Smartphone-Based Recognition of Heart Failure by Means of Microelectromechanical Sensors DOI
François Haddad, Antti Saraste, Kristiina Santalahti

и другие.

JACC Heart Failure, Год журнала: 2024, Номер 12(6), С. 1030 - 1040

Опубликована: Апрель 3, 2024

Язык: Английский

Blood pressure estimation from appropriate and inappropriate PPG signals using A whole-based method DOI
Seyedeh Somayyeh Mousavi,

Mohammad Firouzmand,

Mostafa Charmi

и другие.

Biomedical Signal Processing and Control, Год журнала: 2018, Номер 47, С. 196 - 206

Опубликована: Авг. 29, 2018

Язык: Английский

Процитировано

198

Photoplethysmogram Analysis and Applications: An Integrative Review DOI Creative Commons
Junyung Park, Hyeon Seok Seok, Sang-Su Kim

и другие.

Frontiers in Physiology, Год журнала: 2022, Номер 12

Опубликована: Март 1, 2022

Beyond its use in a clinical environment, photoplethysmogram (PPG) is increasingly used for measuring the physiological state of an individual daily life. This review aims to examine existing research on concerning generation mechanisms, measurement principles, applications, noise definition, pre-processing techniques, feature detection and post-processing techniques processing, especially from engineering point view. We performed extensive search with PubMed, Google Scholar, Institute Electrical Electronics Engineers (IEEE), ScienceDirect, Web Science databases. Exclusion conditions did not include year publication, but articles published English were excluded. Based 118 articles, we identified four main topics enabling PPG: (A) PPG waveform, (B) features applications including basic based original combined PPG, derivative (C) motion artifact baseline wandering hypoperfusion, (D) signal processing preprocessing, peak detection, quality index. The application field has been extending mobile environment. Although there no standardized pipeline as data are acquired accumulated various ways, recently proposed machine learning-based method expected offer promising solution.

Язык: Английский

Процитировано

183

Wearable Photoplethysmography for Cardiovascular Monitoring DOI Creative Commons
Peter Charlton, P. A. Kyriacou, Jonathan Mant

и другие.

Proceedings of the IEEE, Год журнала: 2022, Номер 110(3), С. 355 - 381

Опубликована: Март 1, 2022

Smart wearables provide an opportunity to monitor health in daily life and are emerging as potential tools for detecting cardiovascular disease (CVD). Wearables such fitness bands smartwatches routinely the photoplethysmogram signal, optical measure of arterial pulse wave that is strongly influenced by heart blood vessels. In this survey, we summarize fundamentals wearable photoplethysmography its analysis, identify clinical applications, outline pressing directions future research order realize full tackling CVD.

Язык: Английский

Процитировано

94

Synchronized wearables for the detection of haemodynamic states via electrocardiography and multispectral photoplethysmography DOI
Daniel Franklin, Andreas Tzavelis, Jong Yoon Lee

и другие.

Nature Biomedical Engineering, Год журнала: 2023, Номер 7(10), С. 1229 - 1241

Опубликована: Окт. 2, 2023

Язык: Английский

Процитировано

50

Clinical assessment of a non-invasive wearable MEMS pressure sensor array for monitoring of arterial pulse waveform, heart rate and detection of atrial fibrillation DOI Creative Commons
Matti Kaisti, Tuukka Panula, Joni Leppänen

и другие.

npj Digital Medicine, Год журнала: 2019, Номер 2(1)

Опубликована: Май 14, 2019

There is an unmet clinical need for a low cost and easy to use wearable devices continuous cardiovascular health monitoring. A flexible wristband, based on microelectromechanical sensor (MEMS) elements array was developed support this need. The performance of the device in monitoring investigated by (i) comparing arterial pressure waveform recordings gold standard, invasive catheter recording (n = 18), (ii) analyzing ability detect irregularities rhythm 7), (iii) measuring heartrate accuracy 31). Arterial waveforms carry important physiological information comparison study revealed that made with standard resulted almost identical (r 0.9-0.99) pulse waveforms. can measure heart possible it. clustering analysis demonstrates perfect classification between atrial fibrillation (AF) sinus rhythm. showed near beat-to-beat (sensitivity 99.1%, precision 100%) healthy subjects. In contrast, detection from coronary artery disease patients challenging, but averaged extracted successfully (95% CI: -1.2 1.1 bpm). conclusion, results indicate could be useful remote diseases personalized medicine.

Язык: Английский

Процитировано

135

Blood Pressure Estimation Using Photoplethysmogram Signal and Its Morphological Features DOI
Navid Hasanzadeh, Mohammad Mahdi Ahmadi,

Hoda Mohammadzade

и другие.

IEEE Sensors Journal, Год журнала: 2019, Номер 20(8), С. 4300 - 4310

Опубликована: Дек. 24, 2019

In this paper, we present a machine learning model to estimate the blood pressure (BP) of person using only his photoplethysmogram (PPG) signal. We propose algorithms better detect some critical points PPG signal, such as systolic and diastolic peaks, dicrotic notch inflection point. These are applicable different signal morphologies improve precision feature extraction. show that logarithm reflection index, ratio low- high-frequency components heart rate (HR) variability product HR multiplied by modified Normalized Pulse Volume (mNPV) key features in accurately estimating BP Our proposed method has achieved higher accuracies compared previously reported methods use For BP, correlation coefficient between estimated values real is 0.78, mean absolute error 8.22 mmHg, their standard deviation 10.38 mmHg. 0.72, 4.17 4.22 The results fall within Grade A for diastolic, C B based on BHS standard.

Язык: Английский

Процитировано

99

Geometric leaf classification DOI
Cem Kalyoncu, Önsen Toygar

Computer Vision and Image Understanding, Год журнала: 2014, Номер 133, С. 102 - 109

Опубликована: Ноя. 20, 2014

Язык: Английский

Процитировано

97

RobustPeriod: Robust Time-Frequency Mining for Multiple Periodicity Detection DOI
Qingsong Wen, Kai He, Liang Sun

и другие.

Proceedings of the 2022 International Conference on Management of Data, Год журнала: 2021, Номер unknown, С. 2328 - 2337

Опубликована: Июнь 9, 2021

Periodicity detection is a crucial step in time series tasks, including monitoring and forecasting of metrics many areas, such as IoT applications self-driving database management system. In these applications, multiple periodic components exist are often interlaced with each other. Such dynamic complicated patterns make the accurate periodicity difficult. addition, other series, trend, outliers noises, also pose additional challenges for detection. this paper, we propose robust general framework Our algorithm applies maximal overlap discrete wavelet transform to into temporal-frequency scales that different can be isolated. We rank them by variance, then at scale detect single our proposed Huber-periodogram Huber-ACF robustly. rigorously prove theoretical properties justify use Fisher's test on To further refine detected periods, compute unbiased autocorrelation function based Wiener-Khinchin theorem from improved robustness efficiency. Experiments synthetic real-world datasets show outperforms popular ones both

Язык: Английский

Процитировано

57

Detecting beats in the photoplethysmogram: benchmarking open-source algorithms DOI Creative Commons
Peter Charlton,

Kevin Kotzen,

Elisa Mejía‐Mejía

и другие.

Physiological Measurement, Год журнала: 2022, Номер 43(8), С. 085007 - 085007

Опубликована: Июль 19, 2022

Abstract The photoplethysmogram (PPG) signal is widely used in pulse oximeters and smartwatches. A fundamental step analysing the PPG detection of heartbeats. Several beat algorithms have been proposed, although it not clear which performs best. Objective: This study aimed to: (i) develop a framework with to design test detectors; (ii) assess performance detectors different use cases; (iii) investigate how their affected by patient demographics physiology. Approach: Fifteen were assessed against electrocardiogram-derived heartbeats using data from eight datasets. Performance was F 1 score, combines sensitivity positive predictive value. Main results: Eight performed well absence movement scores ≥90% on hospital wearable collected at rest. Their poorer during exercise 55%–91%; neonates than adults 84%–96% compared 98%–99% adults; atrial fibrillation (AF) 92%–97% AF 99%–100% normal sinus rhythm. Significance: Two denoted ‘MSPTD’ ‘qppg’ best, complementary characteristics. evidence can be inform choice detector algorithm. algorithms, datasets, assessment are freely available.

Язык: Английский

Процитировано

44

Overhead Power Line Tension Estimation Method Using Accelerometers DOI Creative Commons
Sang‐Hyun Kim, Kwan-Ho Chun

Energies, Год журнала: 2025, Номер 18(1), С. 181 - 181

Опубликована: Янв. 3, 2025

Overhead power lines are important components of grids, and the status transmission line equipment directly affects safe reliable operation grids. In order to guarantee efficient usage grid, tension overhead is an parameter be measured. The can calculated from modal frequency, but measured acceleration data obtained accelerometer severely contaminated with noises. this paper, a multiscale-based peak detection (M-AMPD) algorithm used find possible frequencies in spectral density data. To obtain noise-free signal, median absolute deviations baseline correction (MAD-BS) applied. An accurate estimation for by iteration MAD-BS reduction frequency range technique. iterative technique improves accuracy estimated lines. contribute improving reliability efficiency grid. proposed implemented MATLAB R2020a verified comparison tensiometer.

Язык: Английский

Процитировано

1