Comparison of different methods of measuring similarity in physiologic time series

Kianimajd, A.; Ruano, M.G.; Carvalho, Paulo; Henriques, J.; Rocha, Teresa; Paredes, S.; Ruano, A. E.

doi:10.1016/j.ifacol.2017.08.2479

Cited by 47 publications

(15 citation statements)

References 25 publications

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“…According to [34], the Pearson’s correlation coefficient is the most robust metric when measuring the similarity in physiological time series—where robustness is understood as insensitivity to small variations. However, Pearson’s r is highly sensitive to outliers and only considers linear relationships.…”

Section: Sensor Benchmark Methods—related Workmentioning

confidence: 99%

Wearables and the Quantified Self: Systematic Benchmarking of Physiological Sensors

Sagl

Resch

Petutschnig

et al. 2019

Sensors

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Wearable sensors are increasingly used in research, as well as for personal and private purposes. A variety of scientific studies are based on physiological measurements from such rather low-cost wearables. That said, how accurate are such measurements compared to measurements from well-calibrated, high-quality laboratory equipment used in psychological and medical research? The answer to this question, undoubtedly impacts the reliability of a study’s results. In this paper, we demonstrate an approach to quantify the accuracy of low-cost wearables in comparison to high-quality laboratory sensors. We therefore developed a benchmark framework for physiological sensors that covers the entire workflow from sensor data acquisition to the computation and interpretation of diverse correlation and similarity metrics. We evaluated this framework based on a study with 18 participants. Each participant was equipped with one high-quality laboratory sensor and two wearables. These three sensors simultaneously measured the physiological parameters such as heart rate and galvanic skin response, while the participant was cycling on an ergometer following a predefined routine. The results of our benchmarking show that cardiovascular parameters (heart rate, inter-beat interval, heart rate variability) yield very high correlations and similarities. Measurement of galvanic skin response, which is a more delicate undertaking, resulted in lower, but still reasonable correlations and similarities. We conclude that the benchmarked wearables provide physiological measurements such as heart rate and inter-beat interval with an accuracy close to that of the professional high-end sensor, but the accuracy varies more for other parameters, such as galvanic skin response.

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Section: Sensor Benchmark Methods—related Workmentioning

confidence: 99%

Wearables and the Quantified Self: Systematic Benchmarking of Physiological Sensors

Sagl

Resch

Petutschnig

et al. 2019

Sensors

View full text Add to dashboard Cite

show abstract

“…We used MSE and correlation as similarity measures to compare between the original PPG signal and synthesized. The reason behind using MSE is it is a very popular distance measure for quantifying similarity 24 , and the reason behind using Pearson’s Correlation coefficient is the robustness in quantifying morphological changes in time series physiological signals such as PPG 25 . To obtain optimal parameters for the model, the two similarity measures were used simultaneously.…”

Section: Discussionmentioning

confidence: 99%

Synthetic photoplethysmogram generation using two Gaussian functions

Tang

Chen

Ward

et al. 2020

Sci Rep

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Evaluating the performance of photoplethysmogram (PPG) event detection algorithms requires a large number of PPG signals with different noise levels and sampling frequencies. As publicly available PPG databases provide few options, artificially constructed PPG signals can also be used to facilitate this evaluation. Here, we propose a dynamic model to synthesize PPG over specified time durations and sampling frequencies. In this model, a single pulse was simulated by two Gaussian functions. Additionally, the beat-to-beat intervals were simulated using a normal distribution with a specific mean value and a specific standard deviation value. To add periodicity and to generate a complete signal, the circular motion principle was used. We synthesized three classes of pulses by emulating three different templates: excellent (systolic and diastolic waves are salient), acceptable (systolic and diastolic waves are not salient), and unfit (systolic and diastolic waves are noisy). The optimized model fitting of the Gaussian functions to the templates yielded 0.99, 0.98, and 0.85 correlations between the template and synthetic pulses for the excellent, acceptable, and unfit classes, respectively, with mean square errors of 0.001, 0.003, and 0.017, respectively. By comparing the heart rate variability of real PPG and randomly synthesized PPG for 5 min in 116 records from the MIMIC III database, strong correlations were found in SDNN,

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“…We adopted two analysis approaches: one analyzing each signal’s correlation in the test group to determine the behavior of the signals in a heterogeneous test group, as any GSR signal-based stress detection system should work, the other analyzing the correlation between the signals measured in each respondent and comparing them among the test group. The literature documents that the Pearson’s correlation method fits with time series data analysis for physiological signals [ 46 ], so this was used for both workflows. We carried out all of the data processing and plots in the statistical computing software RStudio.…”

Section: Methodsmentioning

confidence: 99%

Correlation Analysis of Different Measurement Places of Galvanic Skin Response in Test Groups Facing Pleasant and Unpleasant Stimuli

Sanchez-Comas

Synnes

Molina-Estren

et al. 2021

Sensors

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The galvanic skin response (GSR; also widely known as electrodermal activity (EDA)) is a signal for stress-related studies. Given the sparsity of studies related to the GSR and the variety of devices, this study was conducted at the Human Health Activity Laboratory (H2AL) with 17 healthy subjects to determine the variability in the detection of changes in the galvanic skin response among a test group with heterogeneous respondents facing pleasant and unpleasant stimuli, correlating the GSR biosignals measured from different body sites. We experimented with the right and left wrist, left fingers, the inner side of the right foot using Shimmer3GSR and Empatica E4 sensors. The results indicated the most promising homogeneous places for measuring the GSR, namely, the left fingers and right foot. The results also suggested that due to a significantly strong correlation among the inner side of the right foot and the left fingers, as well as the moderate correlations with the right and left wrists, the foot may be a suitable place to homogenously measure a GSR signal in a test group. We also discuss some possible causes of weak and negative correlations from anomalies detected in the raw data possibly related to the sensors or the test group, which may be considered to develop robust emotion detection systems based on GRS biosignals.

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Comparison of different methods of measuring similarity in physiologic time series

Cited by 47 publications

References 25 publications

Wearables and the Quantified Self: Systematic Benchmarking of Physiological Sensors

Wearables and the Quantified Self: Systematic Benchmarking of Physiological Sensors

Synthetic photoplethysmogram generation using two Gaussian functions

Correlation Analysis of Different Measurement Places of Galvanic Skin Response in Test Groups Facing Pleasant and Unpleasant Stimuli

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