The Experts below are selected from a list of 141 Experts worldwide ranked by ideXlab platform
Ki H Chon - One of the best experts on this subject based on the ideXlab platform.
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premature atrial and ventricular contraction detection using photoplethysmographic data from a smartwatch
Sensors, 2020Co-Authors: Dong Han, David D. Mcmanus, Syed Khairul Bashar, Fahimeh Mohagheghian, Eric Y Ding, Cody Whitcomb, Ki H ChonAbstract:We developed an algorithm to detect premature atrial contraction (PAC) and premature ventricular contraction (PVC) using photoplethysmographic (PPG) data acquired from a smartwatch. Our PAC/PVC detection algorithm is composed of a sequence of algorithms that are combined to discriminate various arrhythmias. A novel vector resemblance method is used to enhance the PAC/PVC detection results of the Poincare plot method. The new PAC/PVC detection algorithm with our automated motion and Noise Artifact detection approach yielded a sensitivity of 86% for atrial fibrillation (AF) subjects while the overall sensitivity was 67% when normal sinus rhythm (NSR) subjects were also included. The specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy values for the combined data consisting of both NSR and AF subjects were 97%, 81%, 94% and 92%, respectively, for PAC/PVC detection combined with our automated motion and Noise Artifact detection approach. Moreover, when AF detection was compared with and without PAC/PVC, the sensitivity and specificity increased from 94.55% to 98.18% and from 95.75% to 97.90%, respectively. For additional independent testing data, we used two datasets: a smartwatch PPG dataset that was collected in our ongoing clinical study, and a pulse oximetry PPG dataset from the Medical Information Mart for Intensive Care III database. The PAC/PVC classification results of the independent testing on these two other datasets are all above 92% for sensitivity, specificity, PPV, NPV, and accuracy. The proposed combined approach to detect PAC and PVC can ultimately lead to better accuracy in AF detection. This is one of the first studies involving detection of PAC and PVC using PPG recordings from a smartwatch. The proposed method can potentially be of clinical importance as this enhanced capability can lead to fewer false positive detections of AF, especially for those NSR subjects with frequent episodes of PAC/PVC.
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motion and Noise Artifact resilient atrial fibrillation detection using a smartphone
IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 2018Co-Authors: Jo Woon Chong, David D. Mcmanus, Fatemehsadat Tabei, Duy Leanh, Ki H ChonAbstract:We have recently found that our previously-developed atrial fibrillation (AF) detection algorithm for smartphones can give false positives when subjects’ fingers or hands move, as we rely on proper finger placement over the smartphone camera to collect the signal of interest. Specifically, smartphone camera pulsatile signals that are obtained from normal sinus rhythm (NSR) subjects but are corrupted by motion and Noise Artifacts (MNAs) are frequently detected as AF. AF and motion-corrupted episodes have the similar characteristic that pulse-to-pulse intervals are irregular. We have developed an MNA-resilient smartphone-based AF detection algorithm that first discriminates and eliminates MNA-corrupted episodes in smartphone camera recordings, and then detects AF in MNA-free recordings. We found that MNA-corrupted episodes have highly varying pulse slope, large turning point ratio, or large kurtosis values in smartphone signals compared to MNA-free AF and NSR episodes. We first use these three metrics for MNA discrimination and exclusion. Then, AF is detected in MNA-free signals using our previous algorithm. The capability to discriminate MNAs and AFs separately in smartphone signals increases the specificity of AF detection. To evaluate the performance of the proposed MNA-resilient AF algorithm, 99 subjects, including 88 study participants with AF at baseline and in NSR after electrical cardioversion as well as 11 participants with MNA-corrupted NSR, were recruited. Using iPhone 4S, 5S, and 6S models, we collected 2-min pulsatile time series from each subject. The clinical results show that the accuracy, sensitivity, and specificity of the proposed AF algorithm are 0.97, 0.98, and 0.97, respectively, which are higher than those of the previous AF algorithm.
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Developing a novel Noise Artifact detection algorithm for smartphone PPG signals: Preliminary results
2018 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), 2018Co-Authors: Syed Khairul Bashar, David D. Mcmanus, Apurv Soni, Ki H ChonAbstract:Pulsatile signals recorded from a smartphone are often corrupted with Noise Artifacts, which hampers accuracy of the peak detection and consequently leads to inaccurate heart rate estimation. In this paper, we propose a novel approach which uses an algorithm based on variable frequency complex demodulation (VFCDM) to detect Noise Artifacts in the smartphone's pulsatile signal recorded from a fingertip video. The ultimate goal is to increase the accuracy of atrial fibrillation (AF) detection. In the time-frequency spectra obtained from VFCDM, thresholds are imposed on both the magnitude of the dominant frequency component at each time instant and on the successive difference of the significant frequency component in the heart rate range to enable accurate Noise Artifact detection. For this preliminary analysis, the performance of the proposed method has been evaluated on 200 subjects; the data were collected during a smartphone-based AF screening study in India. The proposed method is shown to detect Noise Artifacts in pulsatile signals with 91.16% accuracy, demonstrating the potential to reduce false alarms when only data segments identified as clean are used for AF detection.
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motion and Noise Artifact resilient atrial fibrillation detection using a smartphone
Cooperative and Human Aspects of Software Engineering, 2016Co-Authors: Rifat Zaman, David D. Mcmanus, Jo Woon Chong, Ki H ChonAbstract:Smartphone signals corrupted by motion and Noise Artifacts (MNAs) are often misclassified into atrial fibrillation (AF) by our previous smartphone AF detection application [1]. We developed an MNA-tolerant AF detection algorithm for smartphones, which first detects MNAs in the smartphone signals, removes them, and finally detects AF from the MNA-free smartphone signals. To detect MNAs, we used time and frequency-domain parameters: high-pass filtered signal amplitude, successive pulse amplitude ratio, and successive maximum dominant frequency. AFs are detected using our previous AF detection algorithm based on root mean square of successive RR difference (RMSSD) and Shannon Entropy (ShE) values [1]. The clinical results show that the accuracy, sensitivity and specificity of the proposed AF algorithm are 0.9632, 0.9341, and 0.9899, respectively.
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motion and Noise Artifact resilient atrial fibrillation detection using a smartphone
Cooperative and Human Aspects of Software Engineering, 2016Co-Authors: Rifat Zaman, David D. Mcmanus, Jo Woon Chong, Chae Ho Cho, Nada Esa, Ki H ChonAbstract:Smartphone signals corrupted by motion and Noise Artifacts (MNAs) are often misclassified into atrial fibrillation (AF) by our previous smartphone AF detection application [1]. We developed an MNA-tolerant AF detection algorithm for smartphones, which first detects MNAs in the smartphone signals, removes them, and finally detects AF from the MNA-free smartphone signals. To detect MNAs, we used time and frequency-domain parameters: high-pass filtered signal amplitude, successive pulse amplitude ratio, and successive maximum dominant frequency. AFs are detected using our previous AF detection algorithm based on root mean square of successive RR difference (RMSSD) and Shannon Entropy (ShE) values [1]. The clinical results show that the accuracy, sensitivity and specificity of the proposed AF algorithm are 0.9632, 0.9341, and 0.9899, respectively.
Jo Woon Chong - One of the best experts on this subject based on the ideXlab platform.
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Motion and Noise Artifact Detection in Smartphone Photoplethysmograph Signals Using Personalized Classifier
2019 IEEE Healthcare Innovations and Point of Care Technologies (HI-POCT), 2019Co-Authors: Fatemehsadat Tabei, Behnam Askarian, Jo Woon ChongAbstract:Health parameters such as heart rhythm, blood pressure, and the level of oxygen saturation in the blood could be measured with photoplethysmography (PPG) signal. The advent of smartphone camera sensors has enabled the extraction of PPG signals from smartphones. PPG signals are weak at motion and Noise Artifacts (MNA) which could generate unreliable heart rate measurement. Smartphone PPG signals are more prone to MNA since they are not designed for clinical applications. PPG signals are known as biometric signals since they have unique behaviors for each individual. However, in previous MNA detection studies this personalized characteristic has not been considered. In this paper, we propose a novel personalized MNA detection method by applying a probabilistic neural network as a classifier. The performance of our personalized method is evaluated with 25 volunteered subjects in terms of accuracy, specificity, and sensitivity and compared with the generalized method. The average accuracy of our personalized method is 97.96% while it is 92.94% in the generalized one. The average values of personalized specificity and sensitivity are 99.69% and 93.91% while the generalized classifier gives 95.38% and 87.4%.
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motion and Noise Artifact resilient atrial fibrillation detection using a smartphone
IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 2018Co-Authors: Jo Woon Chong, David D. Mcmanus, Fatemehsadat Tabei, Duy Leanh, Ki H ChonAbstract:We have recently found that our previously-developed atrial fibrillation (AF) detection algorithm for smartphones can give false positives when subjects’ fingers or hands move, as we rely on proper finger placement over the smartphone camera to collect the signal of interest. Specifically, smartphone camera pulsatile signals that are obtained from normal sinus rhythm (NSR) subjects but are corrupted by motion and Noise Artifacts (MNAs) are frequently detected as AF. AF and motion-corrupted episodes have the similar characteristic that pulse-to-pulse intervals are irregular. We have developed an MNA-resilient smartphone-based AF detection algorithm that first discriminates and eliminates MNA-corrupted episodes in smartphone camera recordings, and then detects AF in MNA-free recordings. We found that MNA-corrupted episodes have highly varying pulse slope, large turning point ratio, or large kurtosis values in smartphone signals compared to MNA-free AF and NSR episodes. We first use these three metrics for MNA discrimination and exclusion. Then, AF is detected in MNA-free signals using our previous algorithm. The capability to discriminate MNAs and AFs separately in smartphone signals increases the specificity of AF detection. To evaluate the performance of the proposed MNA-resilient AF algorithm, 99 subjects, including 88 study participants with AF at baseline and in NSR after electrical cardioversion as well as 11 participants with MNA-corrupted NSR, were recruited. Using iPhone 4S, 5S, and 6S models, we collected 2-min pulsatile time series from each subject. The clinical results show that the accuracy, sensitivity, and specificity of the proposed AF algorithm are 0.97, 0.98, and 0.97, respectively, which are higher than those of the previous AF algorithm.
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a novel personalized motion and Noise Artifact mna detection method for smartphone photoplethysmograph ppg signals
IEEE Access, 2018Co-Authors: Fatemehsadat Tabei, David D. Mcmanus, Rajnish Kumar, Tra Nguyen Phan, Jo Woon ChongAbstract:Photoplethysmography (PPG) is a technique to detect blood volume changes in an optical way. Representative PPG applications are the measurements of oxygen saturation, heart rate, and respiratory rate. However, the PPG signals are sensitive to motion and Noise Artifacts (MNAs), especially when they are obtained from smartphone cameras. Moreover, the PPG signals are different among users and each individual’s PPG signal has a unique characteristic. Hence, an effective MNA detection and reduction method for smartphone PPG signals, which adapts itself to each user in a personalized way, is highly demanded. In this paper, a concept of the probabilistic neural network is introduced to be used with the proposed extracted parameters. The signal amplitude, standard deviation of peak to peak time intervals and amplitudes, along with the mean of moving standard deviation, signal slope changes, and the optimal autoregressive model order are proposed for effective MNA detection. Accordingly, the performance of the proposed personalized algorithm is compared with conventional MNA detection algorithms. As for the performance metrics, we considered accuracy, sensitivity, and specificity. The results show that the overall performance of the personalized MNA detection is enhanced compared to the generalized algorithm. The average values of the accuracy, sensitivity, and specificity of the personalized one are 98.07%, 92.6%, and 99.78%, respectively, while these are 89.92%, 84.21%, and 93.63% for the general one.
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motion and Noise Artifact resilient atrial fibrillation detection using a smartphone
Cooperative and Human Aspects of Software Engineering, 2016Co-Authors: Rifat Zaman, David D. Mcmanus, Jo Woon Chong, Ki H ChonAbstract:Smartphone signals corrupted by motion and Noise Artifacts (MNAs) are often misclassified into atrial fibrillation (AF) by our previous smartphone AF detection application [1]. We developed an MNA-tolerant AF detection algorithm for smartphones, which first detects MNAs in the smartphone signals, removes them, and finally detects AF from the MNA-free smartphone signals. To detect MNAs, we used time and frequency-domain parameters: high-pass filtered signal amplitude, successive pulse amplitude ratio, and successive maximum dominant frequency. AFs are detected using our previous AF detection algorithm based on root mean square of successive RR difference (RMSSD) and Shannon Entropy (ShE) values [1]. The clinical results show that the accuracy, sensitivity and specificity of the proposed AF algorithm are 0.9632, 0.9341, and 0.9899, respectively.
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motion and Noise Artifact resilient atrial fibrillation detection using a smartphone
Cooperative and Human Aspects of Software Engineering, 2016Co-Authors: Rifat Zaman, David D. Mcmanus, Jo Woon Chong, Chae Ho Cho, Nada Esa, Ki H ChonAbstract:Smartphone signals corrupted by motion and Noise Artifacts (MNAs) are often misclassified into atrial fibrillation (AF) by our previous smartphone AF detection application [1]. We developed an MNA-tolerant AF detection algorithm for smartphones, which first detects MNAs in the smartphone signals, removes them, and finally detects AF from the MNA-free smartphone signals. To detect MNAs, we used time and frequency-domain parameters: high-pass filtered signal amplitude, successive pulse amplitude ratio, and successive maximum dominant frequency. AFs are detected using our previous AF detection algorithm based on root mean square of successive RR difference (RMSSD) and Shannon Entropy (ShE) values [1]. The clinical results show that the accuracy, sensitivity and specificity of the proposed AF algorithm are 0.9632, 0.9341, and 0.9899, respectively.
David D. Mcmanus - One of the best experts on this subject based on the ideXlab platform.
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premature atrial and ventricular contraction detection using photoplethysmographic data from a smartwatch
Sensors, 2020Co-Authors: Dong Han, David D. Mcmanus, Syed Khairul Bashar, Fahimeh Mohagheghian, Eric Y Ding, Cody Whitcomb, Ki H ChonAbstract:We developed an algorithm to detect premature atrial contraction (PAC) and premature ventricular contraction (PVC) using photoplethysmographic (PPG) data acquired from a smartwatch. Our PAC/PVC detection algorithm is composed of a sequence of algorithms that are combined to discriminate various arrhythmias. A novel vector resemblance method is used to enhance the PAC/PVC detection results of the Poincare plot method. The new PAC/PVC detection algorithm with our automated motion and Noise Artifact detection approach yielded a sensitivity of 86% for atrial fibrillation (AF) subjects while the overall sensitivity was 67% when normal sinus rhythm (NSR) subjects were also included. The specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy values for the combined data consisting of both NSR and AF subjects were 97%, 81%, 94% and 92%, respectively, for PAC/PVC detection combined with our automated motion and Noise Artifact detection approach. Moreover, when AF detection was compared with and without PAC/PVC, the sensitivity and specificity increased from 94.55% to 98.18% and from 95.75% to 97.90%, respectively. For additional independent testing data, we used two datasets: a smartwatch PPG dataset that was collected in our ongoing clinical study, and a pulse oximetry PPG dataset from the Medical Information Mart for Intensive Care III database. The PAC/PVC classification results of the independent testing on these two other datasets are all above 92% for sensitivity, specificity, PPV, NPV, and accuracy. The proposed combined approach to detect PAC and PVC can ultimately lead to better accuracy in AF detection. This is one of the first studies involving detection of PAC and PVC using PPG recordings from a smartwatch. The proposed method can potentially be of clinical importance as this enhanced capability can lead to fewer false positive detections of AF, especially for those NSR subjects with frequent episodes of PAC/PVC.
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motion and Noise Artifact resilient atrial fibrillation detection using a smartphone
IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 2018Co-Authors: Jo Woon Chong, David D. Mcmanus, Fatemehsadat Tabei, Duy Leanh, Ki H ChonAbstract:We have recently found that our previously-developed atrial fibrillation (AF) detection algorithm for smartphones can give false positives when subjects’ fingers or hands move, as we rely on proper finger placement over the smartphone camera to collect the signal of interest. Specifically, smartphone camera pulsatile signals that are obtained from normal sinus rhythm (NSR) subjects but are corrupted by motion and Noise Artifacts (MNAs) are frequently detected as AF. AF and motion-corrupted episodes have the similar characteristic that pulse-to-pulse intervals are irregular. We have developed an MNA-resilient smartphone-based AF detection algorithm that first discriminates and eliminates MNA-corrupted episodes in smartphone camera recordings, and then detects AF in MNA-free recordings. We found that MNA-corrupted episodes have highly varying pulse slope, large turning point ratio, or large kurtosis values in smartphone signals compared to MNA-free AF and NSR episodes. We first use these three metrics for MNA discrimination and exclusion. Then, AF is detected in MNA-free signals using our previous algorithm. The capability to discriminate MNAs and AFs separately in smartphone signals increases the specificity of AF detection. To evaluate the performance of the proposed MNA-resilient AF algorithm, 99 subjects, including 88 study participants with AF at baseline and in NSR after electrical cardioversion as well as 11 participants with MNA-corrupted NSR, were recruited. Using iPhone 4S, 5S, and 6S models, we collected 2-min pulsatile time series from each subject. The clinical results show that the accuracy, sensitivity, and specificity of the proposed AF algorithm are 0.97, 0.98, and 0.97, respectively, which are higher than those of the previous AF algorithm.
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Developing a novel Noise Artifact detection algorithm for smartphone PPG signals: Preliminary results
2018 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), 2018Co-Authors: Syed Khairul Bashar, David D. Mcmanus, Apurv Soni, Ki H ChonAbstract:Pulsatile signals recorded from a smartphone are often corrupted with Noise Artifacts, which hampers accuracy of the peak detection and consequently leads to inaccurate heart rate estimation. In this paper, we propose a novel approach which uses an algorithm based on variable frequency complex demodulation (VFCDM) to detect Noise Artifacts in the smartphone's pulsatile signal recorded from a fingertip video. The ultimate goal is to increase the accuracy of atrial fibrillation (AF) detection. In the time-frequency spectra obtained from VFCDM, thresholds are imposed on both the magnitude of the dominant frequency component at each time instant and on the successive difference of the significant frequency component in the heart rate range to enable accurate Noise Artifact detection. For this preliminary analysis, the performance of the proposed method has been evaluated on 200 subjects; the data were collected during a smartphone-based AF screening study in India. The proposed method is shown to detect Noise Artifacts in pulsatile signals with 91.16% accuracy, demonstrating the potential to reduce false alarms when only data segments identified as clean are used for AF detection.
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a novel personalized motion and Noise Artifact mna detection method for smartphone photoplethysmograph ppg signals
IEEE Access, 2018Co-Authors: Fatemehsadat Tabei, David D. Mcmanus, Rajnish Kumar, Tra Nguyen Phan, Jo Woon ChongAbstract:Photoplethysmography (PPG) is a technique to detect blood volume changes in an optical way. Representative PPG applications are the measurements of oxygen saturation, heart rate, and respiratory rate. However, the PPG signals are sensitive to motion and Noise Artifacts (MNAs), especially when they are obtained from smartphone cameras. Moreover, the PPG signals are different among users and each individual’s PPG signal has a unique characteristic. Hence, an effective MNA detection and reduction method for smartphone PPG signals, which adapts itself to each user in a personalized way, is highly demanded. In this paper, a concept of the probabilistic neural network is introduced to be used with the proposed extracted parameters. The signal amplitude, standard deviation of peak to peak time intervals and amplitudes, along with the mean of moving standard deviation, signal slope changes, and the optimal autoregressive model order are proposed for effective MNA detection. Accordingly, the performance of the proposed personalized algorithm is compared with conventional MNA detection algorithms. As for the performance metrics, we considered accuracy, sensitivity, and specificity. The results show that the overall performance of the personalized MNA detection is enhanced compared to the generalized algorithm. The average values of the accuracy, sensitivity, and specificity of the personalized one are 98.07%, 92.6%, and 99.78%, respectively, while these are 89.92%, 84.21%, and 93.63% for the general one.
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motion and Noise Artifact resilient atrial fibrillation detection using a smartphone
Cooperative and Human Aspects of Software Engineering, 2016Co-Authors: Rifat Zaman, David D. Mcmanus, Jo Woon Chong, Ki H ChonAbstract:Smartphone signals corrupted by motion and Noise Artifacts (MNAs) are often misclassified into atrial fibrillation (AF) by our previous smartphone AF detection application [1]. We developed an MNA-tolerant AF detection algorithm for smartphones, which first detects MNAs in the smartphone signals, removes them, and finally detects AF from the MNA-free smartphone signals. To detect MNAs, we used time and frequency-domain parameters: high-pass filtered signal amplitude, successive pulse amplitude ratio, and successive maximum dominant frequency. AFs are detected using our previous AF detection algorithm based on root mean square of successive RR difference (RMSSD) and Shannon Entropy (ShE) values [1]. The clinical results show that the accuracy, sensitivity and specificity of the proposed AF algorithm are 0.9632, 0.9341, and 0.9899, respectively.
David L Hayes - One of the best experts on this subject based on the ideXlab platform.
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survival after shock therapy in implantable cardioverter defibrillator and cardiac resynchronization therapy defibrillator recipients according to rhythm shocked the altitude survival by rhythm study
Journal of the American College of Cardiology, 2013Co-Authors: Brian D Powell, Roosevelt F Gilliam, Leslie A Saxon, John Boehmer, John D Day, Paul A Heidenreich, Paul W Jones, Matthew J Rousseau, David L HayesAbstract:Objectives This study sought to determine if the risk of mortality associated with inappropriate implantable cardioverter-defibrillator (ICD) shocks is due to the underlying arrhythmia or the shock itself. Background Shocks delivered from ICDs are associated with an increased risk of mortality. It is unknown if all patients who experience inappropriate ICD shocks have an increased risk of death. Methods We evaluated survival outcomes in patients with an ICD and a cardiac resynchronization therapy defibrillator enrolled in the LATITUDE remote monitoring system (Boston Scientific Corp., Natick, Massachusetts) through January 1, 2010. First shock episode rhythms from 3,809 patients who acutely survived the initial shock were adjudicated by 7 electrophysiologists. Patients with a shock were matched to patients without a shock (n = 3,630) by age at implant, implant year, sex, and device type. Results The mean age of the study group was 64 ± 13 years, and 78% were male. Compared with no shock, there was an increased rate of mortality in those who received their first shock for monomorphic ventricular tachycardia (hazard ratio [HR]: 1.65, p Conclusions Compared with no shock, those who received their first shock for ventricular rhythms and atrial fibrillation had an increased risk of death. There was no significant difference in survival after inappropriate shocks for sinus tachycardia or Noise/Artifact/oversensing. In this study, the adverse prognosis after first shock appears to be more related to the underlying arrhythmia than to an adverse effect from the shock itself.
Yitzhak Mendelson - One of the best experts on this subject based on the ideXlab platform.
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Photoplethysmograph Signal Reconstruction based on a Novel Motion Artifact Detection-Reduction Approach. Part II: Motion and Noise Artifact Removal
Annals of Biomedical Engineering, 2014Co-Authors: S M A Salehizadeh, Yitzhak Mendelson, Jo Woon Chong, Duy K. Dao, David Mcmanus, Chad Darling, Ki H ChonAbstract:We introduce a new method to reconstruct motion and Noise Artifact (MNA) contaminated photoplethysmogram (PPG) data. A method to detect MNA corrupted data is provided in a companion paper. Our reconstruction algorithm is based on an iterative motion Artifact removal (IMAR) approach, which utilizes the singular spectral analysis algorithm to remove MNA Artifacts so that the most accurate estimates of uncorrupted heart rates (HRs) and arterial oxygen saturation (SpO_2) values recorded by a pulse oximeter can be derived. Using both computer simulations and three different experimental data sets, we show that the proposed IMAR approach can reliably reconstruct MNA corrupted data segments, as the estimated HR and SpO_2 values do not significantly deviate from the uncorrupted reference measurements. Comparison of the accuracy of reconstruction of the MNA corrupted data segments between our IMAR approach and the time-domain independent component analysis (TD-ICA) is made for all data sets as the latter method has been shown to provide good performance. For simulated data, there were no significant differences in the reconstructed HR and SpO_2 values starting from 10 dB down to −15 dB for both white and colored Noise contaminated PPG data using IMAR; for TD-ICA, significant differences were observed starting at 10 dB. Two experimental PPG data sets were created with contrived MNA by having subjects perform random forehead and rapid side-to-side finger movements show that; the performance of the IMAR approach on these data sets was quite accurate as non-significant differences in the reconstructed HR and SpO_2 were found compared to non-contaminated reference values, in most subjects. In comparison, the accuracy of the TD-ICA was poor as there were significant differences in reconstructed HR and SpO_2 values in most subjects. For non-contrived MNA corrupted PPG data, which were collected with subjects performing walking and stair climbing tasks, the IMAR significantly outperformed TD-ICA as the former method provided HR and SpO_2 values that were non-significantly different than MNA free reference values.
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photoplethysmograph signal reconstruction based on a novel hybrid motion Artifact detection reduction approach part i motion and Noise Artifact detection
Annals of Biomedical Engineering, 2014Co-Authors: Jo Woon Chong, David D. Mcmanus, S M A Salehizadeh, Chad E Darling, Ki H Chon, Yitzhak MendelsonAbstract:Motion and Noise Artifacts (MNA) are a serious obstacle in utilizing photoplethysmogram (PPG) signals for real-time monitoring of vital signs. We present a MNA detection method which can provide a clean vs. corrupted decision on each successive PPG segment. For motion Artifact detection, we compute four time-domain parameters: (1) standard deviation of peak-to-peak intervals (2) standard deviation of peak-to-peak amplitudes (3) standard deviation of systolic and diastolic interval ratios, and (4) mean standard deviation of pulse shape. We have adopted a support vector machine (SVM) which takes these parameters from clean and corrupted PPG signals and builds a decision boundary to classify them. We apply several distinct features of the PPG data to enhance classification performance. The algorithm we developed was verified on PPG data segments recorded by simulation, laboratory-controlled and walking/stair-climbing experiments, respectively, and we compared several well-established MNA detection methods to our proposed algorithm. All compared detection algorithms were evaluated in terms of motion Artifact detection accuracy, heart rate (HR) error, and oxygen saturation (SpO2) error. For laboratory controlled finger, forehead recorded PPG data and daily-activity movement data, our proposed algorithm gives 94.4, 93.4, and 93.7% accuracies, respectively. Significant reductions in HR and SpO2 errors (2.3 bpm and 2.7%) were noted when the Artifacts that were identified by SVM-MNA were removed from the original signal than without (17.3 bpm and 5.4%). The accuracy and error values of our proposed method were significantly higher and lower, respectively, than all other detection methods. Another advantage of our method is its ability to provide highly accurate onset and offset detection times of MNAs. This capability is important for an automated approach to signal reconstruction of only those data points that need to be reconstructed, which is the subject of the companion paper to this article. Finally, our MNA detection algorithm is real-time realizable as the computational speed on the 7-s PPG data segment was found to be only 7 ms with a Matlab code.