Comparison of different signal processing methods for reducing artifacts from photoplethysmograph signal

Naraharisetti, Kali; Bawa, Manan

doi:10.1109/eit.2011.5978571

Cited by 33 publications

(25 citation statements)

References 10 publications

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“…Very common example of online databases is PhysioNet [90,91] database which consists a wide range of physiological data sets with categorized and robust annotations for complex clinical signals. Several papers in the literature have used two main data sets in physioNet bank, MIMIC data sets (e.g., [53,60,61]) and MIT data sets (e.g., [27,54,92]) that contain the time series of patients vital signs obtained from hospital medical information systems. Simulated sensor data: For the sake of having a wide controlled analysis system, few works have designed and tested their data mining methods through shapely simulated physiological data [49]. Data simulation would be useful when the more focus of data processing method is on the efficiency and robustness of information extraction [28,57] rather than handling real-world data including the artifact, errors, conditions of data gathering environment, etc.…”

Section: Data Sets and Their Propertiesmentioning

confidence: 99%

Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges

Banaee

Ahmed

Loutfi

2013

Sensors

382

180

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The past few years have witnessed an increase in the development of wearable sensors for health monitoring systems. This increase has been due to several factors such as development in sensor technology as well as directed efforts on political and stakeholder levels to promote projects which address the need for providing new methods for care given increasing challenges with an aging population. An important aspect of study in such system is how the data is treated and processed. This paper provides a recent review of the latest methods and algorithms used to analyze data from wearable sensors used for physiological monitoring of vital signs in healthcare services. In particular, the paper outlines the more common data mining tasks that have been applied such as anomaly detection, prediction and decision making when considering in particular continuous time series measurements. Moreover, the paper further details the suitability of particular data mining and machine learning methods used to process the physiological data and provides an overview of the properties of the data sets used in experimental validation. Finally, based on this literature review, a number of key challenges have been outlined for data mining methods in health monitoring systems.

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Section: Data Sets and Their Propertiesmentioning

confidence: 99%

Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges

Banaee

Ahmed

Loutfi

2013

Sensors

382

180

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“…2, which depends on the sampling rate. If a sampling rate is 256 Hz, then level N is set to 6, so the lowest frequency band of the decomposed low pass filter becomes between 0 and 4 Hz [5], [7]. The decomposition procedure consists of two digital filters and two down samples by 2.…”

Section: Proposal Algorithmmentioning

confidence: 99%

Movement noise cancellation in PPG signals

Ban

Kwon

2016

2016 IEEE International Conference on Consumer Electronics (ICCE)

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“…The approach based on the wavelet transform [ 12 , 13 ] has a drawback that its performance is dependent on the threshold value that should be chosen because the reconstructed signal waveform relies on this value of threshold which may not be optimal in all users. Therefore, further studies need to be done on selecting the optimal threshold value for better and more robust performance [ 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

“…The Kalman filter approach has a difficulty of finding suitable initial values for its filter coefficients since the system model in the Kalman filter requires information on certain targets, including the motion artifact variance and PPG signal’s characteristics. It is known that the estimation performance is similar to that of ANC [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

“…This approach basically requires the assumption that heartbeat rate and motion artifact signals are independent of one another. However, there is actual overlapping of the frequency range, even if there is an increase in independency to a certain degree due to the preprocess procedure, and its performance degrades significantly [ 14 , 19 ]. For mitigating this degradation, the ICA approach, presented by Kim and Yoo, is to employ preprocessing such as block interleaving [ 20 ].…”

Section: Introductionmentioning

confidence: 99%

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Characterization of Quadratic Nonlinearity between Motion Artifact and Acceleration Data and its Application to Heartbeat Rate Estimation

Kim

Park

2017

Sensors

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Accelerometers are applied to various applications to collect information about movements of other sensors deployed at diverse fields ranging from underwater area to human body. In this study, we try to characterize the nonlinear relationship between motion artifact and acceleration data. The cross bicoherence test and the Volterra filter are used as the approaches to detection and modeling. We use the cross bicoherence test to directly detect in the frequency domain and we indirectly identify the nonlinear relationship by improving the performance of eliminating motion artifact in heartbeat rate estimation using a nonlinear filter, the second-order Volterra filter. In the experiments, significant bicoherence values are observed through the cross bicoherence test between the photoplethysmogram (PPG) signal contaminated with motion artifact and the acceleration sensor data. It is observed that for each dataset, the heartbeat rate estimation based on the Volterra filter is superior to that of the linear filter in terms of average absolute error. Furthermore, the leave one out cross-validation (LOOCV) is employed to develop an optimal structure of the Volterra filter for the total datasets. Due to lack of data, the developed Volterra filter does not demonstrate significant difference from the optimal linear filter in terms of t-test. Through this study, it can be concluded that motion artifact may have a quadaratical relationship with acceleration data in terms of bicoherence and more experimental data are required for developing a robust and efficient model for the relationship.

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Comparison of different signal processing methods for reducing artifacts from photoplethysmograph signal

Cited by 33 publications

References 10 publications

Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges

Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges

Movement noise cancellation in PPG signals

Characterization of Quadratic Nonlinearity between Motion Artifact and Acceleration Data and its Application to Heartbeat Rate Estimation

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