Fingertip Based Estimation Of Heart Rate Using Photoplethysmography

Aarthi, Yamuna; Karthikeyan, B.; Raj, Nikhila; Ganesan, M.

doi:10.1109/icaccs.2019.8728432

Cited by 8 publications

(1 citation statement)

References 13 publications

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“…This new filtering approach eliminates baseline wandering sequence and morphological changes in PPG with the help of frequencyselective filters. In estimating heart rate [14] from PPG, the baseline wandering noise is removed more effectively using wavelet decomposition from fingertip PPG. A signal processing method employs comb filter-based wavelet denoising to suppress the motion artifact in PPG as a pre-processing stage [15].…”

Section: Related Workmentioning

confidence: 99%

Pre-processing the Photoplethysmography Signals for Enhancing the Cardiovascular Diseases Detection for Wrist Pulse Analysis in Nadi Ayurveda

Tandon,

Kumar,

Choudhury

2024

EAI Endorsed Trans Perv Health Tech

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INTRODUCTION: In recent years, Photoplethysmography (PPG) signal has played a vital role in detecting Cardiovascular Diseases (CVDs) in case of wrist pulse analysis emulating the Nadi Ayurveda. The PPG signals acquired from the sensor measurement are severely distorted by various artifacts, which significantly impact the accuracy of disease detection and hamper the disease diagnosis process. OBJECTIVES: Removing the noises is essential before detecting CVDs from the signals and thus, developing a simple and effective noise reduction method for enhancing the PPG signal quality constitutes a challenging research problem, particularly with prominent artifacts. METHODS: This paper designs an effective pre-processing technique that improves denoising methods to enhance the PPG signal quality. The design of pre-processing technique contains two major phases: Primary denoising-based artifact removal and secondary denoising-based Premature Ventricular Contraction (PVC) detection and Power-Line Interference (PLI) noise removal. The primary denoising method involves coarse and fine-grained filtering. The coarse-grained filtering removes the major artifacts, such as Baseline Wander (BLW) and Motion Artifacts (MA), by developing the Two-Stage Adaptive Noise Canceller (TANC) method. The fine-grained filtering process utilizes a detrended filter to filter the refined signal obtained from the TANC method. For the signals filtered from the primary denoising method, the secondary denoising method targets to detect the PVC-induced PPG signals from the decomposed high-frequency signals and removes high-frequency noise, such as PLI from noisy signals, by adopting the Wavelet Transform (WT) method. RESULTS: During the signal reconstruction process in the WT method, the research work reconstructs the denoised PPG signals along with the PVC-induced PPG signals. The experimental results of the noise removal methodology illustrated significant improvements in PPG signal quality. CONCLUSION: The designed pre-processing technique effectively denoises PPG signals, leading to enhanced signal quality which can further aid in accurate disease detection.

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Section: Related Workmentioning

confidence: 99%