scholarly journals Development of a Finger-Ring-Shaped Hybrid Smart Stethoscope for Automatic S1 and S2 Heart Sound Identification

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6294
Author(s):  
Soomin Lee ◽  
Qun Wei ◽  
Heejoon Park ◽  
Yuri Na ◽  
Donghwa Jeong ◽  
...  
Keyword(s):  
Heart Sound ◽  
Heart Sounds ◽  
Experimental Results ◽  
Time Interval ◽  
Signal Acquisition ◽  
Cardiac Auscultation ◽  
Sound Analysis ◽  
Physiological Signal ◽  
Ring Shape ◽  
Prototype Device

Cardiac auscultation is one of the most popular diagnosis approaches to determine cardiovascular status based on listening to heart sounds with a stethoscope. However, heart sounds can be masked by visceral sounds such as organ movement and breathing, and a doctor’s level of experience can more seriously affect the accuracy of auscultation results. To improve the accuracy of auscultation, and to allow nonmedical staff to conduct cardiac auscultation anywhere and anytime, a hybrid-type personal smart stethoscope with an automatic heart sound analysis function is presented in this paper. The device was designed with a folding finger-ring shape that can be worn on the finger and placed on the chest to measure photoplethysmogram (PPG) signals and acquire the heart sound simultaneously. The measured heart sounds are detected as phonocardiogram (PCG) signals, and the boundaries of the heart sound variation and the peaks of the PPG signal are detected in preprocessing by an advanced Shannon entropy envelope. According to the relationship between PCG and PPG signals, an automatic heart sound analysis algorithm based on calculating the time interval between the first and second heart sounds (S1, S2) and the peak of the PPG was developed and implemented via the manufactured prototype device. The prototype device underwent accuracy and usability testing with 20 young adults, and the experimental results showed that the proposed smart stethoscope could satisfactorily collect the heart sounds and PPG signals. In addition, within the developed algorithm, the device was as accurate in start-points of heart sound detection as professional physiological signal-acquisition systems. Furthermore, the experimental results demonstrated that the device was able to identify S1 and S2 heart sounds automatically with high accuracy.

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.

Heart Sound Signal Generator Based on LabVIEW

2011 ◽  
Vol 121-126 ◽  
pp. 872-876
Author(s):  
Ye Wei Tao ◽  
Xie Feng Cheng ◽  
Shu Yang He ◽  
Yan Ping Ge ◽  
Yan Hong Huang
Keyword(s):  
Heart Sound ◽  
Signal Generator ◽  
Heart Sounds ◽  
Sound Signal ◽  
Sound Analysis ◽  
Analysis Instrument ◽  
Heart Sound Signal

A heart sounds signal generator in the heart sound analysis instrument based on the LabVIEW is devised. The instrument is developed in PC. Heart sounds signal generator can according to need to produce a synthetic heart sounds signal for users to learn and use. The parameters setting are also discussed to find out the best for the each part. All the parameters can be set by user and the best ones are default values so that the instrument can fit other environment. The running test of this instrument proves it can generate and play heart sound precisely，and can be used as an assistance to show, play, and analyze heart sound

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

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 Design of Pulse Wave Feature Vigilance Detection System Based on Computer Software Technology

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Yidong Zeng ◽  
Jun Ji ◽  
Jinghua Wang ◽  
Jiasuo Gao ◽  
Jie Hu ◽  
...  
Keyword(s):  
Pulse Wave ◽  
Detection System ◽  
Skin Resistance ◽  
Computer Software ◽  
Pulse Amplitude ◽  
Acquisition System ◽  
Signal Acquisition ◽  
Blood Oxygen Saturation ◽  
Software Technology ◽  
Physiological Signal

In this paper, the pulse wave feature alertness detection system based on computer software technology is researched. First, the computer software technology designs the alertness detection system and then conducts the system alertness test experiment using a system that can not affect the subjects’ alertness, a portable multichannel physiological signal acquisition system that measures the subjects’ ECG signal, skin resistance, blood oxygen saturation, and other physiological signals in the case of a degree task experiment. The multichannel physiological signal acquisition system collects the signals during the vigilance task experiment. At the same time, before, during, and after the experiment, subjects are required to fill in the Stanford Sleepiness Scale (SSS) and evaluate the level of individual alertness through subjective self-evaluation. The relevant experimental data show that, 10 minutes before the experiment, the pulse amplitude increased rapidly, then slowly decreased at the beginning, reached a peak in about 25 minutes, and then began to rise.

scholarly journals Age of puppies at referral to veterinary cardiology specialists for murmur investigation

2021 ◽  
Vol 63 (1) ◽  
Author(s):  
Lynn Bernadette Rovroy ◽  
Viktor Szatmári
Keyword(s):  
Medical Records ◽  
Digital Archive ◽  
Cardiac Anomaly ◽  
Time Interval ◽  
Health Examination ◽  
Cardiac Auscultation ◽  
Congenital Cardiac Anomaly ◽  
Cardiac Anomalies ◽  
Veterinary Practitioner ◽  
Congenital Cardiac Anomalies

Abstract Background Cardiac auscultation is an important screening test at the first health examination of puppies because most clinically relevant congenital cardiac anomalies cause a loud murmur from birth. This retrospective study aimed to investigate the age at which dogs with suspected congenital cardiac anomalies were referred to a veterinary cardiology specialist for murmur investigation. A secondary aim was to establish the time interval between the visit to the cardiologist and the first available murmur documentation. The digital archive of a veterinary teaching hospital was searched for dogs with congenital cardiac anomalies and puppies with innocent murmurs during a 5-year period. Dogs had to be referred because of a murmur, and they had to undergo physical examination and echocardiography by a veterinary cardiology specialist. The health certificate section of the pet passport, and the medical records from the referring veterinarian, were reviewed to identify the date when the murmur was first documented. Results Of the 271 included dogs, 94% had a congenital cardiac anomaly and 6% had an innocent murmur. The dogs’ median age was 190 days when they were examined by the cardiologist. Only 10% of the dogs were referred by the breeder’s veterinarian, while 90% of the dogs were referred by the new owner’s veterinarian. The median age of the first available murmur documentation by a first opinion veterinary practitioner was 95 days. Conclusions Only 10% of the puppies in the present study were referred to a veterinary cardiology specialist for murmur investigation before they were sold to a new owner. Referral prior to re-homing would have been feasible if the murmur had been detected and documented by the breeder’s veterinarian, if referral was offered by the breeder’s veterinarian and the referral was accepted by the breeder.

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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