1
|
Rajeshkumar C, Soundar KR. TO-LAB model: Real time Touchless Lung Abnormality detection model using USRP based machine learning algorithm. Technol Health Care 2024:THC240149. [PMID: 38968032 DOI: 10.3233/thc-240149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/07/2024]
Abstract
BACKGROUND Due to the increasing prevalence of respiratory diseases and the importance of early diagnosis. The need for non-invasive and touchless medical diagnostic solutions has become increasingly crucial in modern healthcare to detect lung abnormalities. OBJECTIVE Existing methods for lung abnormality detection often rely on invasive and time-consuming procedures limiting their effectiveness in real-time diagnosis. This work introduces a novel Touchless Lung Abnormality (TO-LAB) detection model utilizing universal software radio peripherals (USRP) and machine learning algorithms. METHODS The TO-LAB model integrates a blood pressure meter and an RGB-D depth-sensing camera to gather individual data without physical contact. Heart rate (HR) is analyzed through image conversion to IPPG signals, while blood pressure (BP) is obtained via analog conversion from the blood pressure meter. This touchless imaging setup facilitates the extraction of essential signal features crucial for respiratory pattern analysis. Advanced computer vision algorithms like Mel-frequency cepstral coefficients (MFCC) and Principal Component Analysis (PCA) process the acquired data to focus on breathing abnormalities. These features are then combined and inputted into a machine learning-based Multi-class SVM for breathing activity analysis. The Multi-class SVM categorizes breathing abnormalities as normal, shallow, or elevated based on the fused features. The efficiency of this TO-LAB model is evaluated with the simulated and real-time data. RESULTS According to the findings, the proposed TO-LAB model attains the maximum accuracy of 96.15% for real time data; however, the accuracy increases to 99.54% for simulated data for the efficient classification of breathing abnormalities. CONCLUSION From this analysis, our model attains better results in simulated data but it declines the accuracy while processing with real-time data. Moreover, this work has a significant medical impact since it presents a solution to the problem of gathering enough data during the epidemic to create a realistic model with a large dataset.
Collapse
Affiliation(s)
- C Rajeshkumar
- Department of Information Technology, Sri Krishna College of Technology, Coimbatore, India
| | - K Ruba Soundar
- Department of Computer Science and Engineering, Mepco Schlenk Engineering College, Sivakasi, India
| |
Collapse
|
2
|
Jiang S, Li B, Yang Z, Li Y, Zhou Z. A back propagation neural network based respiratory motion modelling method. Int J Med Robot 2024; 20:e2647. [PMID: 38804195 DOI: 10.1002/rcs.2647] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2024] [Revised: 05/14/2024] [Accepted: 05/17/2024] [Indexed: 05/29/2024]
Abstract
BACKGROUND This study presents the development of a backpropagation neural network-based respiratory motion modelling method (BP-RMM) for precisely tracking arbitrary points within lung tissue throughout free respiration, encompassing deep inspiration and expiration phases. METHODS Internal and external respiratory data from four-dimensional computed tomography (4DCT) are processed using various artificial intelligence algorithms. Data augmentation through polynomial interpolation is employed to enhance dataset robustness. A BP neural network is then constructed to comprehensively track lung tissue movement. RESULTS The BP-RMM demonstrates promising accuracy. In cases from the public 4DCT dataset, the average target registration error (TRE) between authentic deep respiration phases and those forecasted by BP-RMM for 75 marked points is 1.819 mm. Notably, TRE for normal respiration phases is significantly lower, with a minimum error of 0.511 mm. CONCLUSIONS The proposed method is validated for its high accuracy and robustness, establishing it as a promising tool for surgical navigation within the lung.
Collapse
Affiliation(s)
- Shan Jiang
- School of Mechanical Engineering, Tianjin University, Tianjin, China
| | - Bowen Li
- School of Mechanical Engineering, Tianjin University, Tianjin, China
| | - Zhiyong Yang
- School of Mechanical Engineering, Tianjin University, Tianjin, China
| | - Yuhua Li
- School of Mechanical Engineering, Tianjin University, Tianjin, China
| | - Zeyang Zhou
- School of Mechanical Engineering, Tianjin University, Tianjin, China
| |
Collapse
|
3
|
Yan L, Long Z, Qian J, Lin J, Xie SQ, Sheng B. Rehabilitation Assessment System for Stroke Patients Based on Fusion-Type Optoelectronic Plethysmography Device and Multi-Modality Fusion Model: Design and Validation. SENSORS (BASEL, SWITZERLAND) 2024; 24:2925. [PMID: 38733031 PMCID: PMC11086329 DOI: 10.3390/s24092925] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/20/2024] [Revised: 04/28/2024] [Accepted: 04/29/2024] [Indexed: 05/13/2024]
Abstract
This study aimed to propose a portable and intelligent rehabilitation evaluation system for digital stroke-patient rehabilitation assessment. Specifically, the study designed and developed a fusion device capable of emitting red, green, and infrared lights simultaneously for photoplethysmography (PPG) acquisition. Leveraging the different penetration depths and tissue reflection characteristics of these light wavelengths, the device can provide richer and more comprehensive physiological information. Furthermore, a Multi-Channel Convolutional Neural Network-Long Short-Term Memory-Attention (MCNN-LSTM-Attention) evaluation model was developed. This model, constructed based on multiple convolutional channels, facilitates the feature extraction and fusion of collected multi-modality data. Additionally, it incorporated an attention mechanism module capable of dynamically adjusting the importance weights of input information, thereby enhancing the accuracy of rehabilitation assessment. To validate the effectiveness of the proposed system, sixteen volunteers were recruited for clinical data collection and validation, comprising eight stroke patients and eight healthy subjects. Experimental results demonstrated the system's promising performance metrics (accuracy: 0.9125, precision: 0.8980, recall: 0.8970, F1 score: 0.8949, and loss function: 0.1261). This rehabilitation evaluation system holds the potential for stroke diagnosis and identification, laying a solid foundation for wearable-based stroke risk assessment and stroke rehabilitation assistance.
Collapse
Affiliation(s)
- Liangwen Yan
- School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China; (L.Y.)
| | - Ze Long
- School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China; (L.Y.)
| | - Jie Qian
- Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310009, China
| | - Jianhua Lin
- Department of Rehabilitation Therapy, Yangzhi Affiliated Rehabilitation Hospital of Tongji University, Shanghai 201619, China
| | - Sheng Quan Xie
- School of Electronic and Electrical Engineering, University of Leeds, Leeds LS2 9JT, UK;
| | - Bo Sheng
- School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China; (L.Y.)
| |
Collapse
|
4
|
Zhang X, Zhang Y, Si Y, Gao N, Zhang H, Yang H. A high altitude respiration and SpO2 dataset for assessing the human response to hypoxia. Sci Data 2024; 11:248. [PMID: 38413602 PMCID: PMC10899206 DOI: 10.1038/s41597-024-03065-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Accepted: 02/13/2024] [Indexed: 02/29/2024] Open
Abstract
This report presents the Harespod dataset, an open dataset for high altitude hypoxia research, which includes respiration and SpO2 data. The dataset was collected from 15 college students aged 23-31 in a hypobaric oxygen chamber, during simulated altitude changes and induced hypoxia. Real-time physiological data, such as oxygen saturation waveforms, oxygen saturation, respiratory waveforms, heart rate, and pulse rate, were obtained at 100 Hz. Approximately 12 hours of valid data were collected from all participants. Researchers can easily identify the altitude corresponding to physiological signals based on their inherent patterns. Time markers were also recorded during altitude changes to facilitate realistic annotation of physiological signals and analysis of time-difference-of-arrival between various physiological signals for the same altitude change event. In high altitude scenarios, this dataset can be used to enhance the detection of human hypoxia states, predict respiratory waveforms, and develop related hardware devices. It will serve as a valuable and standardized resource for researchers in the field of high altitude hypoxia research, enabling comprehensive analysis and comparison.
Collapse
Affiliation(s)
- Xi Zhang
- School of Life Sciences, Northwestern Polytechnical University, Xi'an, 710072, China
- Engineering Research Center of Chinese Ministry of Education for Biological Diagnosis, Treatment and Protection Technology and Equipment, Northwestern Polytechnical University, Xi'an, 710072, China
| | - Yu Zhang
- School of Computer Science, Northwestern Polytechnical University, Xi'an, 710129, China.
| | - Yingjun Si
- School of Life Sciences, Northwestern Polytechnical University, Xi'an, 710072, China
- Engineering Research Center of Chinese Ministry of Education for Biological Diagnosis, Treatment and Protection Technology and Equipment, Northwestern Polytechnical University, Xi'an, 710072, China
| | - Nan Gao
- Department of Computer Science and Technology, Tsinghua University, Beijing, 100084, China
| | - Honghao Zhang
- School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an, 710072, China
| | - Hui Yang
- School of Life Sciences, Northwestern Polytechnical University, Xi'an, 710072, China.
- Engineering Research Center of Chinese Ministry of Education for Biological Diagnosis, Treatment and Protection Technology and Equipment, Northwestern Polytechnical University, Xi'an, 710072, China.
| |
Collapse
|
5
|
Roberts JD, Walton RD, Loyer V, Bernus O, Kulkarni K. Open-source software for respiratory rate estimation using single-lead electrocardiograms. Sci Rep 2024; 14:167. [PMID: 38168512 PMCID: PMC10762020 DOI: 10.1038/s41598-023-50470-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Accepted: 12/20/2023] [Indexed: 01/05/2024] Open
Abstract
Respiratory rate (RR) is a critical vital sign used to assess pulmonary function. Currently, RR estimating instrumentation is specialized and bulky, therefore unsuitable for remote health monitoring. Previously, RR was estimated using proprietary software that extract surface electrocardiogram (ECG) waveform features obtained at several thoracic locations. However, developing a non-proprietary method that uses minimal ECG leads, generally available from mobile cardiac monitors is highly desirable. Here, we introduce an open-source and well-documented Python-based algorithm that estimates RR requiring only single-stream ECG signals. The algorithm was first developed using ECGs from awake, spontaneously breathing adult human subjects. The algorithm-estimated RRs exhibited close linear correlation to the subjects' true RR values demonstrating an R2 of 0.9092 and root mean square error of 2.2 bpm. The algorithm robustness was then tested using ECGs generated by the ischemic hearts of anesthetized, mechanically ventilated sheep. Although the ECG waveforms during ischemia exhibited severe morphologic changes, the algorithm-determined RRs exhibited high fidelity with a resolution of 1 bpm, an absolute error of 0.07 ± 0.07 bpm, and a relative error of 0.67 ± 0.64%. This optimized Python-based RR estimation technique will likely be widely adapted for remote lung function assessment in patients with cardiopulmonary disease.
Collapse
Affiliation(s)
- Jesse D Roberts
- Departments of Anesthesia, Pediatrics, and Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Richard D Walton
- IHU-LIRYC, Heart Rhythm Disease Institute, Fondation Bordeaux Université, 33600, Pessac, Bordeaux, France
- INSERM, Centre de Recherche Cardio-Thoracique de Bordeaux, U1045, University of Bordeaux, 33000, Bordeaux, France
| | - Virginie Loyer
- IHU-LIRYC, Heart Rhythm Disease Institute, Fondation Bordeaux Université, 33600, Pessac, Bordeaux, France
- INSERM, Centre de Recherche Cardio-Thoracique de Bordeaux, U1045, University of Bordeaux, 33000, Bordeaux, France
| | - Olivier Bernus
- IHU-LIRYC, Heart Rhythm Disease Institute, Fondation Bordeaux Université, 33600, Pessac, Bordeaux, France
- INSERM, Centre de Recherche Cardio-Thoracique de Bordeaux, U1045, University of Bordeaux, 33000, Bordeaux, France
| | - Kanchan Kulkarni
- IHU-LIRYC, Heart Rhythm Disease Institute, Fondation Bordeaux Université, 33600, Pessac, Bordeaux, France.
- INSERM, Centre de Recherche Cardio-Thoracique de Bordeaux, U1045, University of Bordeaux, 33000, Bordeaux, France.
| |
Collapse
|
6
|
Urrea C, Kern J, Navarrete R. Bioinspired Photoreceptors with Neural Network for Recognition and Classification of Sign Language Gesture. SENSORS (BASEL, SWITZERLAND) 2023; 23:9646. [PMID: 38139492 PMCID: PMC10747091 DOI: 10.3390/s23249646] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Revised: 12/03/2023] [Accepted: 12/04/2023] [Indexed: 12/24/2023]
Abstract
This work addresses the design and implementation of a novel PhotoBiological Filter Classifier (PhBFC) to improve the accuracy of a static sign language translation system. The captured images are preprocessed by a contrast enhancement algorithm inspired by the capacity of retinal photoreceptor cells from mammals, which are responsible for capturing light and transforming it into electric signals that the brain can interpret as images. This sign translation system not only supports the effective communication between an agent and an operator but also between a community with hearing disabilities and other people. Additionally, this technology could be integrated into diverse devices and applications, further broadening its scope, and extending its benefits for the community in general. The bioinspired photoreceptor model is evaluated under different conditions. To validate the advantages of applying photoreceptors cells, 100 tests were conducted per letter to be recognized, on three different models (V1, V2, and V3), obtaining an average of 91.1% of accuracy on V3, compared to 63.4% obtained on V1, and an average of 55.5 Frames Per Second (FPS) in each letter classification iteration for V1, V2, and V3, demonstrating that the use of photoreceptor cells does not affect the processing time while also improving the accuracy. The great application potential of this system is underscored, as it can be employed, for example, in Deep Learning (DL) for pattern recognition or agent decision-making trained by reinforcement learning, etc.
Collapse
Affiliation(s)
- Claudio Urrea
- Electrical Engineering Department, Faculty of Engineering, University of Santiago of Chile, Las Sophoras 165, Estación Central, Santiago 9170020, Chile; (J.K.); (R.N.)
| | | | | |
Collapse
|
7
|
Hwang CS, Kim YH, Hyun JK, Kim JH, Lee SR, Kim CM, Nam JW, Kim EY. Evaluation of the Photoplethysmogram-Based Deep Learning Model for Continuous Respiratory Rate Estimation in Surgical Intensive Care Unit. Bioengineering (Basel) 2023; 10:1222. [PMID: 37892952 PMCID: PMC10604201 DOI: 10.3390/bioengineering10101222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 10/16/2023] [Accepted: 10/17/2023] [Indexed: 10/29/2023] Open
Abstract
The respiratory rate (RR) is a significant indicator to evaluate a patient's prognosis and status; however, it requires specific instrumentation or estimates from other monitored signals. A photoplethysmogram (PPG) is extensively used in clinical environments as well as in intensive care units (ICUs) to primarily monitor peripheral circulation while capturing indirect information about intrathoracic pressure changes. This study aims to apply and evaluate several deep learning models using a PPG for the continuous and accurate estimation of the RRs of patients. The dataset was collected twice for 2 min each in 100 patients aged 18 years and older from the surgical intensive care unit of a tertiary referral hospital. The BIDMC and CapnoBase public datasets were also analyzed. The collected dataset was preprocessed and split according to the 5-fold cross-validation. We used seven deep learning models, including our own Dilated Residual Neural Network, to check how accurately the RR estimates match the ground truth using the mean absolute error (MAE). As a result, when validated using the collected dataset, our model showed the best results with a 1.2628 ± 0.2697 MAE on BIDMC and RespNet and with a 3.1268 ± 0.6363 MAE on our dataset, respectively. In conclusion, RR estimation using PPG-derived models is still challenging and has many limitations. However, if there is an equal amount of data from various breathing groups to train, we expect that various models, including our Dilated ResNet model, which showed good results, can achieve better results than the current ones.
Collapse
Affiliation(s)
- Chi Shin Hwang
- Spass Inc., 905Ho, RnD Tower, 396, Worldcup Buk-ro, Mapo-gu, Seoul 03925, Republic of Korea; (C.S.H.); (J.W.N.)
| | - Yong Hwan Kim
- Spass Inc., 905Ho, RnD Tower, 396, Worldcup Buk-ro, Mapo-gu, Seoul 03925, Republic of Korea; (C.S.H.); (J.W.N.)
| | - Jung Kyun Hyun
- Spass Inc., 905Ho, RnD Tower, 396, Worldcup Buk-ro, Mapo-gu, Seoul 03925, Republic of Korea; (C.S.H.); (J.W.N.)
| | - Joon Hwang Kim
- Spass Inc., 905Ho, RnD Tower, 396, Worldcup Buk-ro, Mapo-gu, Seoul 03925, Republic of Korea; (C.S.H.); (J.W.N.)
| | - Seo Rak Lee
- Spass Inc., 905Ho, RnD Tower, 396, Worldcup Buk-ro, Mapo-gu, Seoul 03925, Republic of Korea; (C.S.H.); (J.W.N.)
| | - Choong Min Kim
- Spass Inc., 905Ho, RnD Tower, 396, Worldcup Buk-ro, Mapo-gu, Seoul 03925, Republic of Korea; (C.S.H.); (J.W.N.)
| | - Jung Woo Nam
- Spass Inc., 905Ho, RnD Tower, 396, Worldcup Buk-ro, Mapo-gu, Seoul 03925, Republic of Korea; (C.S.H.); (J.W.N.)
| | - Eun Young Kim
- Division of Trauma and Surgical Critical Care, Department of Surgery, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Banpo-daero 222, Seocho-gu, Seoul 06591, Republic of Korea
| |
Collapse
|
8
|
Zhao Q, Liu F, Song Y, Fan X, Wang Y, Yao Y, Mao Q, Zhao Z. Predicting Respiratory Rate from Electrocardiogram and Photoplethysmogram Using a Transformer-Based Model. Bioengineering (Basel) 2023; 10:1024. [PMID: 37760126 PMCID: PMC10525435 DOI: 10.3390/bioengineering10091024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Revised: 08/27/2023] [Accepted: 08/29/2023] [Indexed: 09/29/2023] Open
Abstract
The respiratory rate (RR) serves as a critical physiological parameter in the context of both diagnostic and prognostic evaluations. Due to the challenges of direct measurement, RR is still predominantly measured through the traditional manual counting-breaths method in clinic practice. Numerous algorithms and machine learning models have been developed to predict RR using physiological signals, such as electrocardiogram (ECG) or/and photoplethysmogram (PPG) signals. Yet, the accuracy of these existing methods on available datasets remains limited, and their prediction on new data is also unsatisfactory for actual clinical applications. In this paper, we proposed an enhanced Transformer model with inception blocks for predicting RR based on both ECG and PPG signals. To evaluate the generalization capability on new data, our model was trained and tested using subject-level ten-fold cross-validation using data from both BIDMC and CapnoBase datasets. On the test set, our model achieved superior performance over five popular deep-learning-based methods with mean absolute error (1.2) decreased by 36.5% and correlation coefficient (0.85) increased by 84.8% compared to the best results of these models. In addition, we also proposed a new pipeline to preprocess ECG and PPG signals to improve model performance. We believe that the development of the TransRR model is expected to further expedite the clinical implementation of automatic RR estimation.
Collapse
Affiliation(s)
- Qi Zhao
- School of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110819, China; (Q.Z.); (Y.W.)
| | - Fang Liu
- School of Information Technology, Dalian Maritime University, Dalian 116026, China; (F.L.); (Y.S.)
| | - Yide Song
- School of Information Technology, Dalian Maritime University, Dalian 116026, China; (F.L.); (Y.S.)
| | - Xiaoya Fan
- School of Software, Key Laboratory for Ubiquitous Network and Service Software, Dalian University of Technology, Dalian 116024, China;
| | - Yu Wang
- School of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110819, China; (Q.Z.); (Y.W.)
| | - Yudong Yao
- Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ 07030, USA;
| | - Qian Mao
- School of Light Industry, Liaoning University, Shenyang 110136, China
| | - Zheng Zhao
- School of Artificial Intelligence, Dalian Maritime University, Dalian 116026, China
| |
Collapse
|
9
|
Hazratifard M, Agrawal V, Gebali F, Elmiligi H, Mamun M. Ensemble Siamese Network (ESN) Using ECG Signals for Human Authentication in Smart Healthcare System. SENSORS (BASEL, SWITZERLAND) 2023; 23:4727. [PMID: 37430641 DOI: 10.3390/s23104727] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 05/10/2023] [Accepted: 05/11/2023] [Indexed: 07/12/2023]
Abstract
Advancements in digital communications that permit remote patient visits and condition monitoring can be attributed to a revolution in digital healthcare systems. Continuous authentication based on contextual information offers a number of advantages over traditional authentication, including the ability to estimate the likelihood that the users are who they claim to be on an ongoing basis over the course of an entire session, making it a much more effective security measure for proactively regulating authorized access to sensitive data. Current authentication models that rely on machine learning have their shortcomings, such as the difficulty in enrolling new users to the system or model training sensitivity to imbalanced datasets. To address these issues, we propose using ECG signals, which are easily accessible in digital healthcare systems, for authentication through an Ensemble Siamese Network (ESN) that can handle small changes in ECG signals. Adding preprocessing for feature extraction to this model can result in superior results. We trained this model on ECG-ID and PTB benchmark datasets, achieving 93.6% and 96.8% accuracy and 1.76% and 1.69% equal error rates, respectively. The combination of data availability, simplicity, and robustness makes it an ideal choice for smart healthcare and telehealth.
Collapse
Affiliation(s)
- Mehdi Hazratifard
- Department of Electrical and Computer Engineering, University of Victoria, Victoria, BC V8W 2Y2, Canada
| | - Vibhav Agrawal
- Department of Electrical and Computer Engineering, University of Victoria, Victoria, BC V8W 2Y2, Canada
| | - Fayez Gebali
- Department of Electrical and Computer Engineering, University of Victoria, Victoria, BC V8W 2Y2, Canada
| | - Haytham Elmiligi
- Department of Electrical and Computer Engineering, University of Victoria, Victoria, BC V8W 2Y2, Canada
| | - Mohammad Mamun
- National Research Council of Canada, Government of Canada, Ottawa, ON K1A 0R6, Canada
| |
Collapse
|
10
|
Kumar AK, Jain S, Jain S, Ritam M, Xia Y, Chandra R. Physics-informed neural entangled-ladder network for inhalation impedance of the respiratory system. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 231:107421. [PMID: 36805280 DOI: 10.1016/j.cmpb.2023.107421] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 02/11/2023] [Accepted: 02/13/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND AND OBJECTIVES The use of machine learning methods for modelling bio-systems is becoming prominent which can further improve bio-medical technologies. Physics-informed neural networks (PINNs) can embed the knowledge of physical laws that govern a system during the model training process. PINNs utilise differential equations in the model which traditionally used numerical methods that are computationally complex. METHODS We integrate PINNs with an entangled ladder network for modelling respiratory systems by considering a lungs conduction zone to evaluate the respiratory impedance for different initial conditions. We evaluate the respiratory impedance for the inhalation phase of breathing for a symmetric model of the human lungs using entanglement and continued fractions. RESULTS We obtain the impedance of the conduction zone of the lungs pulmonary airways using PINNs for nine different combinations of velocity and pressure of inhalation. We compare the results from PINNs with the finite element method using the mean absolute error and root mean square error. The results show that the impedance obtained with PINNs contrasts with the conventional forced oscillation test used for deducing the respiratory impedance. The results show similarity with the impedance plots for different respiratory diseases. CONCLUSION We find a decrease in impedance when the velocity of breathing is lowered gradually by 20%. Hence, the methodology can be used to design smart ventilators to the improve flow of breathing.
Collapse
Affiliation(s)
- Amit Krishan Kumar
- Faculty of Electrical-Electronic Engineering, Duy Tan University, Da Nang, 550000, Vietnam; State Key Laboratory of Intelligent Control and Decision of Complex Systems, School of Automation, Beijing Institute of Technology, Beijing, 100081, China.
| | - Snigdha Jain
- Department of Electronics and Communications Engineering, Indian Institute of Technology Guwahati, Assam, India.
| | - Shirin Jain
- Department of Electronics and Communications Engineering, Indian Institute of Technology Guwahati, Assam, India.
| | - M Ritam
- Department of Chemical Engineering, Indian Institute of Technology Guwahati, Assam, India.
| | - Yuanqing Xia
- State Key Laboratory of Intelligent Control and Decision of Complex Systems, School of Automation, Beijing Institute of Technology, Beijing, 100081, China.
| | - Rohitash Chandra
- Transitional Artificial Intelligence Research Group, School of Mathematics and Statistics, UNSW Sydney, NSW 2052, Australia.
| |
Collapse
|
11
|
Rathore KS, Vijayarangan S, Sp P, Sivaprakasam M. A Multifunctional Network with Uncertainty Estimation and Attention-Based Knowledge Distillation to Address Practical Challenges in Respiration Rate Estimation. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23031599. [PMID: 36772640 PMCID: PMC9920118 DOI: 10.3390/s23031599] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 01/20/2023] [Accepted: 01/23/2023] [Indexed: 05/03/2023]
Abstract
Respiration rate is a vital parameter to indicate good health, wellbeing, and performance. As the estimation through classical measurement modes are limited only to rest or during slow movements, respiration rate is commonly estimated through physiological signals such as electrocardiogram and photoplethysmography due to the unobtrusive nature of wearable devices. Deep learning methodologies have gained much traction in the recent past to enhance accuracy during activities involving a lot of movement. However, these methods pose challenges, including model interpretability, uncertainty estimation in the context of respiration rate estimation, and model compactness in terms of deployment in wearable platforms. In this direction, we propose a multifunctional framework, which includes the combination of an attention mechanism, an uncertainty estimation functionality, and a knowledge distillation framework. We evaluated the performance of our framework on two datasets containing ambulatory movement. The attention mechanism visually and quantitatively improved instantaneous respiration rate estimation. Using Monte Carlo dropouts to embed the network with inferential uncertainty estimation resulted in the rejection of 3.7% of windows with high uncertainty, which consequently resulted in an overall reduction of 7.99% in the mean absolute error. The attention-aware knowledge distillation mechanism reduced the model's parameter count and inference time by 49.5% and 38.09%, respectively, without any increase in error rates. Through experimentation, ablation, and visualization, we demonstrated the efficacy of the proposed framework in addressing practical challenges, thus taking a step towards deployment in wearable edge devices.
Collapse
Affiliation(s)
- Kapil Singh Rathore
- Indian Institute of Technology Madras, Chennai 6000001, India
- Healthcare Technology Innovation Center, Chennai 6000001, India
| | - Sricharan Vijayarangan
- Indian Institute of Technology Madras, Chennai 6000001, India
- Healthcare Technology Innovation Center, Chennai 6000001, India
| | - Preejith Sp
- Healthcare Technology Innovation Center, Chennai 6000001, India
| | - Mohanasankar Sivaprakasam
- Indian Institute of Technology Madras, Chennai 6000001, India
- Healthcare Technology Innovation Center, Chennai 6000001, India
| |
Collapse
|
12
|
Lee S, Moon H, Al-antari MA, Lee G. Dual-Sensor Signals Based Exact Gaussian Process-Assisted Hybrid Feature Extraction and Weighted Feature Fusion for Respiratory Rate and Uncertainty Estimations. SENSORS (BASEL, SWITZERLAND) 2022; 22:8386. [PMID: 36366083 PMCID: PMC9654728 DOI: 10.3390/s22218386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Revised: 10/22/2022] [Accepted: 10/23/2022] [Indexed: 06/16/2023]
Abstract
Accurately estimating respiratory rate (RR) has become essential for patients and the elderly. Hence, we propose a novel method that uses exact Gaussian process regression (EGPR)-assisted hybrid feature extraction and feature fusion based on photoplethysmography and electrocardiogram signals to improve the reliability of accurate RR and uncertainty estimations. First, we obtain the power spectral features and use the multi-phase feature model to compensate for insufficient input data. Then, we combine four different feature sets and choose features with high weights using a robust neighbor component analysis. The proposed EGPR algorithm provides a confidence interval representing the uncertainty. Therefore, the proposed EGPR algorithm, including hybrid feature extraction and weighted feature fusion, is an excellent model with improved reliability for accurate RR estimation. Furthermore, the proposed EGPR methodology is likely the only one currently available that provides highly stable variation and confidence intervals. The proposed EGPR-MF, 0.993 breath per minute (bpm), and EGPR-feature fusion, 1.064 (bpm), show the lowest mean absolute error compared to the other models.
Collapse
Affiliation(s)
- Soojeong Lee
- Department of Computer Engineering, Sejong University, 209 Neungdong-ro, Gwangjin-gu, Seoul 05006, Korea
| | - Hyeonjoon Moon
- Department of Computer Engineering, Sejong University, 209 Neungdong-ro, Gwangjin-gu, Seoul 05006, Korea
| | - Mugahed A. Al-antari
- Department of Artificial intelligence, Sejong University, 209 Neungdong-ro, Gwangjin-gu, Seoul 05006, Korea
| | - Gangseong Lee
- Ingenium College, Kwangwoon University, 20 Kwangwoon-ro, Nowon-gu, Seoul 01897, Korea
| |
Collapse
|
13
|
Wang Z, Yan Z, Xing Y, Wang H. Real‐time trajectory prediction of laparoscopic instrument tip based on long short‐term memory neural network in laparoscopic surgery training. Int J Med Robot 2022; 18:e2441. [DOI: 10.1002/rcs.2441] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 06/03/2022] [Accepted: 07/11/2022] [Indexed: 11/10/2022]
Affiliation(s)
- Ziheng Wang
- School of Mechanical Engineering Tianjin University Tianjin China
| | - Zhengxiang Yan
- College of Intelligence and Computing Tianjin University Tianjin China
| | - Yuan Xing
- Key Lab for Mechanism Theory and Equipment Design of Ministry of Education School of Mechanical Engineering Tianjin University Tianjin China
| | - Honglei Wang
- Department of Gastrointestinal Surgery Tianjin Hospital of ITCWM Tianjin China
| |
Collapse
|
14
|
Sviridova N, Zhao T, Nakano A, Ikeguchi T. Photoplethysmogram Recording Length: Defining Minimal Length Requirement from Dynamical Characteristics. SENSORS 2022; 22:s22145154. [PMID: 35890834 PMCID: PMC9324273 DOI: 10.3390/s22145154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Revised: 07/03/2022] [Accepted: 07/07/2022] [Indexed: 01/27/2023]
Abstract
Photoplethysmography is a widely used technique to noninvasively assess heart rate, blood pressure, and oxygen saturation. This technique has considerable potential for further applications—for example, in the field of physiological and mental health monitoring. However, advanced applications of photoplethysmography have been hampered by the lack of accurate and reliable methods to analyze the characteristics of the complex nonlinear dynamics of photoplethysmograms. Methods of nonlinear time series analysis may be used to estimate the dynamical characteristics of the photoplethysmogram, but they are highly influenced by the length of the time series, which is often limited in practical photoplethysmography applications. The aim of this study was to evaluate the error in the estimation of the dynamical characteristics of the photoplethysmogram associated with the limited length of the time series. The dynamical properties were evaluated using recurrence quantification analysis, and the estimation error was computed as a function of the length of the time series. Results demonstrated that properties such as determinism and entropy can be estimated with an error lower than 1% even for short photoplethysmogram recordings. Additionally, the lower limit for the time series length to estimate the average prediction time was computed.
Collapse
Affiliation(s)
- Nina Sviridova
- Department of Information and Computer Technology, Faculty of Engineering, Tokyo University of Science, 6-3-1 Niijuku, Katsushika, Tokyo 125-8585, Japan;
- International Research Center for Neurointelligence, The University of Tokyo, 7-3-1 Hongo Bunkyo-ku, Tokyo 113-0033, Japan
- Correspondence:
| | - Tiejun Zhao
- Faculty of Agro-Food Science, Niigata Agro-Food University, 2416 Hiranedai, Tainai 959-2702, Japan;
| | - Akimasa Nakano
- Innovation Management Organization, Chiba University, Kashiwano-ha Campus 6-2-1, Kashiwano-ha, Kashiwa-shi 277-0882, Japan;
| | - Tohru Ikeguchi
- Department of Information and Computer Technology, Faculty of Engineering, Tokyo University of Science, 6-3-1 Niijuku, Katsushika, Tokyo 125-8585, Japan;
| |
Collapse
|