M. Raghu Ram

Estimation of respiration rate from ECG, BP and PPG signals using empirical mode decomposition

Estimation of respiration rates from electrocardiogram (ECG), blood pressure (BP) and photoplethysmographic (PPG) signals would be an alternative approach for obtaining respiration related information. This process is useful in situations when, ECG, BP and PPG 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), BP-Derived Respiration (BDR) and PPG Derived Respiration (PDR). 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 or frequency modulated (FM) component. The proposed method is a robust, yet simple and makes use of derived Intrinsic Mode Functions (IMF) using Empirical Mode Decomposition (EMD). Test results on ECG, BP and PPG signals of the well known MIMIC database from Physiobank archive reveal that the proposed EMD method has efficiently extracted respiratory information from ECG, BP and PPG signals. The evaluated similarity parameters in both time and frequency domains for original and estimated respiratory rates have shown the superiority of the method.

Robust Extraction of Respiratory Activity From PPG Signals Using Modified MSPCA

The pulse oximeters photoplethysmographic (PPG) signals can be well utilized for extracting the vital respiratory activity, in addition to saturation and heart rate estimations, avoiding the usage of additional sensor for recording respiratory signal, in turn reducing the number of sensors connected to the patients body for recording vital signals. In this paper, we present a robust algorithm called modified multi scale principal component analysis (MMSPCA), for extraction of respiratory activity embedded in the PPG signals. The PPG signals are more commonly corrupted by motion artifacts (MA) due to voluntary or involuntary movements of the patients, making it difficult for the algorithms to extract respiratory signals. The problem of extracting respiratory signals from PPGs in the presence of MA is addressed for the first time in this paper. The problem gets aggravated when PPGs are severely afflicted with MAs in situations such as the MA frequency band (usually below 0.2 Hz) overlapping on to the band of respiratory frequencies (0.2-0.4 Hz). In the presented algorithm, the kurtosis and energy contribution levels (ECL) of approximate and detail coefficients are calculated for each wavelet sub-band matrix, generating a modified wavelet sub-band matrix. This makes the presented algorithm based on MMSPCA more robust in the sense that it is made motion resistant by suitably modifying the MSPCA. Functioning of the proposed algorithm is tested on the data recorded from 15 healthy subjects. Each data set consists of intentionally created possible MA noises, viz., vertical, horizontal, waving, and pressing MAs with different breathing patterns. The method is also applied on the recordings available with MIMIC database of Physionet archive. The statistical and error analysis, performed to test the efficacy of the presented MMSPCA algorithm, revealed a very good acceptance for derived respiratory signal, when compared with the originally recorded respiratory signals using classical method. The MMSPCA method clearly outperformed the conventional MSPCA-based method in the presence of MA.

ICA-Based Improved DTCWT Technique for MA Reduction in PPG Signals With Restored Respiratory Information

In addition to estimation of arterial blood oxygen saturation (SpO2), pulse oximeters photoplethysmographic (PPG) signals can be well utilized for extracting the vital respiratory information. The motion artifacts (MA) in PPGs not only make SpO2 estimations unreliable and inaccurate but also make it difficult to extract respiratory information. Addressing this issue, for the first time, we propose a novel approach called “ICA-based improved dual-tree complex wavelet transform (I2DTCWT)” technique, for efficient reduction of MAs leaving the respiratory information undisturbed. The method makes use of source separation ability of independent component analysis (ICA) along with computationally efficient modified DTCWT processing. A prototype pulse oximeter was developed and performance analysis of DTCWT, modified DTCWT and I2DTCWT processing methods was carried out using PPG data recorded with intentionally created MAs (horizontal MA, vertical MA, and bending MA). Experimental results demonstrated the efficiency of DTCWT processing methods in restoring PPG morphology and proved that there is a significant improvement guaranteed in reducing MAs with the presented methods. Statistical performance is evaluated in terms of measures like signal-to-noise ratio, normalized root mean square error, and correlation analysis with correlation co-efficient measure. The I2DTCWT outperformed other DTCWT processing methods in respect of MA reduction and the computed spectra revealed that safe extraction of respiratory information is guaranteed from these MA reduced PPGs. The proposed method is also validated by comparing with the well established signal extraction technology of MASIMO pulse oximeters, for which the discrete saturation transform (DST) is the key element. The %SpO2 estimations from processed PPGs by the proposed method closely followed the estimations based on DST and were very close to that of clean sections of PPG. In addition, the proposed method resulted in less computation cost compared to the MASIMO SET. Digital volume pulse waveform contour analysis is also performed on MA reduced PPGs to validate PPG morphology and the conventional parameters are calculated for assessing the arterial stiffness.

Extraction of respiratory activity from PPG and BP signals using Principal Component Analysis

In high risk situations such as cardiac arrhythmias, ambulatory monitoring, stress tests, sleep disorder investigations and post-operative hypoxemia situations, monitoring of respiratory activity would be mandatory. Electrocardiogram (ECG), blood pressure (BP) and photoplethysmographic (PPG) signals can be used for extraction of respiratory activity, and will eventually eliminate the use of additional respiratory sensor. Using a simple and standard non-parametric mathematical technique, Principal Component Analysis (PCA), the respiratory related information is extracted from complex data sets such as PPG and BP signals. The respiratory induced variations (RIV) of PPG and BP signals are described by coefficients of computed principal components. Singular value ratio (SVR) trend is used to find the periodicity, which is one of the crucial parameters in forming the data sets for PCA. Test results on MIMIC data base clearly indicated a strong correlation between the extracted and actual respiratory signals. Statistical measures in both time and frequency domains such as Relative Correlation Coefficient (RCC) and Magnitude Squared Coherence (MSC) respectively and Accuracy Rate (AR) are calculated to demonstrate the fact, that respiratory signal is present in the form of first principal components.

Estimation of respiratory rate from principal components of photoplethysmographic signals

Continuous monitoring of respiratory activity is mandatory in clinical, high risk situations such as ambulatory monitoring, intensive care, stress tests and sleep disorder investigations. Extraction of surrogate respiratory activity from electrocardiogram (ECG), blood pressure (BP) and photoplethysmographic (PPG) signals will potentially eliminate the use of additional sensor intended to record respiration. Principal Component Analysis (PCA) is a simple and standard non-parametric mathematical tool for extracting relevant information from complex data sets. In this paper, PCA is exploited for extraction of surrogate respiratory activity from PPG signals. The respiratory induced intensity variations (RIIV) of PPG signal are described by coefficients of computed principal components. Singular value ratio (SVR) trend is used to find the periodicity, which is one of the key parameters in forming the data sets for PCA. Test results on MIMIC data base clearly indicated a strong correlation between the extracted and actual respiratory signals. The evaluated similarity measures, both in time (RCC-Relative Correlation Coefficient) and frequency (MSC-Magnitude Squared Coherence) domains and calculated accuracy demonstrated the fact that respiratory signal is present in the form of first principal component.

On the performance of Time Varying Step-size Least Mean Squares(TVS-LMS) adaptive filter for MA reduction from PPG signals

Clinical Investigation of hypoxic status of the patients requires accurate information about the heart rate and oxygen saturation of arterial blood. Pulse oximeters are widely used for monitoring these parameters by recording the raw pulse oximeter signal, namely Photoplethysmogram (PPG). The recorded PPG Signal acquired using PPG sensors are usually corrupted with Motion Artifacts (MA) due to the voluntary or involuntary movements of patient. Reduction of MA has received much attention in the literature over recent years. In this paper, we present an efficient adaptive filtering technique based on Time Varying Step-size Least Mean Squares (TVS-LMS) algorithm for MA reduction. The novelty of the method lies in the fact that a synthetic noise reference signal for adaptive filtering, representing MA noise, is generated internally from the MA corrupted PPG signal itself instead of using any additional hardware such as accelerometer or source-detector pair for noise reference signal generation. Convergence analysis, SNR calculations and Statistical analysis revealed that the proposed TVS-LMS method has a clear edge over the Constant Step-size LMS (CS-LMS) based adaptive filtering technique. Test results, on the PPG data recorded with different MAs, demonstrated the efficacy of the proposed TVS-LMS algorithm in MA reduction and thus making it best suitable for real-time pulse oximetry applications.

Use of Multi-Scale Principal Component Analysis for motion artifact reduction of PPG signals

Arterial blood oxygen saturation (SpO2), a vital measure of amount of oxygen that is dissolved in blood, is estimated using commercial pulse oximeter by recording the Photoplethysmographic (PPG) signals. Ever since the invention of pulse oximetry, reliable and accurate estimation of arterial blood oxygen saturation (SpO2) has been a challenging problem for researchers. Mostly inaccurate estimation of SpO2 in a pulse oximeter arises due to the motion artifacts (MA) created in the detected PPG signals by the voluntary or involuntary movements of a patient. We present an MA reduction method based on Multi Scale Principal Component Analysis (MSPCA) technique. MSPCA combines the ability of PCA to decorrelate the variable with wavelet analysis for MA reduction from recorded PPG data. MSPCA computes PCA of wavelet coefficients at each scale followed by combining the results at relevant scales. Experimental result revealed that MSPCA outperformed the basic wavelet based processing for MA reduction of PPG signals and is best suited for pulse oximetry applications.

Adaptive reduction of motion artifacts from PPG signals using a synthetic noise reference signal

Pulse oximeters estimate both the heart rate and oxygen saturation accurately and are widely used in clinical applications for monitoring the patients at risk of hypoxia. The raw pulse oximeter signal namely Photoplethysmogram (PPG) usually suffers from motion artifacts (MA) corruption, due to the voluntary or involuntary movements of patient while recording the data from PPG sensor. The identification and elimination of these erroneous signal features has received much attention in the scientific literature over recent years. In this paper, we present a simple and efficient adaptive filtering technique for MA reduction using a synthetic noise reference signal without any extra hardware for noise reference signal generation. A thorough experimental analysis is carried out on real MA corrupted PPG data (for horizontal, vertical and bending motions of finger) to demonstrate the efficacy of the proposed method. Simulation results and statistical analysis reveal that the proposed method has shown better performance in MA reduction, making it suitable for pulse oximetry applications.

On the performance of AS-LMS based adaptive filter for reduction of motion artifacts from PPG signals

A Photoplethysmographic (PPG) signal is invariably corrupted with motion artifacts (MA) due to voluntary or involuntary movements of the patient. PPG is a non-invasive signal, used for the estimation of arterial blood oxygen saturation (SpO2), which helps the physician to know the hypoxic status of patient during clinical investigations. This paper presents an efficient Adaptive Step-size Least Mean Squares (AS-LMS) based adaptive filter for reducing the MA from corrupted PPG signals. The novelty of the proposed algorithm lies in the fact that a synthetic noise reference signal for adaptive filtering, representing MA noise, is generated internally from the MA corrupted PPG signal itself instead of using any additional hardware such as accelerometer or source-detector pair for acquiring noise reference signal. Convergence analysis, SNR calculations and Statistical analysis revealed that the proposed AS-LMS algorithm has a clear edge over the Time-Varying Step-size LMS (TVS-LMS) and Constant Step-size LMS (CS-LMS) based adaptive algorithms for MA reduction from PPG signals. Experimental results, for the PPG data recorded with different motion artifacts (Horizontal, Vertical and Bending motion of finger), demonstrated the efficacy of the proposed algorithm in MA reduction and thus making it best suitable for real-time pulse oximetry applications.

A robust signal processing method for extraction of respiratory activity from artifact corrupted PPG signal

Continuous monitoring of respiratory activity is required in many clinical settings from emergency departments to operating theatres and high dependency intensive care units. Current techniques such as nasal thermistors, capnography and monitoring of transthoracic impedance are prone to movement artifact or difficult to use in a continuously ventilating patient. To help overcome some of these problems, it was reported in literature that it is possible to extract respiratory activity from the Photoplethysmogram (PPG) using wavelet transform based methods. In this paper, we present a robust yet simple ICA based signal processing method to extract the surrogate respiratory activity from non-invasive recordings of artifact corrupted PPG signals. To prove the efficacy of proposed method, experimental results are carried out considering two commonly encountered motion artifacts of PPG signals. Simulation results and statistical calculations in terms of Magnitude Squared Coherence (MSC), Relative Correlation Co-efficient (RCC) and Accuracy Rate (AR) indicated a strong correlation between the extracted respiratory signal with that of original respiratory signal.