Detection of Normal and Paradoxical Splitting in Second Heart Sound (S2) using a Wearable Accelerometer Contact Microphone

Brian Sang, Haoran Wen, Pranav Gupta, Arash Shokouhmand, Samiha Khan, Joseph A. Puma, Amisha Patel, Philip Green, Negar Tavassolian, Farrokh Ayazi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

8 Scopus citations

Abstract

Second heart sound (S2) splitting can potentially be used as an indicator for diagnosis of cardiovascular diseases. One example is S2 paradoxical splitting, which can occur when the pulmonic sound (P2) occurs before the aortic sound (A2). However, current means of capturing S2 sounds are not sensitive enough for quantification of A2 and P2. In this paper, we present that S2 's acoustic signatures can be captured accurately using a wearable hermetically sealed sensitive accelerometer contact microphone (ACM) MEMS device. Smoothed Pseudo Wigner-Ville distribution is used to extract the S2 splitting interval. This methodology has been used to capture S2 splitting from patients with varying BMI and used to identify paradoxical splitting from patients with known heart diseases such as aortic stenosis.

Original languageEnglish
Title of host publication2022 IEEE Sensors, SENSORS 2022 - Conference Proceedings
ISBN (Electronic)9781665484640
DOIs
StatePublished - 2022
Event2022 IEEE Sensors Conference, SENSORS 2022 - Dallas, United States
Duration: 30 Oct 20222 Nov 2022

Publication series

NameProceedings of IEEE Sensors
Volume2022-October
ISSN (Print)1930-0395
ISSN (Electronic)2168-9229

Conference

Conference2022 IEEE Sensors Conference, SENSORS 2022
Country/TerritoryUnited States
CityDallas
Period30/10/222/11/22

Keywords

  • Accelerometer Contact Microphone
  • CVD
  • S2 Splitting
  • Time Frequency Representation
  • Wearable

Fingerprint

Dive into the research topics of 'Detection of Normal and Paradoxical Splitting in Second Heart Sound (S2) using a Wearable Accelerometer Contact Microphone'. Together they form a unique fingerprint.

Cite this