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

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

On-device artificial intelligence: mobile solution for detecting severe aortic valve stenosis based on heart sounds

Background Aortic stenosis is still one of the major causes of sudden cardiac death in the elderly. Noninvasive screening for severe aortic valve stenosis (AS) may result in early cardiac diagnostic leading to an appropriate and timely medical intervention. Purpose The aims of this study were 1) to develop an artificial intelligence to detect severe AS based on heart sounds and 2) to build an application to screen patients using electronic stethoscope and smartphones, which will provide an efficient diagnostic workflow for screening as a complementary tool in daily clinical practice. Methods We enrolled 100 patients diagnosed with severe AS and 200 patients without severe AS (no echocardiographic sign of AS [n=100], mild AS [n=50], moderate AS [n=50]). The heart sounds were recorded in 4000 Hz waveform audio format at the following 3 sites of each patient; the 2nd intercostal right sternal border, the Erb's area and the apex. Each record was divided into multiple data of 4 seconds duration, which built 10800 sound records in total. We developed multiple convolutional neural networks (CNN) designed to recognize severe AS in heart sounds according to the recorded 3 sites. We adopted a stratified 4-fold cross-validation method by which the CNN was trained with 60% of the whole data, validated with 20% data and tested with the remaining 20% data not used during training and validation. As performance metrics we adopted the accuracy, F1 value and the area under the curve (AUC) calculated as the average of all cross-validation folds. For the smartphone application, we combined the best CNN-models from each recorded site for the best performance. Further 40 patients were newly enrolled for its clinical validation (no AS [n=10], mild AS [n=10], moderate AS [n=10], severe AS [n=10]). Results The accuracy, F1 value and AUC of each model were 88.9±5.7%, 0.888±0.006 and 0.953±0.008, respectively. The sensitivity and specificity were 87.9±2.2% and 89.9±2.4%. The recognition accuracy of moderate AS was significantly lower as compared to the other AS grades (moderate AS 74.1±6.1% vs no AS 98.0±1.4%, mild AS 97.6±1.2%, severe AS 87.9±2.2%, respectively, P<0.05). Our smartphone application showed a sensitivity of 100% (10/10), a specificity of 73.3% (22/30), and an accuracy of 80.0% (32/40), which implicated a good utility for screening. In the detailed analysis of 8 mistaken decisions, these were highly affected by the presence of severe mitral or tricuspid valve regurgitation despite of non-severe AS (7/8 [87.5%]). Conclusions This study demonstrated the promising possibility of an end-to-end screening for severe aortic valve stenosis using an electronic stethoscope and a smartphone application. This technology may improve the efficacy of daily medicine particularly where the human resource is limited or support a remote medical consultation. Further investigations are necessary to increase accuracy. Funding Acknowledgement Type of funding sources: None.

A Novel Broadband Forcecardiography Sensor for Simultaneous Monitoring of Respiration, Infrasonic Cardiac Vibrations and Heart Sounds

The precordial mechanical vibrations generated by cardiac contractions have a rich frequency spectrum. While the lowest frequencies can be palpated, the higher infrasonic frequencies are usually captured by the seismocardiogram (SCG) signal and the audible ones correspond to heart sounds. Forcecardiography (FCG) is a non-invasive technique that measures these vibrations via force sensing resistors (FSR). This study presents a new piezoelectric sensor able to record all heart vibrations simultaneously, as well as a respiration signal. The new sensor was compared to the FSR-based one to assess its suitability for FCG. An electrocardiogram (ECG) lead and a signal from an electro-resistive respiration band (ERB) were synchronously acquired as references on six healthy volunteers (4 males, 2 females) at rest. The raw signals from the piezoelectric and the FSR-based sensors turned out to be very similar. The raw signals were divided into four components: Forcerespirogram (FRG), Low-Frequency FCG (LF-FCG), High-Frequency FCG (HF-FCG) and heart sounds (HS-FCG). A beat-by-beat comparison of FCG and ECG signals was carried out by means of regression, correlation and Bland–Altman analyses, and similarly for respiration signals (FRG and ERB). The results showed that the infrasonic FCG components are strongly related to the cardiac cycle (R2 > 0.999, null bias and Limits of Agreement (LoA) of ± 4.9 ms for HF-FCG; R2 > 0.99, null bias and LoA of ± 26.9 ms for LF-FCG) and the FRG inter-breath intervals are consistent with ERB ones (R2 > 0.99, non-significant bias and LoA of ± 0.46 s). Furthermore, the piezoelectric sensor was tested against an accelerometer and an electronic stethoscope: synchronous acquisitions were performed to quantify the similarity between the signals. ECG-triggered ensemble averages (synchronized with R-peaks) of HF-FCG and SCG showed a correlation greater than 0.81, while those of HS-FCG and PCG scored a correlation greater than 0.85. The piezoelectric sensor demonstrated superior performances as compared to the FSR, providing more accurate, beat-by-beat measurements. This is the first time that a single piezoelectric sensor demonstrated the ability to simultaneously capture respiration, heart sounds, an SCG-like signal (i.e., HF-FCG) and the LF-FCG signal, which may provide information on ventricular emptying and filling events. According to these preliminary results the novel piezoelectric FCG sensor stands as a promising device for accurate, unobtrusive, long-term monitoring of cardiorespiratory functions and paves the way for a wide range of potential applications, both in the research and clinical fields. However, these results should be confirmed by further analyses on a larger cohort of subjects, possibly including also pathological patients.

Classification of Heart Sounds Associated With Murmur For Diagnosis of Cardiac Valve Disorders

Background: Now a day, cardiovascular diseases have been a major cause of death in the world. The heart sound is still the primary tool used for screening and diagnosing many pathological conditions of the human heart. The abnormality in the heart sounds starts appearing much earlier than the symptoms of the disease. In this study, the Phonocardiography signal has been studied and classified into three classes, namely normal signal, murmur signal and extra sound signal. A total of 15 features from different domains have been extracted and then reduced to 7 features. The features have been selected on the basis of correlation based feature selection technique. The selected features are used to classify the signal into the predefined classes using multi- class SVM classifier. The performance of the proposed denoising algorithm is evaluated using the signal to noise ratio, percentage root means square difference, and root mean square error. For this work a publically available database for researchers, Partnership Among South Carolina Academic Libraries (PASCAL) and MATLAB 2018a was used to develop the proposed algorithm.Results: Our experimental result shows that the 4th level of decomposition for the Db10 wavelets shows the highest SNR values when using the soft and hard thresholding. The overall accuracy, Sensitivity and Specificity of the developed algorithm is 97.96%, 97.92 % and of 98.0% respectively.Conclusion: even if the proposed algorithm is useful for murmur detection mainly valve-related diseases and the efficiency of the proposed study is increased, future work will intend to generalize the algorithm by using hybrid classifiers on a larger dataset. Since all experiments used the PASCAL datasets, additional experiments will be needed using new datasets to be implemented using the latest mobile phones which can work as an electronic stethoscope or phonocardiogram. In addition, the case of continuous murmur and types of murmur has been included for classification in further studies.

A Novel Method of Temporomandibular Joint Hypermobility Diagnosis Based on Signal Analysis

Despite the temporomandibular joint (TMJ) being a well-known anatomical structure its diagnosis may become difficult because physiological sounds accompanying joint movement can falsely indicate pathological symptoms. One example of such a situation is temporomandibular joint hypermobility (TMJH), which still requires comprehensive study. The commonly used official research diagnostic criteria for temporomandibular disorders (RDC/TMD) does not support the recognition of TMJH. Therefore, in this paper the authors propose a novel diagnostic method of TMJH based on the digital time–frequency analysis of sounds generated by TMJ. Forty-seven volunteers were diagnosed using the RDC/TMD questionnaire and auscultated with the Littmann 3200 electronic stethoscope on both sides of the head simultaneously. Recorded TMJ sounds were transferred to the computer via Bluetooth® for numerical analysis. The representation of the signals in the time–frequency domain was computed with the use of the Python Numpy and Matplotlib libraries and short-time Fourier transform. The research reveals characteristic time–frequency features in acoustic signals which can be used to detect TMJH. It is also proved that TMJH is a rare disorder; however, its prevalence at the level of around 4% is still significant.

The Utility of Real-Time Remote Auscultation Using an Internet-Connected Electronic Stethoscope: An Open-Label Randomized Controlled Pilot Trial

Objective: This study aimed to assess the utility of real-time remote auscultation using the cardiopulmonary simulators.Methods: In this open-label, randomized controlled trial, the researchers randomly assigned general internal medicine doctors to the real-time remote auscultation group (intervention group) or the classical auscultation group (control group). In the training session, participants listened to five different lung sounds and five cardiac sounds in a previously determined order with the correct classification. In the test session, participants had to classify the five lung sounds and five cardiac sounds in random order. For both sessions, the intervention group auscultated at a distance of 220 m, with an Internet-connected electronic stethoscope while watching the auscultation places on the computer screen. The control group performed direct auscultation using a classical stethoscope. The primary outcome was the total test score.Results: Twenty participants were included in the study. The total test scores of lung auscultation in the intervention (86%) and control (90%) groups were not significantly different (P = .54). The total test score of cardiac auscultation in the control group (94%) was superior to that in the intervention group (72%, P < .05). Valvular diseases were not misclassified as normal sounds in real-time remote cardiac auscultation. Discussion and Conclusions: The utility of real-time remote lung auscultation using an Internet-connected electronic stethoscope was comparable to that of classical lung auscultation. Classical cardiac auscultation was superior to real-time remote cardiac auscultation. However, real-time remote cardiac auscultation is useful for classifying valvular diseases and normal sounds. Trial Registration: UMIN-CTR UMIN000043153; https://upload.umin.ac.jp/cgi-open-bin/ctr/ctr_view.cgi?recptno=R000049259 The date of first registration:28/01/2021