Analysis of photoplethysmogram signal to estimate heart rate during physical activity using fractional fourier transform – A sampling frequency independent and reference signal-less method. (February 2023)

• Record Type: Journal Article
• Authors: Pankaj, ; Kumar, Ashish; Ashdhir, Aryaman; Komaragiri, Rama; Kumar, Manjeet
• Is Part Of: Computer methods and programs in biomedicine. Volume 229(2023)
• Publication Date: 2023-02
• Subjects: Photoplethysmogram (PPG) -- Wearable device -- Heart rate estimation -- Heart rate tracking -- Fractional fourier transform; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2022.107294
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Fractional fourier transform (FrFT) to effectively suppress the motion artifacts in a photoplethysmogram (PPG) signal for an accurate estimation of heart rate (HR). The proposed work determines an optimal value of the fractional order of the proposed FrFT. The effectiveness of the proposed algorithm is tested using IEEE SPC (training + test) challenge database. The proposed FrFT based algorithm doesn't require additional reference accelerometers. Abstract: Background and objective: Acquiring accurate and reliable health information using a PPG signal in wearable devices requires suppressing motion artifacts. This paper presents a method based on the Fractional Fourier transform (FrFT) to effectively suppress the motion artifacts in a Photoplethysmogram (PPG) signal for an accurate estimation of heart rate (HR). Methods: By analyzing various PPG signals recorded under various physiological conditions and sampling frequencies, the proposed work determines an optimal value of the fractional order of the proposed FrFT. The proposed FrFT-based algorithm separates the motion artifacts component from the acquired PPG signal. Finally, the HR estimation accuracy during the strong motion artifact-affected windows is improved using a post-processing technique. The efficacy of the proposed method is evaluated by computing the root mean square error (RMSE). Results: The performance of the proposed algorithm is compared with methods in recent studies using test and training datasets from the IEEE Signal Processing Cup (SPC). The proposed method provides the mean absolute error of 1.88 beats per minute (BPM) on all twenty-three recordings. Conclusions: The proposed method uses the Fourier method in the fractional domain. A noisy signal is rotated into an intermediate plane between the time and frequency domains to separate the signal from the noise. The algorithm incorporates FrFT analysis to suppress motion artifacts from PPG signals to estimate HR accurately. Further, a post-processing step is used to track the HR for accurate and reliable HR estimation. The proposed FrFT-based algorithm doesn't require additional reference accelerometers or hardware to estimate HR in real-time. The noise and signal separation is optimum for a fractional order ( a ) value in the vicinity of 0.6. The optimized value of fractional order is constant irrespective of the physical activity and sampling frequency.