ArticleSensors (Basel, Switzerland)2019
Real-Time Smart-Digital Stethoscope System for Heart Diseases Monitoring.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07542509 (Digital diagnoSis of Cardiac sOUNd in peDiatric Patients [DI-SOUND Study]), which is not on this map. Cited by 38 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Digital diagnoSis of Cardiac sOUNd in peDiatric Patients [DI-SOUND Study]
Who cites it
38 citing papers in PubMed.
- Rational and design of the digital diagnosis of cardiac sounds in paediatric patients (DI_SOUND) study.European heart journal. Digital health · 2026Article
- How and Why Telehealth Interventions Improve Self-Care Among Vulnerable Groups of Patients With Heart Failure: Scoping Review and Rapid Realist Synthesis.JMIR human factors · 2026Article
- Digital Cardiovascular Twins, AI Agents, and Sensor Data: A Narrative Review from System Architecture to Proactive Heart Health.Sensors (Basel, Switzerland) · 2025Review
- Digital auscultation in clear and present threat of novel respiratory infectious disease: a narrative review.Journal of Yeungnam medical science · 2025Article
- Review
- Automated analysis of heart sound signals in screening for structural heart disease in children.European journal of pediatrics · 2024Article
- Performance discrepancy mitigation in heart disease prediction for multisensory inter-datasets.PeerJ. Computer science · 2024Article
- CARDPSoML: Comparative approach to analyze and predict cardiovascular disease based on medical report data and feature fusion approach.Health science reports · 2024Article
- Wireless broadband acousto-mechanical sensing system for continuous physiological monitoring.Nature medicine · 2023Article
- Exploring Microphone Technologies for Digital Auscultation Devices.Micromachines · 2023Article
- A Wearable Multimodal Wireless Sensing System for Respiratory Monitoring and Analysis.Sensors (Basel, Switzerland) · 2023Article
- Review on the Advancements of Stethoscope Types in Chest Auscultation.Diagnostics (Basel, Switzerland) · 2023Review
- IoT-Enabled Classification of Echocardiogram Images for Cardiovascular Disease Risk Prediction with Pre-Trained Recurrent Convolutional Neural Networks.Diagnostics (Basel, Switzerland) · 2023Article
- Advancement in the Cuffless and Noninvasive Measurement of Blood Pressure: A Review of the Literature and Open Challenges.Bioengineering (Basel, Switzerland) · 2022Review
- Real-world evaluation of the Stemoscope electronic tele-auscultation system.Biomedical engineering online · 2022Article
- Evaluation of the electronic stethoscope (FONODOC) as a cardiac screening tool during the preoperative evaluation of children.Indian journal of anaesthesia · 2022Article
- Smart Consumer Wearables as Digital Diagnostic Tools: A Review.Diagnostics (Basel, Switzerland) · 2022Review
- Fully portable continuous real-time auscultation with a soft wearable stethoscope designed for automated disease diagnosis.Science advances · 2022Article
- QUCoughScope: An Intelligent Application to Detect COVID-19 Patients Using Cough and Breath Sounds.Diagnostics (Basel, Switzerland) · 2022Article
- Thermal Change Index-Based Diabetic Foot Thermogram Image Classification Using Machine Learning Techniques.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
One of the major causes of death all over the world is heart disease or cardiac dysfunction. These diseases could be identified easily with the variations in the sound produced due to the heart activity. These sophisticated auscultations need important clinical experience and concentrated listening skills. Therefore, there is an unmet need for a portable system for the early detection of cardiac illnesses. This paper proposes a prototype model of a smart digital-stethoscope system to monitor patient's heart sounds and diagnose any abnormality in a real-time manner. This system consists of two subsystems that communicate wirelessly using Bluetooth low energy technology: A portable digital stethoscope subsystem, and a computer-based decision-making subsystem. The portable subsystem captures the heart sounds of the patient, filters and digitizes, and sends the captured heart sounds to a personal computer wirelessly to visualize the heart sounds and for further processing to make a decision if the heart sounds are normal or abnormal. Twenty-seven t-domain, f-domain, and Mel frequency cepstral coefficients (MFCC) features were used to train a public database to identify the best-performing algorithm for classifying abnormal and normal heart sound (HS). The hyper parameter optimization, along with and without a feature reduction method, was tested to improve accuracy. The cost-adjusted optimized ensemble algorithm can produce 97% and 88% accuracy of classifying abnormal and normal HS, respectively.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.