing Zhang ◽  
Shijie Chang ◽  
Chengyu Liu ◽  
Xianzheng Sha
Keyword(s):  
Noise Level ◽  
Filter Bank ◽  
Heart Sound ◽  
Low Noise ◽  
Heart Sounds ◽  
Noisy Environments ◽  
Sound System ◽  
Electronic Stethoscope ◽  
Surrounding Environment ◽  
Great Performance

Along with the great performance in diagnosing cardiovascular diseases, current stethoscopes perform unsatisfactorily in controlling undesired noise caused by the surrounding environment and detector operation. In this case, a low-noise-level heart sound system was designed to inhibit noise by a novel thorax-integration head with a flexible electric film. A hardware filter bank and wavelet-based algorithm were employed to enhance the recorded heart sounds from the system. In the experiments, we used the new system and the 3M™ Littmann® Model 3200 Electronic Stethoscope separately to record heart sounds in different noisy environments. The results illustrated that the average estimated noise ratio represented 21.26% and the lowest represented only 12.47% compared to the 3M stethoscope, demonstrating the better performance in denoising ability of this system than state-of-the-art equipment. Furthermore, based on the heart sounds recorded with this system, some diagnosis results were achieved from an expert and compared to echocardiography reports. The diagnoses were correct except for two uncertain items, which greatly confirmed the fact that this system could reserve complete pathological information in the end.

The Utility of Real-Time Remote Cardiac Auscultation Using a Bluetooth-Connected Electronic Stethoscope: Open-Label Randomized Controlled Pilot Trial (Preprint)

2020 ◽  
Author(s):  
Takanobu Hirosawa ◽  
Yukinori Harada ◽  
Kohei Ikenoya ◽  
Shintaro Kakimoto ◽  
Taro Shimizu
Keyword(s):  
Real Time ◽  
Heart Sound ◽  
Intervention Group ◽  
Heart Sounds ◽  
Control Group ◽  
Cardiac Auscultation ◽  
Open Label ◽  
Patient Simulator ◽  
Electronic Stethoscope ◽  
Randomized Controlled

BACKGROUND With the coronavirus disease 2019 pandemic, the need for telemedicine is rapidly growing worldwide. The development and improvement of remote physical examination systems, especially remote auscultation, are required to facilitate telemedicine. A Bluetooth system combined with an electronic stethoscope is a promising option for remote auscultation in clinics and hospitals. In our previous work, we demonstrated that the utility of a Bluetooth-connected real-time remote auscultation system for the lung simulator is comparable to that of classical direct auscultation. However, the utility of such systems remains unknown for cardiac auscultation. OBJECTIVE This study was conducted to evaluate the utility of real-time auscultation using a Bluetooth-connected electronic stethoscope compared to that of classical auscultation using a cardiology patient simulator. METHODS This was an open-label randomized controlled trial, including senior residents and faculty members in the Department of General Internal Medicine of a university hospital. The only exclusion criterion was a refusal to participate. All participants attended a tutorial session, in which they listened to 15 heart sounds on the cardiology patient simulator using a traditional stethoscope and were told the correct classification. Thereafter, participants were randomly assigned to either the real-time remote auscultation group (intervention group) or the classical auscultation group (control group) for test sessions. In the test sessions, participants had to classify a series of ten heart sounds. The intervention group remotely listened to the heart sounds using an electronic stethoscope, a Bluetooth transmitter, and a wireless, noise-canceling, stereo headset. The control group listened to the heart sounds directly using a classic stethoscope. The primary outcome was the test score. The secondary outcomes were the rates of correct answers for each heart sound. The two groups were compared using Fisher’s exact test. RESULTS In total, 20 participants were included; six and 14 were assigned to the intervention and control groups, respectively. There was no difference in age (P=.99), sex (P=.99), or years from graduation (P=.78) between the two groups. The overall test score in the intervention group (50/60, 83.3%) was not different from that in the control group (119/140, 85.0%) (P=.77). There was no heart sound for which the correct answer rate differed between groups. CONCLUSIONS This study demonstrated that the utility of a real-time remote cardiac auscultation system using a Bluetooth-connected electronic stethoscope was comparable to that of direct auscultation using a classic stethoscope. This implies that the real world’s essential heart sounds could be classified by a real-time remote cardiac auscultation system using a Bluetooth-connected electronic stethoscope. CLINICALTRIAL UMIN-CTR UMIN000041601; https://upload.umin.ac.jp/cgi-open-bin/ctr/ctr_view.cgi?recptno=R000047136

scholarly journals Performance Evaluation of Heart Sound Cancellation in FPGA Hardware Implementation for Electronic Stethoscope

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Chun-Tang Chao ◽  
Nopadon Maneetien ◽  
Chi-Jo Wang ◽  
Juing-Shian Chiou
Keyword(s):  
Heart Sound ◽  
Sound Processing ◽  
Electronic Stethoscope ◽  
Gate Arrays ◽  
Implementation Evaluation ◽  
Breath Sounds ◽  
Field Programmable ◽  
Programmable Gate Arrays ◽  
Hardware Circuit ◽  
Sound Cancellation

This paper presents the design and evaluation of the hardware circuit for electronic stethoscopes with heart sound cancellation capabilities using field programmable gate arrays (FPGAs). The adaptive line enhancer (ALE) was adopted as the filtering methodology to reduce heart sound attributes from the breath sounds obtained via the electronic stethoscope pickup. FPGAs were utilized to implement the ALE functions in hardware to achieve near real-time breath sound processing. We believe that such an implementation is unprecedented and crucial toward a truly useful, standalone medical device in outpatient clinic settings. The implementation evaluation with one Altera cyclone II–EP2C70F89 shows that the proposed ALE used 45% resources of the chip. Experiments with the proposed prototype were made using DE2-70 emulation board with recorded body signals obtained from online medical archives. Clear suppressions were observed in our experiments from both the frequency domain and time domain perspectives.

scholarly journals Classification of Heart Sound Signal Using Multiple Features

2018 ◽  
Vol 8 (12) ◽  
pp. 2344 ◽  
Author(s):  
Yaseen ◽  
Gui-Young Son ◽  
Soonil Kwon
Keyword(s):  
Heart Sound ◽  
Sound Signal ◽  
Support Vector ◽  
Multiple Features ◽  
Electronic Stethoscope ◽  
Heart Disorders ◽  
Discrete Wavelets ◽  
Mel Frequency Cepstral Coefficient ◽  
Heart Sound Signal

Cardiac disorders are critical and must be diagnosed in the early stage using routine auscultation examination with high precision. Cardiac auscultation is a technique to analyze and listen to heart sound using electronic stethoscope, an electronic stethoscope is a device which provides the digital recording of the heart sound called phonocardiogram (PCG). This PCG signal carries useful information about the functionality and status of the heart and hence several signal processing and machine learning technique can be applied to study and diagnose heart disorders. Based on PCG signal, the heart sound signal can be classified to two main categories i.e., normal and abnormal categories. We have created database of 5 categories of heart sound signal (PCG signals) from various sources which contains one normal and 4 are abnormal categories. This study proposes an improved, automatic classification algorithm for cardiac disorder by heart sound signal. We extract features from phonocardiogram signal and then process those features using machine learning techniques for classification. In features extraction, we have used Mel Frequency Cepstral Coefficient (MFCCs) and Discrete Wavelets Transform (DWT) features from the heart sound signal, and for learning and classification we have used support vector machine (SVM), deep neural network (DNN) and centroid displacement based k nearest neighbor. To improve the results and classification accuracy, we have combined MFCCs and DWT features for training and classification using SVM and DWT. From our experiments it has been clear that results can be greatly improved when Mel Frequency Cepstral Coefficient and Discrete Wavelets Transform features are fused together and used for classification via support vector machine, deep neural network and k-neareast neighbor(KNN). The methodology discussed in this paper can be used to diagnose heart disorders in patients up to 97% accuracy. The code and dataset can be accessed at “https://github.com/yaseen21khan/Classification-of-Heart-Sound-Signal-Using-Multiple-Features-/blob/master/README.md”.

scholarly journals Microcontrollers Performance in Portable Electronic Stethoscope Design with a Disease Symptoms Detection Feature

2021 ◽  
Vol 1 (2) ◽  
pp. 61-67
Author(s):  
Torib Hamzah Hamzah ◽  
Endang Dian Setioningsih ◽  
Sumber Sumber
Keyword(s):  
Heart Disease ◽  
Data Collection ◽  
Research Method ◽  
Heart Sound ◽  
Process Data ◽  
Disease Symptoms ◽  
Electronic Stethoscope ◽  
Significant Difference ◽  
Different Types ◽  
Battery Consumption

One of the early examinations that is often done is to detect heart disease using a stethoscope. The electronic stethoscope consists of a membrane and tube from a conventional stethoscope coupled with a condenser microphone which is then connected to a computer. The purpose of this study is to analyze the comparison of two types of microcontrollers in the design of a portable electronic stethoscope equipped with a symptom detector. The research method used is instrumentation with 2 types of microcontrollers to design a heart sound detector. In processing the data to be displayed on the 16x2 Character LCD. Sending heart signal data for 60 seconds to produce BPM data which is processed using 2 different types of microcontrollers. The results of data collection on battery consumption of power usage on the AT mega 2560 resulted in an average saving of 0.11W. Therefore, it can be concluded that the two stethoscopes have a significant difference when compared, where the Arduino Mega 2560 is able to process data from heart signals faster than the AT mega 328P. The results of the research that have been carried out can be implemented using a system that strongly supports the needs when checking heart sound signals

DIAGNOSIS OF CARDIAC ABNORMALITY USING HEART SOUND

2016 ◽  
Vol 28 (05) ◽  
pp. 1650032 ◽  
Author(s):  
Anandeep Chaudhuri ◽  
T. Jayanthi
Keyword(s):  
Heart Sound ◽  
Low Cost ◽  
Back Propagation ◽  
Normal Subjects ◽  
Cost Ratio ◽  
Cardiac Abnormality ◽  
External Disturbances ◽  
Electronic Stethoscope ◽  
Non Invasive ◽  
Feed Forward Back Propagation

Heart sound (HS) analysis or auscultation is a standout amongst the most simple, non-invasive and costless methods used to evaluate heart health and is one of the basic and foremost routine of a doctor while reviewing a patient. Detecting cardiac abnormality by auscultation demands a physician’s experience and even then there is a high scope of committing error. In this paper, a low cost electronic stethoscope is built to acquire HS in a novel manner by taking one from each ventricular and auricular area and superimposed, to get a resultant signal of both distinct lub-dub sound. Then, a light, fast and low computation speed beat track method followed by wavelet reconstruction is presented for correct detection of S1 and S2. It is done without ECG reference, and can be used satisfactorily on both normal and pathological HSs. Moreover, heartbeats can be identified in both de-noised and noised environment as it is independent of external disturbances. Significant features are extracted from the resultant HSs with detected S1 and S2 and feed-forward back propagation method. It is used to classify the HS nature into normal and pathological. This algorithm has been implemented on 24 pairs of HSs, extracted from 24 patients of 15 pathological and nine normal subjects and the classification yields a result of 91.7% accuracy with 81.8% sensitivity. The overall performance suggests a good performance to cost ratio. This system can be used as first diagnosis tool by the medical professionals.

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