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Purpose <p>Heart rate (HR) measurement is essential for assessing physical fitness and diagnosing various diseases. Photoplethysmogram (PPG) is a convenient method for HR estimation due to its simplicity. However, PPG signals recorded from the wrist are highly susceptible to motion artifacts. Traditional noise cancellation techniques, such as adaptive filtering, are sensitive to the reference signal choice. This paper presents a novel approach using switch-mode decomposed gyroscopic and accelerometer signals as reference inputs for a three-stage Least Mean Square (LMS) filter-based noise cancellation system.</p> Methods <p>The signal decomposition is done using Multivariate Variational Mode Decomposition (MVMD), allowing more flexible noise handling. By using decomposed modes as noise references, this method enables separate treatment of different motion artifact components by adjusting the LMS filter parameters, which is not feasible with raw accelerometer or gyroscope data.</p> Results <p>The proposed method was tested on a publicly available PPG dataset with recordings from 24 subjects. The method achieved an Average Absolute Error (AAE) of 2.3, a Standard Deviation of Absolute Error of 2.05, and an Average Relative Error of 4.43%.</p> Conclusion <p>The novel aspect of this proposed scheme is the use of switch mode decomposed reference inputs to the three-stage LMS filter along with a new peak tracking algorithm. The MVMD decomposition technique and three-stage filtering enable us to denoise the PPG signal effectively and achieve an AAE of 2.3, at least 13.86% better than the previous best method.</p>

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Decomposed switch mode signal assisted approach for estimating heart rate from PPG

  • 期刊论文

  • 发表日期：

    2026年5月26日

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  • Kamrul Hasan,
  • Mehdi Hasan Chowdhury,
  • Aditta Chowdhury,
  • Diba Das,
  • Quazi Delwar Hossain

摘要

Purpose

Heart rate (HR) measurement is essential for assessing physical fitness and diagnosing various diseases. Photoplethysmogram (PPG) is a convenient method for HR estimation due to its simplicity. However, PPG signals recorded from the wrist are highly susceptible to motion artifacts. Traditional noise cancellation techniques, such as adaptive filtering, are sensitive to the reference signal choice. This paper presents a novel approach using switch-mode decomposed gyroscopic and accelerometer signals as reference inputs for a three-stage Least Mean Square (LMS) filter-based noise cancellation system.

Methods

The signal decomposition is done using Multivariate Variational Mode Decomposition (MVMD), allowing more flexible noise handling. By using decomposed modes as noise references, this method enables separate treatment of different motion artifact components by adjusting the LMS filter parameters, which is not feasible with raw accelerometer or gyroscope data.

Results

The proposed method was tested on a publicly available PPG dataset with recordings from 24 subjects. The method achieved an Average Absolute Error (AAE) of 2.3, a Standard Deviation of Absolute Error of 2.05, and an Average Relative Error of 4.43%.

Conclusion

The novel aspect of this proposed scheme is the use of switch mode decomposed reference inputs to the three-stage LMS filter along with a new peak tracking algorithm. The MVMD decomposition technique and three-stage filtering enable us to denoise the PPG signal effectively and achieve an AAE of 2.3, at least 13.86% better than the previous best method.