PPG pulse direction determination algorithm for PPG waveform inversion by wrist rotation

Choi, Changmok; Ko, Byung-Hoon; Lee, Jong‐Wook; Yoon, Seung Keun; Kwon, Ui-Kun; Kim, Sang Joon; Kim, Younho

doi:10.1109/embc.2017.8037755

Cited by 11 publications

(9 citation statements)

References 14 publications

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“…Currently, LSTM training requires GPU-grade computational power. With current low-power Bluetooth low energy devices [ 11 , 24 , 25 ], it may be possible to acquire PPG data and stream real-time data to a cloud-based GPU server to run online training. Once the weights and biases of the LSTM architecture are found, it may also be possible for an embedded platform to perform the required processing to obtain real-time breathing metric predictions.…”

Section: Discussionmentioning

confidence: 99%

Derivation of Breathing Metrics From a Photoplethysmogram at Rest: Machine Learning Methodology

Prinable¹,

Jones²,

Boland³

et al. 2020

JMIR Mhealth Uhealth

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Background There has been a recent increased interest in monitoring health using wearable sensor technologies; however, few have focused on breathing. The ability to monitor breathing metrics may have indications both for general health as well as respiratory conditions such as asthma, where long-term monitoring of lung function has shown promising utility. Objective In this paper, we explore a long short-term memory (LSTM) architecture and predict measures of interbreath intervals, respiratory rate, and the inspiration-expiration ratio from a photoplethysmogram signal. This serves as a proof-of-concept study of the applicability of a machine learning architecture to the derivation of respiratory metrics. Methods A pulse oximeter was mounted to the left index finger of 9 healthy subjects who breathed at controlled respiratory rates. A respiratory band was used to collect a reference signal as a comparison. Results Over a 40-second window, the LSTM model predicted a respiratory waveform through which breathing metrics could be derived with a bias value and 95% CI. Metrics included inspiration time (–0.16 seconds, –1.64 to 1.31 seconds), expiration time (0.09 seconds, –1.35 to 1.53 seconds), respiratory rate (0.12 breaths per minute, –2.13 to 2.37 breaths per minute), interbreath intervals (–0.07 seconds, –1.75 to 1.61 seconds), and the inspiration-expiration ratio (0.09, –0.66 to 0.84). Conclusions A trained LSTM model shows acceptable accuracy for deriving breathing metrics and could be useful for long-term breathing monitoring in health. Its utility in respiratory disease (eg, asthma) warrants further investigation.

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Section: Discussionmentioning

confidence: 99%

Derivation of Breathing Metrics From a Photoplethysmogram at Rest: Machine Learning Methodology

Prinable¹,

Jones²,

Boland³

et al. 2020

JMIR Mhealth Uhealth

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“…Moreover, there is a lot of confusion about the direction of the original, raw signal's course. For both modes, the received light intensity is inversely proportional to the blood volume by nature, but commonly flipped to be consistent with the associated arterial blood pressure (ABP) [1,8].…”

Section: Photoplethysmographymentioning

confidence: 99%

“…The first one determines the pulses' center of mass which is usually originated at the systolic onset while the diastolic peak is much lighter. Based on Choi et al [8], the second measure compares the steepness of the down and up slopes which are steeper for the systolic than for the diastolic phase.…”

Section: Time Basementioning

confidence: 99%

The quest for raw signals

Wolling

Laerhoven

2020

Proceedings of the Third Workshop on Data: Acquisition to Analysis

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Photoplethysmography is an optical measurement principle which is present in most modern wearable devices such as fitness trackers and smartwatches. As the analysis of physiological signals requires reliable but energy-efficient algorithms, suitable datasets are essential for their development, evaluation, and benchmark. A broad variety of clinical datasets is available with recordings from medical pulse oximeters which traditionally apply transmission mode photoplethysmography at the fingertip or earlobe. However, only few publicly available datasets utilize recent reflective mode sensors which are typically worn at the wrist and whose signals show different characteristics. Moreover, the recordings are often advertised as raw, but then turn out to be preprocessed and filtered while the applied parameters are not stated. In this way, the heart rate and its variability can be extracted, but interesting secondary information from the non-stationary signal is often lost. Consequently, the test of novel signal processing approaches for wearable devices usually implies the gathering of own or the use of inappropriate data. In this paper, we present a multi-varied method to analyze the suitability and applicability of presumably raw photoplethysmography signals. We present an analytical tool which applies 7 decision metrics to characterize 10 publicly available datasets with a focus on less or ideally unfiltered, raw signals. Besides the review, we finally provide a guideline for future datasets, to suit to and to be applicable in digital signal processing, to support the development and evaluation of algorithms for resource-limited wearable devices. CCS CONCEPTS • Human-centered computing → Ubiquitous and mobile devices; • Hardware → Digital signal processing; • Applied computing → Health informatics.

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“…A common characteristic shared by these approaches is their focus on creating a smaller cuff (for instance a finger cuff) envisaged to reduce the hindrance caused due to traditional sphygmomanometer. In addition to these approaches, [33,34,35] represent efforts to introduce novel methods to achieve continuous PPG measurements for blood pressure monitoring such as on a toilet seat which can be beneficial for elderly and less mobile patients. In addition to the above efforts, Kurylyak et al [36] proposed a neural networks based approach to estimate blood pressure from the PPG signal.…”

Section: State Of the Artmentioning

confidence: 99%

Pervasive blood pressure monitoring using Photoplethysmogram (PPG) sensor

Riaz¹,

Azad

Arshad

et al. 2019

Future Generation Computer Systems

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Preventive healthcare requires continuous monitoring of the blood pressure (BP) of patients, which is not feasible using conventional methods. Photoplethysmogram (PPG) signals can be effectively used for this purpose as there is a physiological relation between the pulse width and BP and can be easily acquired using a wearable PPG sensor. However, developing real-time algorithms for wearable technology is a significant challenge due to various conflicting requirements such as high accuracy, computationally constrained devices, and limited power supply. In this paper, we propose a novel feature set for continuous, real-time identification of abnormal BP. This feature set is obtained by identifying the peaks and valleys in a PPG signal (using a peak detection algorithm), followed by the calculation of rising time, falling time and peak-to-peak distance. The histograms of these times are calculated to form a feature set that can be used for classification of PPG signals into one of the two classes: normal or abnormal BP. No public dataset is available for such study and therefore a prototype is developed to collect PPG signals alongside BP measurements. The proposed feature set shows very good performance with an overall accuracy of approximately 95%. Although the proposed feature set is effective, the significance of individual features varies greatly (validated using significance testing) which led us to perform weighted voting of features for classification by performing autoregressive modeling. Our experiments show that the simplest linear classifiers produce very good results indicating the strength of the proposed feature set. The weighted voting improves the results significantly, producing an overall accuracy of about 98%. Conclusively, the PPG signals can be effectively used to identify BP, and the proposed feature set is efficient and computationally feasible for implementation on standalone devices.

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PPG pulse direction determination algorithm for PPG waveform inversion by wrist rotation

Cited by 11 publications

References 14 publications

Derivation of Breathing Metrics From a Photoplethysmogram at Rest: Machine Learning Methodology

Derivation of Breathing Metrics From a Photoplethysmogram at Rest: Machine Learning Methodology

The quest for raw signals

Pervasive blood pressure monitoring using Photoplethysmogram (PPG) sensor

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