# PPG and Bioimpedance-Based Wearable Applications in Heart Rate Monitoring--A Comprehensive Review
## 5 Heart Rate Monitoring
### Signal Pre-Processing
#### 5.1.3 Motion Artifact Removal  
Motion artifacts (MA) exist in the monitored signals as shown in Figure 10(b), because of the motion-induced interface change between wearable sensors and human skin. Hence, MAs can theoretically be tracked by the change in contact pressure at the interface between human skin and wearable sensor [71]. MA removal is crucial in design of wrist-worn EBI and PPG-based measurement devices for accurate instantaneous HR estimation [71; 72]. The algorithms of MA removal can loosely be classified into two categories--algorithms that require supplementary measured signals and algorithms that do not. Both these types of algorithms are described in the following paragraphs. **Without supplementary signals**  
Independent component analysis (ICA)-based decomposition techniques are commonly used to filter out MAs without the need for any supplementary signal in both the time domain [73] and the frequency domain [74]. Different variations of ICA or combinations with other techniques have also been used. For example, in [73], the ICA was combined together with a block interleaving with low-pass filtering, while in [75], a temporally constrained ICA (cICA) was used in a combination with adaptive filters. The downside of the ICA method is that the ICA provides reliable output only when noise and useful signals are uncorrelated, which in practice is not satisfied. Wavelet transform (WT) operates in the time-frequency domain. However, wavelet threshold along with a choice of the mother wavelet function have to be set, often empirically, limiting the usage of this method [9]. On the other hand, MA removal with adaptive thresholding was carried out in [76]. Authors performed stationary wavelet transform on all segments of the signal. The  
Figure 11: The PPG signal: (**a**) normal; (**b**) disrupted by motion artifacts. Figure 10: Sequence of noise filtering in EBI signals—(**a**) EBI signal as measured; (**b**) high-pass filter with a cut-off of 0.05 Hz; (**c**) low-pass filter with a cut-off of 50 Hz; (**d**) 50 Hz notch filter; (**e**) moving average filter with a sliding window of 60 ms.  
threshold for MA removal was selected by examining a statistical distribution of wavelet transform coefficients in each segment, which was modeled as a mixture of two Gaussians. An MA-free signal was obtained by applying an inverse wavelet transform of the thresholded wavelet transform coefficients. Empirical mode decomposition (EMD) is an adaptive and data-driven algorithm well-suited to non-stationary data that decomposes a time series into multiple intrinsic mode functions (IMFs) [77]. EMD has been used to remove MA from biomedical signals, such as ECG signals [78; 79] and PPG signals [80; 81; 82]. In [81], the authors removed those IMFs whose mean instantaneous frequency was out of the frequency band of PPG. There are several drawbacks to EMD:  
* Sensitivity to noise in the recorded signals. * Mode mixing. This has been addressed with the ensemble empirical mode decomposition (EEMD) method [82], where the true IMF components are chosen as the mean of an ensemble of trials. Each trial consists of the signal plus an addition of white noise [83]. * Aliasing [9]. * Trend uncertainty [9]. * The method and its variations are based on empirical and heuristic procedures; thus, interpretability is its weakness [84]. * EMD and especially EEMD have a significant computational cost. A fast version of EEMD (FEEMD) was proposed in [85] lessening the computational burden of the original EEMD. Another drawback of EMD (and its variations) is that no rigorous guidelines on the choice of relevant IMF exist as the whole EMD procedure is empirical in nature. An example of extracted IMFs from a PPG signal is shown in Figure 12. The authors of [69] speculated that the IMFs related to noise and MAs are of high frequency, while low-frequency IMFs are related to low oscillations, so the trend and both of these groups of IMFs should be removed.