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Bondala VR, Komalla AR. An efficient model for extracting respiratory and blood oxygen saturation data from photoplethysmogram signals by removing motion artifacts using heuristic-aided ensemble learning model. Comput Biol Med 2024; 180:108911. [PMID: 39089111 DOI: 10.1016/j.compbiomed.2024.108911] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2024] [Revised: 07/02/2024] [Accepted: 07/15/2024] [Indexed: 08/03/2024]
Abstract
Patients with surgical, pulmonary, and cardiac problems, continual monitoring of Oxygen Saturation of a Person (SpO2) and Respiratory Rate (RR) is essential. Similarly, the persons with cardiopulmonary health issues, RR estimation is crucial. The performance of the ventilator assistance and lung medicines are evaluated using SpO2 and RR. For the persons, those who are living alone with respiratory illnesses need a compulsory estimation of RR. In case of serious illness, the RR might face abrupt changes. The immobility of the disturbance and RR makes the RR evaluation from the PhotoPlethysmoGraphic (PPG) signals is a difficult challenge. So, an efficient RR and SpO2 estimation framework from the PPG signal using the deep learning method is developed in this paper. At first, the PPG signal is collected from standard data sources. The collected PPG signals undergo signal pre-processing. The pre-processing procedures include Motion Artifacts (MA) removal and filtering techniques. The pre-processed signals are split into distinct windows. From the split windows of the signals, the spectral features, RR, and Respiratory Peak Variance (RPV) features are extracted. The retrieved features are selected optimally with the help of Advanced Golden Tortoise Beetle Optimizer (AGTBO). The weights are chosen optimally with the same AGTBO. The optimally selected features are fused with the optimal features to get the weighted optimal features. These weighted optimal features are fed into the Ensemble Learning-based RR and SpO2 Estimation Network (ELRR-SpO2EN). The ensemble learning model is developed by combining Multilayer Perceptron (MLP), AdaBoost, and Attention-based Long Short Term Memory (A-LSTM). The performance of the developed RR and SpO2 estimation model is compared with other existing techniques. The experimental analysis results revealed that the proposed AGTBO-ELRR-SpO2EN model attained 96 % accuracy for the second dataset, which is higher than the conventional models such as MLP (90 %), Adaboost (92 %), A-LSTM (92 %), and MLP-ADA-ALSTM (94 %). Thus, it has been confirmed that the designed RR and SpO2 estimation framework from PPG signals is more efficient than the other conventional models.
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Affiliation(s)
- Venumaheswar Rao Bondala
- Department of E&I Engineering, Kakatiya Institute of Technology and Science, Warangal, Koukonda, Telangana, 506015, India.
| | - Ashoka Reddy Komalla
- Department of ECE, Kakatiya Institute of Technology and Science, Warangal, Koukonda, Telangana, 506015, India.
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Qi Y, Zhang A, Ma Y, Wang H, Li J. Interference source-based quality assessment method for postauricular photoplethysmography signals. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104751] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/27/2023]
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Zheng X, Dwyer VM, Barrett LA, Derakhshani M, Hu S. Rapid Vital Sign Extraction for Real-Time Opto-Physiological Monitoring at Varying Physical Activity Intensity Levels. IEEE J Biomed Health Inform 2023; 27:3107-3118. [PMID: 37071520 DOI: 10.1109/jbhi.2023.3268240] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/19/2023]
Abstract
Robustness of physiological parameters obtained from photoplethysmographic (PPG) signals is highly dependent on a signal quality that is often affected by the motion artefacts (MAs) generated during physical activity. This study aims to suppress MAs and obtain reliable physiological readings using the part of the pulsatile signal, captured by a multi-wavelength illumination optoelectronic patch sensor (mOEPS), that minimizes the residual between the measured signal and the motion estimates obtained from an accelerometer. The minimum residual (MR) method requires the simultaneous collection of (1) multiple wavelength data from the mOEPS, and (2) motion reference signals from a triaxial accelerometer attached to the mOEPS. The MR method suppresses those frequencies associated with motion in a manner that is easily embedded on a microprocessor. The performance of the method in reducing both in-band and out-of-band frequencies of MAs is evaluated through two protocols with 34 subjects engaged in the study. The MA-suppressed PPG signal, obtained through MR, enables the calculation of the heart rate (HR) with an average absolute error of 1.47 beats/min for the IEEE-SPC datasets, and the calculation of HR and respiration rate (RR) to 1.44 beats/min and 2.85 breaths/min respectively for our in-house datasets. Oxygen saturation (SpO 2) levels calculated from the minimum residual wave forms were consistently [Formula: see text]. The comparison with the reference HR and RR show errors with an absolute accuracy of [Formula: see text] and the Pearson correlation ( R) for HR and RR are 0.9976 and 0.9118, respectively. These outcomes demonstrate that MR is capable of effective suppression of MAs for a range of physical activity intensities and to achieve real-time signal processing for wearable health monitoring.
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Jeong Y, Park J, Kwon SB, Lee SE. Photoplethysmography-Based Distance Estimation for True Wireless Stereo. MICROMACHINES 2023; 14:252. [PMID: 36837951 PMCID: PMC9962750 DOI: 10.3390/mi14020252] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 01/11/2023] [Accepted: 01/17/2023] [Indexed: 06/18/2023]
Abstract
Recently, supplying healthcare services with wearable devices has been investigated. To realize this for true wireless stereo (TWS), which has limited resources (e.g. space, power consumption, and area), implementing multiple functions with one sensor simultaneously is required. The Photoplethysmography (PPG) sensor is a representative healthcare sensor that measures repeated data according to the heart rate. However, since the PPG data are biological, they are influenced by motion artifact and subject characteristics. Hence, noise reduction is needed for PPG data. In this paper, we propose the distance estimation algorithm for PPG signals of TWS. For distance estimation, we designed a waveform adjustment (WA) filter that minimizes noise while maintaining the relationship between before and after data, a lightweight deep learning model called MobileNet, and a PPG monitoring testbed. The number of criteria for distance estimation was set to three. In order to verify the proposed algorithm, we compared several metrics with other filters and AI models. The highest accuracy, precision, recall, and f1 score of the proposed algorithm were 92.5%, 92.6%, 92.8%, and 0.927, respectively, when the signal length was 15. Experimental results of other algorithms showed higher metrics than the proposed algorithm in some cases, but the proposed model showed the fastest inference time.
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Respiratory analysis during sleep using a chest-worn accelerometer: A machine learning approach. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.104014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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Adaptive notch-filtration to effectively recover photoplethysmographic signals during physical activity. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103303] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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Han D, Bashar SK, Lázaro J, Mohagheghian F, Peitzsch A, Nishita N, Ding E, Dickson EL, DiMezza D, Scott J, Whitcomb C, Fitzgibbons TP, McManus DD, Chon KH. A Real-Time PPG Peak Detection Method for Accurate Determination of Heart Rate during Sinus Rhythm and Cardiac Arrhythmia. BIOSENSORS 2022; 12:82. [PMID: 35200342 PMCID: PMC8869811 DOI: 10.3390/bios12020082] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Revised: 01/25/2022] [Accepted: 01/27/2022] [Indexed: 01/20/2023]
Abstract
OBJECTIVE We have developed a peak detection algorithm for accurate determination of heart rate, using photoplethysmographic (PPG) signals from a smartwatch, even in the presence of various cardiac rhythms, including normal sinus rhythm (NSR), premature atrial contraction (PAC), premature ventricle contraction (PVC), and atrial fibrillation (AF). Given the clinical need for accurate heart rate estimation in patients with AF, we developed a novel approach that reduces heart rate estimation errors when compared to peak detection algorithms designed for NSR. METHODS Our peak detection method is composed of a sequential series of algorithms that are combined to discriminate the various arrhythmias described above. Moreover, a novel Poincaré plot scheme is used to discriminate between basal heart rate AF and rapid ventricular response (RVR) AF, and to differentiate PAC/PVC from NSR and AF. Training of the algorithm was performed only with Samsung Simband smartwatch data, whereas independent testing data which had more samples than did the training data were obtained from Samsung's Gear S3 and Galaxy Watch 3. RESULTS The new PPG peak detection algorithm provides significantly lower average heart rate and interbeat interval beat-to-beat estimation errors-30% and 66% lower-and mean heart rate and mean interbeat interval estimation errors-60% and 77% lower-when compared to the best of the seven other traditional peak detection algorithms that are known to be accurate for NSR. Our new PPG peak detection algorithm was the overall best performers for other arrhythmias. CONCLUSION The proposed method for PPG peak detection automatically detects and discriminates between various arrhythmias among different waveforms of PPG data, delivers significantly lower heart rate estimation errors for participants with AF, and reduces the number of false negative peaks. SIGNIFICANCE By enabling accurate determination of heart rate despite the presence of AF with rapid ventricular response or PAC/PVCs, we enable clinicians to make more accurate recommendations for heart rate control from PPG data.
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Affiliation(s)
- Dong Han
- Department of Biomedical Engineering, University of Connecticut, Storrs, CT 06269, USA; (D.H.); (F.M.); (A.P.)
| | - Syed Khairul Bashar
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA;
| | - Jesús Lázaro
- BSICoS Group, Aragon Institute of Engineering Research (I3A), IIS Aragon, University of Zaragoza, 50018 Zaragoza, Spain;
- Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), 28029 Madrid, Spain
| | - Fahimeh Mohagheghian
- Department of Biomedical Engineering, University of Connecticut, Storrs, CT 06269, USA; (D.H.); (F.M.); (A.P.)
| | - Andrew Peitzsch
- Department of Biomedical Engineering, University of Connecticut, Storrs, CT 06269, USA; (D.H.); (F.M.); (A.P.)
| | - Nishat Nishita
- Department of Public Health Sciences, University of Connecticut Health, Farmington, CT 06030, USA;
| | - Eric Ding
- Division of Cardiology, University of Massachusetts Medical School, Worcester, MA 01655, USA; (E.D.); (D.D.); (J.S.); (T.P.F.); (D.D.M.)
| | - Emily L. Dickson
- College of Osteopathic Medicine, Des Moines University, Des Moines, IA 50312, USA;
| | - Danielle DiMezza
- Division of Cardiology, University of Massachusetts Medical School, Worcester, MA 01655, USA; (E.D.); (D.D.); (J.S.); (T.P.F.); (D.D.M.)
| | - Jessica Scott
- Division of Cardiology, University of Massachusetts Medical School, Worcester, MA 01655, USA; (E.D.); (D.D.); (J.S.); (T.P.F.); (D.D.M.)
| | - Cody Whitcomb
- School of Medicine, Tufts University, Medford, MA 02155, USA;
| | - Timothy P. Fitzgibbons
- Division of Cardiology, University of Massachusetts Medical School, Worcester, MA 01655, USA; (E.D.); (D.D.); (J.S.); (T.P.F.); (D.D.M.)
| | - David D. McManus
- Division of Cardiology, University of Massachusetts Medical School, Worcester, MA 01655, USA; (E.D.); (D.D.); (J.S.); (T.P.F.); (D.D.M.)
| | - Ki H. Chon
- Department of Biomedical Engineering, University of Connecticut, Storrs, CT 06269, USA; (D.H.); (F.M.); (A.P.)
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