A Simple and Robust Method for Determining the Quality of Cardiovascular Signals Using the Signal Similarity

Jang, Dae-Geun; Kwon, Ui Kun; Yoon, Seung Keun; Park, Changsoon; Ku, Yun Hyi; Noh, Seung Woo; Kim, Youn Ho

doi:10.1109/embc.2018.8512341

Cited by 6 publications

(12 citation statements)

References 10 publications

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“…Several researchers have already developed automatic methods for PPG signal quality assessment. Table 1 shows a selection of their works [ 24 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 ].…”

Section: Introductionmentioning

confidence: 99%

“…Recent works used PPG signals recorded by wrist-worn WDs in a real-world context and collected PPG pulses prone to lifelike motion artifacts [ 29 , 30 , 31 ] to overcome these limitations. Unfortunately, in these studies, no information is provided about the motion of the sensors, so it is unclear to what degree the related method is robust to daily life motion artifacts.…”

Section: Introductionmentioning

confidence: 99%

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Wrist Photoplethysmography Signal Quality Assessment for Reliable Heart Rate Estimate and Morphological Analysis

Moscato

Giudice

Massaro

et al. 2022

Sensors

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Photoplethysmographic (PPG) signals are mainly employed for heart rate estimation but are also fascinating candidates in the search for cardiovascular biomarkers. However, their high susceptibility to motion artifacts can lower their morphological quality and, hence, affect the reliability of the extracted information. Low reliability is particularly relevant when signals are recorded in a real-world context, during daily life activities. We aim to develop two classifiers to identify PPG pulses suitable for heart rate estimation (Basic-quality classifier) and morphological analysis (High-quality classifier). We collected wrist PPG data from 31 participants over a 24 h period. We defined four activity ranges based on accelerometer data and randomly selected an equal number of PPG pulses from each range to train and test the classifiers. Independent raters labeled the pulses into three quality levels. Nineteen features, including nine novel features, were extracted from PPG pulses and accelerometer signals. We conducted ten-fold cross-validation on the training set (70%) to optimize hyperparameters of five machine learning algorithms and a neural network, and the remaining 30% was used to test the algorithms. Performances were evaluated using the full features and a reduced set, obtained downstream of feature selection methods. Best performances for both Basic- and High-quality classifiers were achieved using a Support Vector Machine (Acc: 0.96 and 0.97, respectively). Both classifiers outperformed comparable state-of-the-art classifiers. Implementing automatic signal quality assessment methods is essential to improve the reliability of PPG parameters and broaden their applicability in a real-world context.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Wrist Photoplethysmography Signal Quality Assessment for Reliable Heart Rate Estimate and Morphological Analysis

Moscato

Giudice

Massaro

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…Many studies have been done to improve the PPG physical sensing components so as to decrease motion artefacts, yet more analysis is needed to determine which sensor location is the best for monitoring heart rates in animals. Recently, many studies (e.g., [9,12,13]) have focused on determining the clinical reliability of the PPG measurements and the optimal signal quality index (SQI) in assessing the PPG signals, especially for mobile health and real-time applications. The present paper presents a proof-of-concept study, with the main goal of testing the possibility of using the PPG-based technique to assess a pig′s heart rate and to determine the optimal location on the pig′s body that gives the best PPG signal quality.…”

Section: Experimental Setup and Measurementsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Approach towards Motion-Tolerant PPG-Based Algorithm for Real-Time Heart Rate Monitoring of Moving Pigs

Youssef

Fernández

Wassermann

et al. 2020

Sensors

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Animal welfare remains a very important issue in the livestock sector, but monitoring animal welfare in an objective and continuous way remains a serious challenge. Monitoring animal welfare, based upon physiological measurements instead of the audio–visual scoring of behaviour, would be a step forward. One of the obvious physiological signals related to welfare and stress is heart rate. The objective of this research was to measure heart rate (beat per minutes) in pigs with technology that soon will be affordable. Affordable heart rate monitoring is done today at large scale on humans using the Photo Plethysmography (PPG) technology. We used PPG sensors on a pig′s body to test whether it allows the retrieval of a reliable heart rate signal. A continuous wavelet transform (CWT)-based algorithm is developed to decouple the cardiac pulse waves from the pig. Three different wavelets, namely second, fourth and sixth order Derivative of Gaussian (DOG), are tested. We show the results of the developed PPG-based algorithm, against electrocardiograms (ECG) as a reference measure for heart rate, and this for an anaesthetised versus a non-anaesthetised animal. We tested three different anatomical body positions (ear, leg and tail) and give results for each body position of the sensor. In summary, it can be concluded that the agreement between the PPG-based heart rate technique and the reference sensor is between 91% and 95%. In this paper, we showed the potential of using the PPG-based technology to assess the pig′s heart rate.

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“…Many works are done to improve the PPG physical sensing components to decrease motion artefacts, yet more analysis is needed to determine which sensor location is the best for monitoring heart rates in animals. Recently, many studies (e.g., [9], [12], [13]) focused on determining the clinical reliability of the PPG measurements and the optimal signal quality index (SQI) for assessing the PPG signals, especially for mobile health and real-time applications.…”

Section: Introductionmentioning

confidence: 99%

An Approach Towards Motion-Tolerant PPG-Based Algorithm for Real-Time Heart Rate Monitoring of Moving Pigs

Youssef

Fernández

Wassermann

et al. 2020

Preprint

View full text Add to dashboard Cite

Animal welfare remains a very important issue in the livestock sector but monitoring animal welfare in an objective and continuous way remains a serious challenge. Monitoring animal welfare based upon physiological measurements instead of audio-visual scoring of behaviour would be a step forward. One of the obvious physiological signals related to welfare and stress is heart rate. The objective of this research was to measure heart rate (beat per minutes) on pigs with technology that soon will be affordable. Affordable heart rate monitoring is done today at large scale on humans using the Photo Plethysmography (PPG) technology. We used PPG sensors on pig’s body to test whether it allows getting reliable heart rate signal. A continuous wavelet transform (CWT)-based algorithm is developed to decouple the cardiac pulse waves from the pig. Three different wavelets, namely 2nd, 4th and 6th order Derivative of Gaussian (DOG, are tested. We show results of the developed PPG-based algorithm against electrocardiograms (ECG) as a reference measure for heart rate and this for an anesthetized versus a non-anesthetised animal. We tested three different anatomical body positions (ear, leg and tail) and give results for each body position of the sensor. In summary, it can be concluded that the agreement between PPG-based heart rate technique and reference sensor goes from 91 to 95 percentage. In this paper we showed the potential of using the PPG-based technology to assess pig’s hear rate.

show abstract

A Simple and Robust Method for Determining the Quality of Cardiovascular Signals Using the Signal Similarity

Cited by 6 publications

References 10 publications

Wrist Photoplethysmography Signal Quality Assessment for Reliable Heart Rate Estimate and Morphological Analysis

Wrist Photoplethysmography Signal Quality Assessment for Reliable Heart Rate Estimate and Morphological Analysis

An Approach towards Motion-Tolerant PPG-Based Algorithm for Real-Time Heart Rate Monitoring of Moving Pigs

An Approach Towards Motion-Tolerant PPG-Based Algorithm for Real-Time Heart Rate Monitoring of Moving Pigs

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