Rhythmic chaos: irregularities of computer ECG diagnosis

Wang, Laureen Yi-Ting; Seow, Swee‐Chong; Singh, Devinder; Poh, Kian‐Keong; Chai, Ping

doi:10.11622/smedj.2017088

Cited by 1 publication

(1 citation statement)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…One drawback of ECG is the possibility of the signal being corrupted with various types of noise, be it due to the nature of measuring equipment, human physiology or random circumstances. This can cause misdiagnosis and can potentially hinder the efforts of clinicians [4]. A substantial body of literature concerned with denoising strategies, aimed specifically at ECG signals [5], [6], [7] can be attributed to the importance of ECG, especially in clinical settings.…”

Section: Introductionmentioning

confidence: 99%

Effects of denoising strategies on R-wave detection in ECG analysis

Kozłowski

Singh

Ramage

et al. 2021

2021 43rd Annual International Conference of the IEEE Engineering in Medicine &Amp; Biology Society (EMBC)

View full text Add to dashboard Cite

The use of ECG in cardiovascular health monitoring is well established. The signal is collected using specialised equipment, capturing the electrical discharge properties of the human heart. This produces a well-structured signal trace which can be characterised through its peaks and troughs. The signal can then be used by clinicians to diagnose cardiac disorders. However, as with any measuring equipment, the ECG output signal can experience deterioration resulting from noise. This can happen due to environmental interference, human issues or measuring equipment failure necessitating the development of various denoising strategies to reduce, or remove the noise entirely. In this paper, we study typically occurring types of noise and implement popular strategies used to rectify them. We also show, that the given strategy's denoising potential is directly related to R-wave detection, and provide best strategies to apply when faced with specific noise type.

show abstract

Section: Introductionmentioning

confidence: 99%