M. Raghuram

Evaluation of wavelets for reduction of motion artifacts in photoplethysmographic signals

Photoplethysmographic (PPG) signals obtained at Red and Infrared wavelengths are utilized in pulse oximetry for estimation of arterial blood oxygen saturation (SpO2). Mostly inaccurate readings in a pulse oximeter arise when PPG signals are contaminated with motion artifacts (MA) due to the movement of patient and hence MA are a common cause of oximeter failure and loss of accuracy. This paper presents performance evaluation of different wavelets for the reduction of MA. Test results on the PPG signals recorded with frequently encountered artifacts (viz., horizontal, vertical and bending motions of finger) reveal that the estimated Spo2 values from MA reduced PPGs using different wavelets are very close to each other. In addition, the Daubechies wavelet interestingly kept the respiratory information intact while effectively removing the MA from PPG signals. Hence, the work establishes that the Daubechies wavelet is the most preferred wavelet for pulse oximetry applications.

On the Performance of Wavelets in Reducing Motion Artifacts from Photoplethysmographic Signals

In a pulse oximeter, clean artifact-free photoplethysmographic (PPG) signals with clearly separable DC and AC parts are necessary for error-free estimation of arterial oxygen saturation (SpO2). Motion artifacts (MA) introduced in the PPG signals due to the movement of a patient result in a significant error in the readings of pulse oximeters and hence are a common cause of oximeter failure and loss of accuracy. In this paper, we present a comparative analysis of using different wavelets for the case of MA reduction from corrupted PPG signals. PPG signals with frequently encountered artifacts (horizontal, vertical and bending motions of finger) were recorded from the subjects and processed with different wavelets. Simulation results and statistical analysis revealed that the Daubechies wavelet performed well for obtaining clean motion artifact-free PPG signal. Interestingly, the wavelet based method proved to be efficient in reducing MA restoring the respiratory information in tact with the PPG.

Acoustic echo cancellation using a computationally efficient transform domain LMS adaptive filter

Applications such as hands-free telephony, tele-classing and video-conferencing require the use of an acoustic echo canceller (AEC) to eliminate acoustic feedback from the loudspeaker to the microphone. Room acoustic echo cancellation typically requires adaptive filters with thousands of coefficients. Transform domain adaptive filter finds best solution for echo cancellation as it results in a significant reduction in the computational burden. Literature finds different orthogonal transform based adaptive filters for echo cancellation. In this paper, we present Hirschman Optimal Transform (HOT) based adaptive filter for elimination of echo from audio signals. Simulations and analysis show that HOT based LMS adaptive filter is computationally efficient and has fast convergence compared to LMS, NLMS and DFT-LMS. The computed Echo Return Loss Enhancement (ERLE), the general evaluation measure of echo cancellation, established the efficacy of proposed HOT based adaptive algorithm. In addition, the spectral flatness measure showed a significant improvement in cancelling the acoustic echo.

Monitoring Respiratory Activity Using PPG Signals by Order Reduced-Modified Covariance AR Technique

The clinical significance of certain cardiac arrhythmias can be understood only with reference to respiration. In normal healthy conditions, the respiratory rate is 10-20 breaths/minute. But, certain problems of illness, accidents or some other causes affect the regular sinus rhythm. A non-invasive, non-occlusive and non-intrusive respiration monitoring is desirable in a number of situations such as ambulatory monitoring, stress tests and sleep disorder investigations. Such methods are based on deriving the respiratory activity from the electrocardiogram (ECG). There have been several efforts on ECG-Derived Respiration (EDR). Presently research is being focused on PPG derived respiratory activity because of its simplicity in sensing the signal and proved a strong correlation between PPG and respiratory signals. In this paper, we present an efficient method for extraction of respiratory activity from photoplethysmographic (PPG) signals based on modified covariance method. The proposed method makes use of order reduced AR-model by restricting the pole locations in the frequency range of interest. Test results reveal that the order reduced-modified covariance AR model (OR-MCAR) has efficiently separated respiratory information from PPG than a normal AR model.

Dual-tree complex wavelet transform for motion artifact reduction of PPG signals

Ever since the medical device pulse oximeter was invented, reliable and accurate estimation of arterial blood oxygen saturation (SpO2), based on the differential absorption of red/infrared light by hemoglobins, has been a challenging task. The Photoplethysmogram (PPG) waveform, also known as the “pulse oximetry waveform”, is well recognized for its use in pulse oximetry applications for the estimation of SpO2 and can be obtained noninvasively and continuously in a comfortable manner using low cost & portable PPG sensors. Inaccuracy in the estimation of SpO2 may prevail due to the motion artifacts (MA) corruption in the detected PPG signals by the intentional or unintentional movements of a patient. The MA noise corruption is unavoidable while recording the PPGs because of a very small pulsatile component in PPG (0.1% of total signal amplitude) and it can be reduced by suitable processing of the PPG signals. In this paper, an approach for motion artifact (MA) reduction of photoplethysmographic (PPG) signals based on the concept of dual-tree complex wavelet transform technique is proposed. Experimental results revealed that DTCWT processing of MA corrupted PPGs outperformed the db10 wavelet processing for MA reduction of PPG signals and can be referred as best suitable MA reduction technique for pulse oximetry applications.

Use of multi scale PCA for extraction of respiratory activity from photoplethysmographic signals

The fact that the photoplethysmographic (PPG) signal caries respiratory information in addition to arterial blood oxygen saturation attracted the researchers to extract the respiratory information from it. In this current work, we present an efficient algorithm, based on the multi scale principal component analysis (MSPCA) technique to extract the respiratory activity from the PPG signals. MSPCA is a powerful combination of wavelets and principal component analysis (PCA). In MSPCA technique, PCA is used in computing coefficients of wavelet at each scale, and finally combining all the results at relevant scales. Experiments carried on the data records drawn from the MIMIC database of Physionet archives revealed a very high degree of coherence between the PPG derived respiratory (PDR) signal and the recorded respiratory signal. Results demonstrated that MSPCA performed exceptionally well for extraction of respiratory activity from PPG signals with high correlation coefficient and accuracy rates of above 98%.

HHT based signal decomposition for reduction of motion artifacts in photoplethysmographic signals

Motion artifact (MA) corrupted photoplethysmographic (PPG) signals are the main source of errors in the estimation of arterial blood oxygen saturation (SpO2) in pulse oximeters. For addressing the issue of MA reduction in pulse oximetry applications, the physical origins of PPG signals are to be explored and effective signal processing technique may be employed. In this paper, we propose simple and efficient empirical mode decomposition (EMD) method based on the Hilbert-Huang Transform (HHT) for MA reduction in PPG signals. EMD is relatively a new time-frequency analysis technique having wide range of applications. EMD uses HHT calculation to handle non-linear and non-stationary data to find the intrinsic mode function (IMF) components and analyze the variations in power spectrum over time. The efficacy of the proposed method is proved by comparing it with well known wavelet transform based MA reduction method for the PPG data recorded with different MA (Horizontal, Vertical and Bending motion of finger). While statistical analysis demonstrated the robustness of the method, the SpO2 estimations from MA reduced PPG signals by proposed method being very close to the actual ones, make it reliable for pulse oximetry applications.

Extraction of respiratory activity from ECG and PPG signals using vector autoregressive model

Respiratory signal is usually recorded with techniques like spirometry, pneumography or whole body plethysmography. These techniques require the use of cumbersome devices that may interfere with natural breathing, unmanageable in certain applications such as ambulatory monitoring, stress testing, and sleep studies. Infact, the joint study of cardiac and pulmonary systems is of great interest in most of these applications. Particularly the methods for extraction of respiratory information from physiological signals are attractive to pursue. In this present work we are addressing a method for extraction of respiratory activity from commonly available physiological signals such as ECG and Photoplethysmogram (PPG) using vector auto regressive (VAR) modelling technique. To test the efficacy of the proposed technique, the method is applied on a set of fifteen data records with different breathing rates and respiration amplitudes of physiobank archive for extraction of respiratory activity from the ECG and PPG signals. Extracted respiratory signal using the proposed bivariate VAR model is compared with the original respiratory signal present in the record and is considered as reference signal for comparison. Correlation analysis done in both frequency and time domains has shown a high degree of acceptance for the extracted respiratory signal with respect to the original reference respiratory signal. Higher values of accuracy rate clearly indicated significance of the extracted respiratory signal from the ECG and BP signals, when compared with the original recorded signal and could become a better alternative to the classical methods for recording respiratory signals.

E 2 MD for reduction of motion artifacts from photoplethysmographic signals

Most of the intensive care units (ICU) are equipped with commercial pulse oximeters for monitoring arterial blood oxygen saturation (SpO2) and pulse rate (PR). Photoplethysmographic (PPG) data recorded from pulse oximeters usually corrupted by motion artifacts (MA), resulting in unreliable and inaccurate estimated measures of SpO2. In this paper, a simple and efficient MA reduction method based on Ensemble Empirical Mode Decomposition (E2MD) is proposed for the estimation of SpO2 from processed PPGs. Performance analysis of the proposed E2MD is evaluated by computing the statistical and quality measures indicating the signal reconstruction like SNR and NRMSE. Intentionally created MAs (Horizontal MA, Vertical MA and Bending MA) in the recorded PPGs are effectively reduced by the proposed one and proved to be the best suitable method for reliable and accurate SpO2 estimation from the processed PPGs.

Extraction of respiration rate from ECG and BP signals using order reduced-modified covariance AR technique

Extraction of respiration rates from electrocardiogram (ECG) and blood pressure (BP) signals would be an alternative approach for obtaining respiration related information. This process is useful in situations when, ECG, or BP but not respiration is routinely monitored or in cases where, the cardiac arrhythmias are to be studied in correlation with respiratory information and is extremely important. There have been several efforts on ECG-Derived Respiration (EDR) and BP-Derived Respiration (BDR). These methods are based on different signal processing techniques like filtering, wavelets and other statistical methods, which work by extraction of respiratory trend embedded into various physiological signals, as an additive component, or an amplitude modulated (AM) component and frequency modulated (FM) component. The proposed method is a robust, yet simple and makes use of order reduced AR-model by restricting the pole locations in the frequency range of interest. Test results on ECG and BP signals of MIMIC data base of Physiobank archive reveal that the proposed order reduced-modified covariance AR model (OR-MCAR) has efficiently extracted respiratory information from ECG and BP signals. The evaluated similarity parameters in both time and frequency domains for original and surrogate respiratory signals have shown the superiority of the method over wavelet based method.