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 Usefulness of the second heart sound for predicting pulmonary hypertension in patients with interstitial lung disease

2016 ◽  
Vol 134 (1) ◽  
pp. 34-39
Author(s):  
Sandra de Barros Cobra ◽  
Rayane Marques Cardoso ◽  
Marcelo Palmeira Rodrigues
Keyword(s):  
Pulmonary Hypertension ◽  
Interstitial Lung Disease ◽  
Lung Disease ◽  
Heart Sound ◽  
Clinical Signs ◽  
Systolic Pressure ◽  
Heart Sounds ◽  
Aortic Area ◽  
Second Heart Sound ◽  
Mitral Area

CONTEXT AND OBJECTIVE: P2 hyperphonesis is considered to be a valuable finding in semiological diagnoses of pulmonary hypertension (PH). The aim here was to evaluate the accuracy of the pulmonary component of second heart sounds for predicting PH in patients with interstitial lung disease. DESIGN AND SETTING: Cross-sectional study at the University of Brasilia and Hospital de Base do Distrito Federal. METHODS: Heart sounds were acquired using an electronic stethoscope and were analyzed using phonocardiography. Clinical signs suggestive of PH, such as second heart sound (S2) in pulmonary area louder than in aortic area; P2 > A2 in pulmonary area and P2 present in mitral area, were compared with Doppler echocardiographic parameters suggestive of PH. Sensitivity (S), specificity (Sp) and positive (LR+) and negative (LR-) likelihood ratios were evaluated. RESULTS: There was no significant correlation between S2 or P2 amplitude and PASP (pulmonary artery systolic pressure) (P = 0.185 and 0.115; P= 0.13 and 0.34, respectively). Higher S2 in pulmonary area than in aortic area, compared with all the criteria suggestive of PH, showed S = 60%, Sp= 22%; LR+ = 0.7; LR- = 1.7; while P2> A2 showed S= 57%, Sp = 39%; LR+ = 0.9; LR- = 1.1; and P2 in mitral area showed: S= 68%, Sp = 41%; LR+ = 1.1; LR- = 0.7. All these signals together showed: S= 50%, Sp = 56%. CONCLUSIONS: The semiological signs indicative of PH presented low sensitivity and specificity levels for clinically diagnosing this comorbidity.

scholarly journals Biometric Identification Method for Heart Sound Based on Multimodal Multiscale Dispersion Entropy

Entropy ◽  
2020 ◽  
Vol 22 (2) ◽  
pp. 238 ◽  
Author(s):  
Xiefeng Cheng ◽  
Pengfei Wang ◽  
Chenjun She
Keyword(s):  
Markov Models ◽  
Heart Sound ◽  
Recognition Rate ◽  
Ensemble Empirical Mode Decomposition ◽  
Heart Sounds ◽  
Feature Representation ◽  
Intrinsic Mode Functions ◽  
Single Cycle ◽  
Starting Position ◽  
Mode Decomposition

In this paper, a new method of biometric characterization of heart sounds based on multimodal multiscale dispersion entropy is proposed. Firstly, the heart sound is periodically segmented, and then each single-cycle heart sound is decomposed into a group of intrinsic mode functions (IMFs) by improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN). These IMFs are then segmented to a series of frames, which is used to calculate the refine composite multiscale dispersion entropy (RCMDE) as the characteristic representation of heart sound. In the simulation experiments I, carried out on the open heart sounds database Michigan, Washington and Littman, the feature representation method was combined with the heart sound segmentation method based on logistic regression (LR) and hidden semi-Markov models (HSMM), and feature selection was performed through the Fisher ratio (FR). Finally, the Euclidean distance (ED) and the close principle are used for matching and identification, and the recognition accuracy rate was 96.08%. To improve the practical application value of this method, the proposed method was applied to 80 heart sounds database constructed by 40 volunteer heart sounds to discuss the effect of single-cycle heart sounds with different starting positions on performance in experiment II. The experimental results show that the single-cycle heart sound with the starting position of the start of the first heart sound (S1) has the highest recognition rate of 97.5%. In summary, the proposed method is effective for heart sound biometric recognition.

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.

HEART SOUND CLASSIFICATION AND RECOGNITION BASED ON EEMD AND CORRELATION DIMENSION

2014 ◽  
Vol 14 (04) ◽  
pp. 1450046 ◽  
Author(s):  
WENYING ZHANG ◽  
XINGMING GUO ◽  
ZHIHUI YUAN ◽  
XINGHUA ZHU
Keyword(s):  
Feature Extraction ◽  
Heart Sound ◽  
Linear Time ◽  
Heart Diseases ◽  
Recognition Rate ◽  
Heart Sounds ◽  
Support Vector ◽  
Svm Classifier ◽  
Intrinsic Mode Functions ◽  
Sound Classification

Analysis of heart sound is of great importance to the diagnosis of heart diseases. Most of the feature extraction methods about heart sound have focused on linear time-variant or time-invariant models. While heart sound is a kind of highly nonstationary and nonlinear vibration signal, traditional methods cannot fully reveal its essential properties. In this paper, a novel feature extraction approach is proposed for heart sound classification and recognition. The ensemble empirical mode decomposition (EEMD) method is used to decompose the heart sound into a finite number of intrinsic mode functions (IMFs), and the correlation dimensions of the main IMF components (IMF1~IMF4) are calculated as feature set. Then the classical Binary Tree Support Vector Machine (BT-SVM) classifier is employed to classify the heart sounds which include the normal heart sounds (NHSs) and three kinds of abnormal signals namely mitral stenosis (MT), ventricular septal defect (VSD) and aortic stenosis (AS). Finally, the performance of the new feature set is compared with the correlation dimensions of original signals and the main IMF components obtained by the EMD method. The results showed that, for NHSs, the feature set proposed in this paper performed the best with recognition rate of 98.67%. For the abnormal signals, the best recognition rate of 91.67% was obtained. Therefore, the proposed feature set is more superior to two comparative feature sets, which has potential application in the diagnosis of cardiovascular diseases.

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 Electronic Method for Detection of Heart Valve Defects

2019 ◽  
Vol 8 (4) ◽  
pp. 2373-2376
Keyword(s):  
Heart Valve ◽  
Heart Sound ◽  
Early Stage ◽  
Heart Function ◽  
Data File ◽  
Frequency Component ◽  
Heart Sounds ◽  
Maximum Magnitude ◽  
Electronic Stethoscope ◽  
Free Environment

This article describes a sophisticated method for detection of Heart valve defects at early stage i.e Grade 1 murmur without the expertise of a doctor. The diagnosis based on heard heart sounds through, either a conventional acoustic or an electronic stethoscope is itself a very specialized skill that will take years to acquire. Sometimes the doctors cannot detect these defects till the murmurs reach grade 3 and above which generally is too late for prognosis. Here, we have taken the recorded heart sounds from 350 subjects and performed the Fast Fourier Transform(FFT) on it, but it didn’t give satisfying result. We have also recoded heart sounds using phonograph for 20 subjects in noise free environment. In this technique Frequency component with the maximum magnitude (in Hertz) was observed to be of varying values across some heart sounds (for e.g., heart sound from subject 41=13.0588Hz and heart sound from subject 58=324.5293Hz). Hence normal heart sounds could not be categorized in a generic way. To overcome this problem, we have used Shannon energy method on same data file, which will classify the condition of heart by finding S1(lub) and S2(dub) frequency component, if they lie between 30-100Hz, the heart is normal and if it is above 100Hz then the heart function is abnormal..

Detection and localization of S1 and S2 heart sounds by 3rd order normalized average Shannon energy envelope algorithm

2021 ◽  
pp. 095441192199810
Author(s):  
Madhwendra Nath ◽  
Subodh Srivastava ◽  
Niharika Kulshrestha ◽  
Dilbag Singh
Keyword(s):  
Success Rate ◽  
Heart Sound ◽  
Heart Diseases ◽  
Heart Sounds ◽  
Sound Signal ◽  
Ecg Signal ◽  
Shannon Energy ◽  
Detection And Localization ◽  
Pcg Signals ◽  
Heart Sound Signal

Adults born after 1970s are more prone to cardiovascular diseases. Death rate percentage is quite high due to heart related diseases. Therefore, there is necessity to enquire the problem or detection of heart diseases earlier for their proper treatment. As, Valvular heart disease, that is, stenosis and regurgitation of heart valve, are also a major cause of heart failure; which can be diagnosed at early-stage by detection and analysis of heart sound signal, that is, HS signal. In this proposed work, an attempt has been made to detect and localize the major heart sounds, that is, S1 and S2. The work in this article consists of three parts. Firstly, self-acquisition of Phonocardiogram (PCG) and Electrocardiogram (ECG) signal through a self-assembled, data-acquisition set-up. The Phonocardiogram (PCG) signal is acquired from all the four auscultation areas, that is, Aortic, Pulmonic, Tricuspid and Mitral on human chest, using electronic stethoscope. Secondly, the major heart sounds, that is, S1 and S2are detected using 3rd Order Normalized Average Shannon energy Envelope (3rd Order NASE) Algorithm. Further, an auto-thresholding has been used to localize time gates of S1 and S2 and that of R-peaks of simultaneously recorded ECG signal. In third part; the successful detection rate of S1 and S2, from self-acquired PCG signals is computed and compared. A total of 280 samples from same subjects as well as from different subjects (of age group 15–30 years) have been taken in which 70 samples are taken from each auscultation area of human chest. Moreover, simultaneous recording of ECG has also been performed. It was analyzed and observed that detection and localization of S1 and S2 found 74% successful for the self-acquired heart sound signal, if the heart sound data is recorded from pulmonic position of Human chest. The success rate could be much higher, if standard data base of heart sound signal would be used for the same analysis method. The, remaining three auscultations areas, that is, Aortic, Tricuspid, and Mitral have smaller success rate of detection of S1 and S2 from self-acquired PCG signals. So, this work justifies that the Pulmonic position of heart is most suitable auscultation area for acquiring PCG signal for detection and localization of S1 and S2 much accurately and for analysis purpose.

scholarly journals Retention Of Cardiac Auscultation Skill Requires Paired Visual And Audio Information In New Learners

2008 ◽  
Vol 2 (2) ◽  
Author(s):  
Glenn Nordehn ◽  
Spencer Strunic ◽  
Tom Soldner ◽  
Nicholas Karlisch ◽  
Ian Kramer ◽  
...  
Keyword(s):  
Visual Information ◽  
Heart Sound ◽  
Heart Sounds ◽  
Sound Recognition ◽  
Cardiac Auscultation ◽  
Time Period ◽  
Significant Difference ◽  
Post Test ◽  
Short Time ◽  
Audio Information

Introduction: Cardiac auscultation accuracy is poor: 20% to 40%. Audio-only of 500 heart sounds cycles over a short time period significantly improved auscultation scores. Hypothesis: adding visual information to an audio-only format, significantly (p<.05) improves short and long term accuracy. Methods: Pre-test: Twenty-two 1st and 2nd year medical student participants took an audio-only pre-test. Seven students comprising our audio-only training cohort heard audio-only, of 500 heart sound repetitions. 15 students comprising our paired visual with audio cohort heard and simultaneously watched video spectrograms of the heart sounds. Immediately after trainings, both cohorts took audio-only post-tests; the visual with audio cohort also took a visual with audio post-test, a test providing audio with simultaneous video spectrograms. All tests were repeated in six months. Results: All tests given immediately after trainings showed significant improvement with no significant difference between the cohorts. Six months later neither cohorts maintained significant improvement on audio-only post-tests. Six months later the visual with audio cohort maintained significant improvement (p<.05) on the visual with audio post-test. Conclusions: Audio retention of heart sound recognition is not maintained if: trained using audio-only; or, trained using visual with audio. Providing visual with audio in training and testing allows retention of auscultation accuracy. Devices providing visual information during auscultation could prove beneficial.

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.

COMPARISON BETWEEN DISCRETE AND PACKET WAVELET TRANSFORM ANALYSES IN THE STUDY OF HEARTBEAT CARDIAC SOUNDS

2007 ◽  
Vol 07 (02) ◽  
pp. 199-214 ◽  
Author(s):  
S. M. DEBBAL ◽  
F. BEREKSI-REGUIG
Keyword(s):  
Wavelet Transform ◽  
Heart Sound ◽  
Wavelet Packet ◽  
Extraction Methods ◽  
Heart Sounds ◽  
Discrete Wavelet ◽  
Clinical Tool ◽  
Prognostic Information ◽  
Transform Method ◽  
Time Frequency

This work investigates the study of heartbeat cardiac sounds through time–frequency analysis by using the wavelet transform method. Heart sounds can be utilized more efficiently by medical doctors when they are displayed visually rather through a conventional stethoscope. Heart sounds provide clinicians with valuable diagnostic and prognostic information. Although heart sound analysis by auscultation is convenient as a clinical tool, heart sound signals are so complex and nonstationary that they are very difficult to analyze in the time or frequency domain. We have studied the extraction of features from heart sounds in the time–frequency (TF) domain for the recognition of heart sounds through TF analysis. The application of wavelet transform (WT) for heart sounds is thus described. The performances of discrete wavelet transform (DWT) and wavelet packet transform (WP) are discussed in this paper. After these transformations, we can compare normal and abnormal heart sounds to verify the clinical usefulness of our extraction methods for the recognition of heart sounds.

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