This paper is in the following e-collection/theme issue:

mHealth in a Clinical Setting (615) mHealth for Data Collection and Research (955) Novel Sensors and Data Acquisition Methods in Cardiology (196)

Published on in Vol 6, No 2 (2018): February

Cardiac Auscultation Using Smartphones: Pilot Study

Cardiac Auscultation Using Smartphones: Pilot Study

Cardiac Auscultation Using Smartphones: Pilot Study

Authors of this article:

Si-Hyuck Kang1 Author Orcid Image ;   Byunggill Joe2 Author Orcid Image ;   Yeonyee Yoon1 Author Orcid Image ;   Goo-Yeong Cho1 Author Orcid Image ;   Insik Shin2 Author Orcid Image ;   Jung-Won Suh3 Author Orcid Image
Article Authors Cited by (25) Tweetations (65) Metrics

Journals

  1. Tobore I, Li J, Yuhang L, Al-Handarish Y, Kandwal A, Nie Z, Wang L. Deep Learning Intervention for Health Care Challenges: Some Biomedical Domain Considerations. JMIR mHealth and uHealth 2019;7(8):e11966 View
  2. Huynh M. Smartphone-Based Device in Exotic Pet Medicine. Veterinary Clinics of North America: Exotic Animal Practice 2019;22(3):349 View
  3. Giebel G, Gissel C. Accuracy of mHealth Devices for Atrial Fibrillation Screening: Systematic Review. JMIR mHealth and uHealth 2019;7(6):e13641 View
  4. Jones B, Reed B, Hayanga J. Autonomously Driven: Artificial Intelligence in Cardiothoracic Surgery. The Annals of Thoracic Surgery 2020;110(2):373 View
  5. Yang C, Zhang W, Pang Z, Zhang J, Zou D, Zhang X, Guo S, Wan J, Wang K, Pang W. A Low-Cost, Ear-Contactless Electronic Stethoscope Powered by Raspberry Pi for Auscultation of Patients With COVID-19: Prototype Development and Feasibility Study. JMIR Medical Informatics 2021;9(1):e22753 View
  6. JUSAK J, PUSPASARI I, KUSUMAWATI W. A Semi-automatic Heart Sounds Identification Model and Its Implementation in Internet of Things Devices. Advances in Electrical and Computer Engineering 2021;21(1):45 View
  7. Jeong Y, Kim J, Kim D, Kim J, Lee K. Methods for Improving Deep Learning-Based Cardiac Auscultation Accuracy: Data Augmentation and Data Generalization. Applied Sciences 2021;11(10):4544 View
  8. Jani V, Danford D, Thompson W, Schuster A, Manlhiot C, Kutty S. The discerning ear: cardiac auscultation in the era of artificial intelligence and telemedicine. European Heart Journal - Digital Health 2021;2(3):456 View
  9. Park J, Kim K, Kim J, Choi Y, Kim K, Suh D. A machine learning approach to the development and prospective evaluation of a pediatric lung sound classification model. Scientific Reports 2023;13(1) View
  10. Mazzu-Nascimento T, Evangelista D, Abubakar O, Roscani M, Aguilar R, Chachá S, Rosa P, Silva D. Smartphone-Based Screening for Cardiovascular Diseases: A Trend?. International Journal of Cardiovascular Sciences 2021;35(1):127 View
  11. Jiao Y, Yuan J, Sodimu O, Qiang Y, Ding Y. Deep Neural Network-Aided Histopathological Analysis of Myocardial Injury. Frontiers in Cardiovascular Medicine 2022;8 View
  12. Luo H, Lamata P, Bazin S, Bautista T, Barclay N, Shahmohammadi M, Lubrecht J, Delhaas T, Prinzen F. Smartphone as an electronic stethoscope: factors influencing heart sound quality. European Heart Journal - Digital Health 2022;3(3):473 View
  13. Shariat Panah D, Hines A, McKeever J, McKeever S. An audio processing pipeline for acquiring diagnostic quality heart sounds via mobile phone. Computers in Biology and Medicine 2022;145:105415 View
  14. Khalilian M, Safari M, Hajipour M, Rahmani K, Safari M, Ahmadpour M, Tahouri T. Evaluation of the heart sounds in children using a Doppler Phonolyser. BioMedical Engineering OnLine 2023;22(1) View
  15. Azmeen A, Vakilzadian H, Haider H, Mathers D, Zimmerman R, Bedi S, O’Leary E. Heart sounds: Past, present, and future from a technological and clinical perspective – a systematic review. Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine 2023;237(6):669 View
  16. Wienhold J, Kemper I, Czaplik M, Follmann A, Rossaint R, Derwall M. Anästhesiologische Risikoevaluation und rechtssichere Aufklärung per Telemedizin – Sind wir bereit für einen Paradigmenwechsel?. Die Anaesthesiologie 2023;72(10):697 View
  17. Huecker M, Schutzman C, French J, El-Kersh K, Ghafghazi S, Desai R, Frick D, Thomas J. Accurate Modeling of Ejection Fraction and Stroke Volume With Mobile Phone Auscultation: Prospective Case-Control Study. JMIR Cardio 2024;8:e57111 View
  18. Huang Y, Song I. Indexing ECG for Integrated Health Social Networks Predicting Keywords from ECG to Access Online Information. SN Computer Science 2024;5(5) View
  19. Nedadur R, Bhatt N, Liu T, Chu M, McCarthy P, Kline A. The Emerging and Important Role of Artificial Intelligence in Cardiac Surgery. Canadian Journal of Cardiology 2024;40(10):1865 View
  20. Echenique M, Godoy E, Cádiz R, Andia M. Enhancement of cardiac and respiratory sounds for cellphone reproduction by means of digital sound processing methods. Personal and Ubiquitous Computing 2024;28(5):845 View
  21. Partovi E, Babic A, Gharehbaghi A. A review on deep learning methods for heart sound signal analysis. Frontiers in Artificial Intelligence 2024;7 View

Books/Policy Documents

  1. Kaboudian A, Velasco-Perez H, Iravanian S, Shiferaw Y, Cherry E, Fenton F. From Reactive Systems to Cyber-Physical Systems. View