SPECMAR: fast heart rate estimation from PPG signal using a modified spectral subtraction scheme with composite motion artifacts reference generation

Abstract

The task of heart rate estimation using photoplethysmographic (PPG) signal is challenging due to the presence of various motion artifacts in the recorded signals. In this paper, a fast algorithm for heart rate estimation based on modified SPEctral subtraction scheme utilizing Composite Motion Artifacts Reference generation (SPECMAR) is proposed using two-channel PPG and three-axis accelerometer signals. First, the preliminary noise reduction is obtained by filtering unwanted frequency components from the recorded signals. Next, a composite motion artifacts reference generation method is developed to be employed in the proposed SPECMAR algorithm for motion artifacts reduction. The heart rate is then computed from the noise and motion artifacts reduced PPG signal. Finally, a heart rate tracking algorithm is proposed considering neighboring estimates. The performance of the SPECMAR algorithm has been tested on publicly available PPG database. The average heart rate estimation error is found to be 2.09 BPM on 23 recordings. The Pearson correlation is 0.9907. Due to low computational complexity, the method is faster than the comparing methods. The low estimation error, smooth and fast heart rate tracking makes SPECMAR an ideal choice to be implemented in wearable devices.

Combining Parallel Adaptive Filtering and Wavelet Threshold Denoising for Photoplethysmography-Based Pulse Rate Monitoring during Intensive Physical Exercise

“…In Table 4, the LOA of our algorithm is [−4.30, 4.18] BPM, and 95% of difference values are within this confidence interval, which indicates that the ground truth and estimation of PR values are in good consistency. The Scatter plot objectively measures the fitting degree between the estimated and the ground truth PR values [27]. It is observed from the Fig.…”

“…[12], [13], and [17]. It also costs approximate time to the spectral subtraction [16] and [27], which have been recognized as the fastest method. The less computation time costs, the easier it is to integrate the algorithm into the wearable devices.…”

Rapid Vital Sign Extraction for Real-Time Opto-Physiological Monitoring at Varying Physical Activity Intensity Levels

“…Such DL system generally requires motion-free signals as input, from which the network tries to extract a wide range of physiological biomarkers [34], [35]. The proposed MR method can extract target PPG signals, as the reference signal for such DL networks, more rapidly than other traditional and more complex methods, i.e., TROIKA [15], JOSS [31] and SPECMAR [32].…”

Novel wearable and contactless monitoring devices to identify deteriorating patients in the clinical setting: a systematic review protocol

“…The generally accepted clinical standard for heart rate monitoring using devices is the use of ECG monitoring [12] or pulse frequency from a photoplethysmogram [13]. Respiratory rate monitoring in patients requiring continuous monitoring is typically performed via thoracic bioimpedance from ECG leads or capnography [14].…”